https://orcid.org/0009-0005-6656-5914
Figures
Abstract
Early and real-time detection of rice leaf diseases (RLD) poses a significant challenge for farmers, especially in rural regions with limited access to advanced technology. Conventional deep learning models often require substantial computational resources, rendering them impractical for deployment on mobile or edge devices commonly used in agricultural environments. Although models such as ResNet50, VGG16, and InceptionV3 can achieve high accuracy, they are computationally expensive and may be less suitable for real-time deployment in smart agricultural systems. Furthermore, many existing studies rely on limited datasets, which can restrict model generalizability under diverse real-world conditions. Although transfer learning can improve classification performance, developing lightweight models suitable for real-time IoT deployment remains challenging. To address these limitations, we curated a hybrid dataset of 7,092 rice leaf images by combining self-collected and Kaggle samples and proposed IoT-RiceMobileNet, a lightweight improved MobileNetV2-based model that can be effectively integrated into our developed IoT system. The proposed model outperformed transfer learning and deep learning baseline models, achieving 99.19% test accuracy and 99.20% precision while maintaining low computational complexity. We further confirmed model stability using stratified 5-fold and 10-fold cross-validation on both the constructed RLD and multi-source datasets, achieving mean accuracies of 98.04% and 98.13% on the constructed RLD dataset and 98.11% and 98.58% on the multi-source dataset, respectively. Moreover, our models compact size and 85.17 FPS inference speed support real-time deployment. Finally, the model was integrated into an IoT-enabled mobile, web, and cloud-based inference framework, demonstrating its practical potential for scalable rice disease detection in smart agriculture.
Citation: Masud KI, Zihad MY, Shuvo MH, Jannat MR, Uddin J, Ali S (2026) IoT-RiceMobileNet: An improved lightweight MobileNetV2 Model for real-time multi-class rice disease detection using IoT. PLoS One 21(9): e0356383. https://doi.org/10.1371/journal.pone.0356383
Editor: Hashmat Fida, Lincoln University College, MALAYSIA
Received: March 15, 2026; Accepted: August 3, 2026; Published: September 15, 2026
Copyright: © 2026 Masud et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Data Availability: The self-collected rice leaf image dataset used in this study is publicly available through the archived IoT-RiceMobileNet repository on Zenodo: https://doi.org/10.5281/zenodo.21140529. The public third-party datasets used in this study are available from their original sources: https://www.kaggle.com/datasets/dedeikhsandwisaputra/rice-leafs-disease-dataset/data https://www.kaggle.com/datasets/anshulm257/rice-disease-dataset https://data.mendeley.com/datasets/hx6f852hw4/2 Third-party images are not redistributed in the Zenodo archive and should be obtained from their original sources according to their respective licenses. The source code is publicly available through the GitHub repository: https://github.com/eimran/IoT-RiceMobileNet and is also archived on Zenodo at: https://doi.org/10.5281/zenodo.21140529.
Funding: The author(s) received no specific funding for this work.
Competing interests: NO authors have competing interests.
Introduction
Rice is a major staple crop that supports food security for a large proportion of the global population, particularly in Asia and Africa [1]. It is deeply embedded in the cultures, diets, and economies of many developing countries, including Bangladesh, and provides an important source of carbohydrates, vitamins, and minerals [2,3]. Rice plants are affected by several diseases, including bacterial leaf blight, brown spot, leaf blast, leaf scald, and narrow brown spot, which can reduce growth and yield and spread across plantations, causing substantial economic losses for farmers [4–6]. Recent advances in machine learning and deep learning have created new opportunities for automated, scalable, and accurate rice leaf disease detection under diverse crop-growing conditions [7–11]. Computer vision-based approaches, particularly convolutional neural networks (CNNs), have shown strong capability in extracting discriminative visual features from leaf images for fast and accurate disease classification [12–15]. Transfer learning (TL) has further accelerated progress in this area by adapting large pre-trained models to domain-specific disease classification tasks, reducing training time and computational cost [16,17]. In recent years, Internet of Things (IoT) [18] based smart agricultural systems have evolved from simple offline image classifiers to connected, real-time decision-support platforms that integrate cameras, sensors, edge devices, and cloud servers to deliver timely disease predictions to farmers [19–21]. The convergence of deep learning and IoT has been widely recognized as a promising approach for image-based crop disease monitoring, remote diagnosis, and automated classification [22,23]. However, practical deployment under real field conditions requires models that are not only accurate but also lightweight and computationally efficient enough for resource-constrained agricultural environments [24].
Scholarly inquiries into rice leaf disease detection using ML- and DL-based models have progressively evolved. On the machine learning side, Sethy et al. [25] combined ResNet50-based deep features with SVM and achieved promising performance; however, their study was limited to four disease classes and did not include real-world deployment. Chaudhary et al. [26] used GLCM and ILMFD-based feature representations with SVM and ANN, reporting competitive results, but the approach remained dependent on hand-crafted features and a limited disease scope. Narmadha et al. [27] reported 97.68% accuracy using only 120 images from three disease classes, which raises concerns regarding generalizability. Li et al. [28] proposed a RegNet-based model for detecting 11 rice disease classes and one healthy class, achieving 96.8% accuracy. Li et al. [29] trained an RDRM-YOLO model on approximately 5,930 images from four disease classes and reported 94.3% accuracy, while Al et al. [30] reported 98.71% accuracy using 2,368 images from four disease classes. Although these studies achieved promising classification performance, many were limited by fewer disease classes, smaller datasets, limited deployment analysis, or insufficient evaluation under diverse real-world conditions. Haruna et al. [31] investigated a StyleGAN2-ADA-enhanced rice leaf disease detection framework using Faster R-CNN and SSD, achieving up to 93% mAP under small-data conditions. Simhadri et al. [32] evaluated 15 pre-trained CNN architectures and reported 99.64% accuracy with InceptionV3; however, the study was conducted on controlled datasets and relied on computationally expensive architectures, limiting suitability for edge deployment. Poorni et al. [33] applied transfer learning using a pre-trained InceptionV3 model and achieved 94.48% accuracy, but the study had limited real-world validation. For IoT-oriented deployment, Sriram et al. [34] integrated InceptionV3 with an IoT-based irrigation control system on Raspberry Pi and achieved 98.12% accuracy using real-field rice images. Verma et al. [35] developed a smartphone- and FPGA-integrated CNN framework for rice and potato disease quantification, achieving 95% classification accuracy. However, these systems were still constrained by limited dataset scale, restricted disease coverage, or incomplete evaluation of lightweight real-time deployment requirements.
Overall, existing studies have advanced rice disease classification using machine learning, deep learning, transfer learning, and IoT-based systems. However, several limitations remain. Many studies rely on relatively small or less diverse datasets, evaluate only a limited number of disease classes, or use computationally expensive architectures that are difficult to deploy on resource-constrained mobile and IoT devices. In addition, relatively limited work has jointly considered dataset diversity, lightweight model design, cross-source validation, explainability, and end-to-end IoT deployment within a unified rice leaf disease detection framework. To address these limitations, this study introduces IoT-RiceMobileNet with the following key contributions:
- A self-curated constructed RLD dataset of 7,092 rice leaf images across six classes, including five disease classes and healthy leaves, designed to improve real-world generalization.
- A lightweight IoT-RiceMobileNet model built on MobileNetV2, achieving 99.19% test accuracy and 99.20% precision on the constructed RLD dataset while maintaining low computational requirements.
- A comprehensive evaluation against eight widely used deep learning architectures on the constructed RLD dataset, supported by stratified cross-validation and additional evaluation on a heterogeneous multi-source dataset of 14,758 images, where IoT-RiceMobileNet achieved 99.25% test accuracy and demonstrated strong cross-source generalization.
- An end-to-end IoT system for real-time rice leaf disease classification, integrating the proposed model with mobile, web, cloud, and hardware components.
Materials and methods
A complete IoT-based solution was designed and developed for real-time rice leaf disease detection using the proposed lightweight MobileNetV2-based IoT-RiceMobileNet architecture.
As illustrated in Fig 1, the framework consists of two connected workflows: model development and IoT-based inference. In the model development workflow, rice leaf images are collected from the dataset, augmented, preprocessed, transformed into a suitable training format, and used to train and deploy the proposed model. In the IoT-based inference workflow, leaf images captured through the ESP32-CAM hardware module are processed by the microcontroller and transmitted to the cloud, where the same preprocessing procedure is applied before classification. The deployed model then predicts one of the six rice leaf classes and delivers the result through the mobile or web application.
(a): IoT-RiceMobileNet framework. (b): IoT system development workflow.
Dataset description
This study developed a rice leaf disease (RLD) dataset comprising six rice leaf conditions, including five disease classes (bacterial leaf blight, brown spot, leaf blast, leaf scald, and narrow brown spot) and healthy leaves. The dataset was constructed by integrating self-collected field images with publicly available Kaggle images [36] to improve environmental diversity and model generalization capability. The original dataset consisted of 3,401 images, including 773 self-collected field images and 2,628 Kaggle images. After augmentation of the self-collected training data and integration with the Kaggle dataset, the final constructed RLD dataset contained 7,092 images across six classes. The overall dataset construction, augmentation statistics, and partitioning strategy are summarized in Tables 1 and 2, while representative samples of all six classes are illustrated in Fig 2.
The classes include bacterial leaf blight, leaf blast, leaf scald, narrow brown spot, brown spot, and healthy leaves.
Self-collected image acquisition.
A total of 827 rice leaf images were collected from approximately 14 agricultural fields in Chapulia, Gazipur, Bangladesh, during the Aman 2025 growing season using an iPhone 12 Pro under natural daylight conditions. No formal collection permit was required because the images were collected from privately accessible agricultural fields with permission from the field owners. No destructive sampling, endangered species, protected plant materials, human participants, or animals were involved in this study. Images were captured at different times of day (morning, midday, and afternoon) to incorporate variations in illumination and shadow effects. Multiple viewpoints and distances were used to reflect real-world farmer-acquired field conditions. Prior to annotation, all images underwent manual quality screening to remove samples affected by severe blur, overexposure, occlusion, or insufficient disease visibility. After screening, 54 images were excluded, resulting in 773 high-quality images for annotation and analysis. Images were first separated into healthy and diseased categories, after which diseased samples were assigned to specific disease classes based on dominant visible symptoms. Disease classification followed standardized visual diagnostic criteria for bacterial leaf blight, brown spot, leaf blast, leaf scald, narrow brown spot, and healthy leaves, guided by the IRRI Rice Knowledge Bank and the Rice Doctor application. All self-collected images were annotated at the class level with the assistance of two agricultural experts in rice disease diagnosis and plant pathology. Ambiguous or overlapping cases were independently reviewed by both experts, and samples without diagnostic consensus were excluded. Final labeling decisions were verified under the supervision of the corresponding author.
Image preprocessing, augmentation, and dataset partitioning.
Image preprocessing and augmentation techniques were applied to improve robustness and reduce overfitting [37,38]. All images were resized to pixels to meet the input requirements of MobileNet-based architectures and ensure consistent dimensionality across the dataset. Pixel intensity values were normalized from [0,255] to [0,1] to improve optimization stability and accelerate model convergence [39]. To address limited dataset size and class imbalance, a comprehensive augmentation strategy was applied only to the training portion of the self-collected dataset. This included geometric transformations: random rotation, horizontal and vertical flipping, translation, zooming, and shearing and photometric transformations such as brightness and contrast adjustments [37,38,40,41]. This process increased intra-class diversity and improved model robustness to real-world field conditions while reducing overfitting to specific acquisition settings and preserving disease-specific visual characteristics. As shown in Table 1, 386 original self-collected images were used for training augmentation, producing 4,077 self-collected training samples after augmentation, while 387 original self-collected images were reserved for validation and testing.
The augmentation and preprocessing pipeline is illustrated in Fig 3. Original self-collected images were partitioned at the source-image level into training and evaluation subsets before any augmentation, ensuring that each source image and its augmented derivatives belonged exclusively to a single partition. Augmentation was applied only to the self-collected training subset. The augmented self-collected dataset was merged with the publicly available Kaggle rice leaf disease dataset [36]. From the Kaggle dataset, 1,526 images were used for training and 1,102 images were reserved for the validation/test holdout set. The final training partition contained 5,603 images, consisting of 1,526 Kaggle training images and 4,077 augmented self-collected training images. The holdout partition contained 1,489 original images, consisting of 1,102 Kaggle images and 387 self-collected images. The holdout partition was then divided into validation and test subsets, resulting in 744 validation images and 745 test images, as reported in Table 2. The validation subset was used for hyperparameter tuning and model selection, whereas the test subset was reserved exclusively for final performance evaluation. Because validation and test images were kept unaugmented, the final performance evaluation was conducted on unseen original images rather than augmented derivatives.
(a): Original and resized images. (b): Original and augmented images.
Additional multi-source benchmark dataset.
In addition to the constructed RLD dataset, a heterogeneous multi-source benchmark dataset was prepared using publicly available Kaggle [42] and Mendeley [43] rice leaf disease images.
This dataset was used for additional training, validation, and testing experiments to evaluate model robustness across different acquisition conditions, backgrounds, disease appearances, and dataset preparation pipelines. The multi-source dataset statistics are shown in Fig 4.
The figure summarizes the integration of Kaggle and Mendeley rice leaf disease images and their partitioning into training, validation, and testing subsets.
Model development
We proposed a lightweight improved MobileNetV2 architecture for real-time rice leaf disease classification and systematically evaluated its performance against several deep learning baselines. We conducted comparative experiments across eight baseline models, encompassing canonical CNN, VGG16, MobileNetV2, InceptionV3, and ResNet50, with selected architectures augmented through transfer learning paradigms to assess the impact of pre-trained weights.
Convolutional neural network (CNN).
For fair comparison with our models, we developed a baseline convolutional neural network (CNN) model and trained it using the same dataset partitions. CNNs are effective at learning complex spatial patterns from image data and have significantly advanced image classification and object detection tasks [44]. Operating through multiple layers, convolutional, pooling, and fully connected layers, CNNs systematically extract and build hierarchical feature representations from raw images. Their strength stems from local feature detection and compositional learning, which culminate in superior performance for object detection and image classification tasks [32],. The CNN architecture used in our experiment is shown in Fig 5.
Transfer learning.
Transfer learning uses features learned from one task to improve performance on a related but distinct task, accelerating convergence and improving classification accuracy through feature reuse [45].
Two important paradigms exist: feature extraction, wherein only terminal layers undergo retraining while the pre-trained model’s weights remain frozen, and fine-tuning, which refurbishes select higher-level layers of the architecture on novel data [45]. Pre-trained models can reduce training time and improve generalization by extracting meaningful feature representations from new samples, which is particularly salient for tasks plagued by scarce labeled data, and researchers frequently use this technique [46]. By learning models pre-trained on voluminous datasets such as ImageNet [47], practitioners can judiciously calibrate them for domain-specific applications [48]. Therefore, to develop our model, we employed a pre-trained MobileNetV2 architecture and then customized the layer architecture and systematically experimented with diverse transfer learning methodologies, achieving strong classification of rice leaf diseases through feature extraction and fine-tuning strategies. The MobileNetV2-based transfer learning workflow is shown in Fig 6.
The figure illustrates the MobileNetV2-based transfer learning workflow used for rice leaf disease classification.
Proposed IoT-RiceMobileNet model.
In this study, we developed IoT-RiceMobileNet, a MobileNetV2-based lightweight CNN architecture designed for IoT-based real-time multi-class rice disease detection. The proposed architecture, shown in Fig 7, uses a MobileNetV2 backbone pre-trained on the ImageNet dataset to accelerate convergence and improve performance on the domain-specific constructed RLD dataset. The top classification layers of MobileNetV2 were discarded, and the image input shape was set to (224, 224, 3). To adapt the pre-trained backbone to rice leaf disease classification, we applied a fine-tuning strategy. Initially, all layers of MobileNetV2 were set as trainable. We then employed a freezing method, where all layers except the last 35 were frozen and tuned as necessary to prevent updating the early layers, which typically learn simple, low-level features such as edges and textures. Pixel values were normalized from the standard range [0, 255] to using:
where is the pixel intensity scaled to [0,1] via standard image-to-tensor conversion in PyTorch, and
represents the normalized value used as input to the network. This normalization improves convergence speed and numerical stability in gradient-based optimization. The same normalization procedure is applied consistently during both training and inference. After the input and rescaling layers, the pre-trained MobileNetV2 backbone was incorporated. Custom convolutional layers were added beyond the pre-trained layers to improve feature extraction. The ReLU activation function was used in all hidden layers:
The architecture illustrates the MobileNetV2 backbone, customized lightweight convolutional layers, and final classification module for six rice leaf classes.
Following the final convolutional block of the backbone network, a depthwise convolution layer (, kernel size
) was employed to further refine spatial feature representations. This was followed by a pointwise convolution layer (
, kernel size
) to reduce channel dimensionality and computational complexity. Batch normalization (BN2d) was subsequently applied to stabilize feature distributions and facilitate training convergence. A Global Average Pooling (GAP) layer compresses the spatial feature maps into a 128-dimensional feature vector, significantly reducing the number of trainable parameters while improving generalization capability. A second batch normalization layer (BN1d) was then applied to normalize the resulting feature embeddings before classification. The pooled feature vector was passed through a fully connected layer with 128 neurons and ReLU activation to learn non-linear discriminative representations. To mitigate overfitting, a dropout layer with a dropout probability of p = 0.4 was employed. Finally, a linear classification layer maps the 128-dimensional feature representation to K output class logits, which are converted into class probabilities using the Softmax function:
where denotes the logit corresponding to the k-th class, and K = 6 represents the total number of rice disease categories. In practice, during training, raw logits are optimized using nn.CrossEntropyLoss, while softmax was applied at inference time when probability estimates are required for interpretation.
Overall, IoT-RiceMobileNet combined a pretrained MobileNetV2 backbone with depthwise-separable convolutions, dual batch normalization, global average pooling, and selective fine-tuning of the last 35 layers, forming a compact and efficient architecture for multi-class rice disease classification.
IoT system development
The IoT-based rice disease classification system comprises several integrated stages. Requirements analysis identified system needs and constraints. The software component controls image capture, disease detection, and remote camera access through microcontroller programming. Hardware components include ESP32-CAM, buck converter, button, and rechargeable battery assembled into a compact prototype mounted on a PVC base (Fig 8(c)). The microcontroller transmits captured image data to the cloud for disease prediction and classification. A mobile app was developed for Android using React Native to facilitate accessibility and usability. Users capture leaf images via camera or upload from the gallery, and the app communicates with a backend API on AWS EC2. The API processes images using the trained deep learning model and responds with predictions in real-time. The lightweight pipeline enables low-latency user interaction, facilitating field deployment for farmers and agricultural extension workers. Fig 8(a) shows the web view architecture, and Fig 8(b) shows the mobile app.
(a): Web-based application flow. (b): Mobile application flow. (c): Hardware system design.
System deployment and inference pipeline.
The proposed rice disease detection system was deployed as a cloud-based inference service integrated with IoT devices, a mobile application, and a web interface. These client platforms serve as image input sources and communicate with a centralized backend hosted on an AWS EC2 instance.
The backend was implemented using FastAPI and exposes RESTful API endpoints for receiving image requests and returning prediction results. Upon receiving an image, the server validates the input and applies the same preprocessing operations used during model training, including RGB conversion, resizing to pixels, tensor conversion, and normalization. The preprocessed image is then forwarded to the pretrained IoT-RiceMobileNet model loaded from a .pth checkpoint file. During inference, the model is set to evaluation mode and executed within torch.no_grad() to disable gradient computation and reduce computational overhead. The model produces raw logits, which are converted into class probabilities using the Softmax function. The class with the highest probability is selected as the final prediction, and the corresponding confidence score is returned to the client in JSON format. This deployment architecture shown in Fig 8 enables real-time, scalable, and device-independent rice disease detection across IoT devices, mobile applications, and web platforms. Algorithm 1 summarizes the complete inference pipeline.
Algorithm 1: Cloud-based IoT-integrated rice disease detection and classification pipeline
Input: Image I submitted from an IoT device, mobile application, or web client
Output: Predicted disease class C and confidence score p
1: Initialize the FastAPI backend server on the AWS EC2 instance;
2: Load the trained IoT-RiceMobileNet model M from the .pth checkpoint;
3: Set the model to evaluation mode: ;
4: Receive image I through the REST API endpoint;
5: Validate the image format and request payload;
6: Convert I to RGB format;
7: Resize I to pixels;
8: Normalize I using the same preprocessing settings used during training;
9: Convert the preprocessed image into tensor T;
10: Add batch dimension to obtain input tensor ;
11: Disable gradient computation using torch.no_grad();
12: Compute raw model output logits: ;
13: Convert logits into class probabilities: ;
14: Determine the predicted class index: ;
15: Map class index k to disease class label C;
16: Extract confidence score: ;
17: Return JSON response containing {C,p} to the client;
Evaluation metrics and validation methodology
To evaluate the effectiveness of the proposed IoT-RiceMobileNet model and compare it with baseline architectures, we used standard classification metrics commonly employed in deep learning-based image classification. These metrics provide a comprehensive assessment of the model’s ability to classify rice leaf diseases accurately. We employed a multi-source dataset methodology to demonstrate the model’s resilience and generalizability.
Evaluation metrics
The following evaluation metrics were used to assess model performance:
- 1. Accuracy: Measures the proportion of correct predictions across all test samples:
- 2. Precision: Quantifies the reliability of positive predictions, indicating how often the model is correct when it predicts a disease class:
- 3. Recall: Measures how well the model identifies actual positive cases. In disease detection, high recall is critical because missing a disease could have serious agricultural consequences:
- 4. F1-Score: Balances precision and recall by computing their harmonic mean.
In the equations above, TP, TN, FP, and FN represent true positives, true negatives, false positives, and false negatives. We also created confusion matrices to show per-class performance and calculated ROC-AUC curves to measure class separation. Gradient-weighted Class Activation Mapping (Grad-CAM) was used to verify whether the model focused on disease-relevant leaf regions.
Dual-dataset validation strategy
To evaluate generalization, IoT-RiceMobileNet was tested on two dataset settings: the constructed RLD dataset of 7,092 images and an additional heterogeneous multi-source dataset of 14,758 images. The multi-soruce datasets are integrated from Kaggle [42] and Mendeley [43]. This dual-validation strategy reduces dataset bias and demonstrates that the model performs consistently across different data sources and image capture conditions.
Experiment results and analysis
Experimental setup
All experiments were conducted on a Windows-based workstation with high-performance computing capacity, including an Intel Core Ultra 9 285K processor running at 3.70 GHz, 64 GB RAM, and a 32 GB GPU. PyTorch was used for model implementation. To ensure reproducibility, the random seed was set to 42, and deterministic PyTorch settings were used across all experiments. Supplementary visualization and model parameter analysis scripts were executed on a separate Windows 11 Pro machine equipped with an Intel Core i5-12400 CPU and 24 GB RAM. All models were evaluated on a held-out test set and compared against multiple baseline and transfer learning configurations. Table 3 summarizes the architectural configurations and training settings of all evaluated models. All models share a common optimization framework, differing only in backbone architecture, fine-tuning strategy, and classification head design.
Comparative analysis of baseline models using RLD dataset
We benchmarked IoT-RiceMobileNet against multiple baseline and transfer learning configurations across the training, validation, and testing phases; the results are reported in Table 4. IoT-RiceMobileNet achieved the strongest validation and test performance, with 98.73% training accuracy, the highest validation accuracy of 97.85%, and the highest test accuracy of 99.19%. It also achieved the highest test precision and recall, with 99.20% precision and 99.19% recall, respectively. ResNet50 TL ranked second in test performance, achieving 98.82% training accuracy, 97.58% validation accuracy, and 96.64% test accuracy. These results indicate that IoT-RiceMobileNet provides stronger test-set generalization than the evaluated baseline and transfer learning models while maintaining a lightweight architecture suitable for IoT deployment.
Overall, transfer learning improved performance for most architectures, particularly ResNet50, MobileNetV2, and InceptionV3. ResNet50 TL achieved 96.64% test accuracy, substantially outperforming scratch-trained ResNet50, which achieved 82.68%. InceptionV3 TL and MobileNetV2 TL achieved 91.01% and 84.16% test accuracy, respectively, also outperforming their scratch-trained counterparts. However, scratch-trained VGG16 achieved 95.57% test accuracy, slightly outperforming VGG16 TL. These results indicate that the effectiveness of transfer learning varies across architectures, while IoT-RiceMobileNet achieved the strongest overall test-set performance with 99.19% accuracy. Fig 9 also shows the effectiveness of the proposed models during training.
The figure compares training accuracy, training loss, and validation accuracy over 50 epochs. Models were trained using early stopping and checkpoint selection based on validation loss.
Comparative analysis of baseline models using multi-source dataset
To further assess the robustness and generalizability of IoT-RiceMobileNet, additional experiments were conducted on a multi-source dataset comprising 14,758 rice leaf images collected from Kaggle [42] and Mendeley [43]. The dataset spans six rice disease categories and provides an additional benchmark with greater source diversity than the RLD dataset. Comparative results are presented in Table 5.
IoT-RiceMobileNet achieved the best overall performance on the multi-source dataset, with 98.91% training accuracy, 98.78% validation accuracy, and 99.25% test accuracy. It also achieved the highest test precision and recall, with values of 99.26% and 99.25%, respectively. ResNet50 TL ranked second in test performance, achieving 98.17% test accuracy, 98.19% test precision, and 98.17% test recall. These results demonstrate that IoT-RiceMobileNet maintained strong generalization on the heterogeneous multi-source dataset while preserving a lightweight architecture suitable for real-time IoT deployment. Among the remaining models, the scratch-trained VGG16 achieved 94.04% test accuracy, outperforming ResNet50, VGG16 TL, InceptionV3 TL, InceptionV3, MobileNetV2, MobileNetV2 TL, and the baseline CNN. ResNet50 achieved 93.29% test accuracy, while VGG16 TL, InceptionV3 TL, InceptionV3, MobileNetV2, and MobileNetV2 TL achieved 92.14%, 88.14%, 83.81%, 74.25%, and 72.90% test accuracy, respectively. The baseline CNN achieved 64.84% test accuracy, highlighting the limitations of shallow architectures for complex multi-class rice disease classification.
Overall, the results demonstrate that IoT-RiceMobileNet maintains consistently high classification performance across both the constructed RLD dataset and the more challenging multi-source dataset while preserving a lightweight architecture suitable for real-time IoT deployment, as discussed in the next subsection 17. Fig 10 shows the convergence behavior and learning stability during training.
The figure compares training accuracy, training loss, and validation accuracy over 50 epochs. Models were trained using early stopping and checkpoint selection based on validation loss.
Model complexity and storage efficiency
Table 6 compares the computational complexity, storage requirement, and inference efficiency of the evaluated models. IoT-RiceMobileNet contains 2.418M total parameters, of which 1.876M are trainable, and requires only 9.501 MB of storage for the full model. It operates at 0.670 G FLOPs with an inference latency of 11.741 ms, corresponding to 85.17 FPS. Since a higher FPS indicates faster inference throughput, these results demonstrate that the proposed model is compact and suitable for real-time IoT-based deployment.
Compared with the baseline MobileNetV2, IoT-RiceMobileNet introduces only a marginal increase in complexity, from 2.232M to 2.418M parameters, 8.779 MB to 9.501 MB full-model size, and 0.652 to 0.670 GFLOPs. This small increase is justified by the improved classification performance reported in the previous section. Compared with heavier architectures, the proposed model uses approximately ,
, and
fewer parameters than VGG16, InceptionV3, and ResNet50, respectively. Its full-model size is also substantially smaller than VGG16, InceptionV3, and ResNet50, which require 57.165 MB, 83.508 MB, and 90.050 MB, respectively. In terms of inference efficiency, IoT-RiceMobileNet achieves 85.17 FPS, compared with 12.45 FPS for VGG16, 22.74 FPS for ResNet50 TL, and 32.09 FPS for InceptionV3 TL. Its inference latency is approximately
lower than VGG16 and
lower than ResNet50 TL. Although MobileNetV2 TL achieves higher throughput, IoT-RiceMobileNet provides a better balance between classification performance and computational efficiency. The slightly higher latency compared with MobileNetV2 TL is mainly due to the additional convolutional refinement layers introduced in the proposed architecture.
Overall, IoT-RiceMobileNet achieves a favorable trade-off among classification performance, model compactness, and inference efficiency, supporting its practical deployment on resource-constrained IoT platforms for rice leaf disease detection.
Cross-validation strategy
To assess the stability and split-level generalization capability of IoT-RiceMobileNet, stratified 5-fold and 10-fold cross-validation were performed on the constructed RLD dataset. Stratification was used to preserve the original class distribution in each fold. Accuracy, precision, recall, and F1-score were recorded for each fold, and the mean () and standard deviation (s) were computed across folds to quantify average performance and variability. The 95% confidence interval (CI) was estimated using the t-distribution as follows:
where denotes the mean fold score, s denotes the standard deviation, n is the number of folds, and
is the critical t-value at the 95% confidence level.
Table 7 summarizes the cross-validation performance of IoT-RiceMobileNet on the constructed RLD dataset. In 5-fold cross-validation, the model achieved a mean accuracy of and a mean F1-score of
, with 95% confidence intervals of [97.33%, 98.75%] and [97.32%, 98.75%], respectively. In 10-fold cross-validation, the model achieved a mean accuracy of
and a mean F1-score of
, with 95% confidence intervals of [97.52%, 98.73%] and [97.52%, 98.73%], respectively. The low standard deviations and narrow confidence intervals indicate stable performance across different stratified data partitions. For each cross-validation fold, data splitting was performed before augmentation, and augmentation was applied only to the training subset of that fold to prevent data leakage between training and validation samples. Additionally, cross-validation for multi-source datasets can be found in Appendix A.
IoT system deployment and field validation
The proposed IoT-RiceMobileNet system was deployed using a cloud-based inference architecture that connects an ESP32-CAM device, a React Native Android application, and a FastAPI server hosted on an AWS EC2 instance running Ubuntu 22.04. The ESP32-CAM and mobile application capture rice leaf images and transmit them as JPEG files through multipart/form-data HTTP POST requests to the /predict API endpoint. Field connectivity was provided through a smartphone hotspot, requiring no dedicated networking infrastructure. The physical hardware setup, real-time field image capture, and Android application interface are shown in Figs 11 and 12.
(a): Physical hardware setup. (b): Real-time field image captured during testing.
The interface enables real-time rice disease prediction through image upload and model inference.
Upon receiving an image, the server validates the input and applies the same preprocessing pipeline used during training, including resizing to pixels, RGB conversion, and normalization. The preprocessed image is then passed to the deployed PyTorch-based IoT-RiceMobileNet model, where inference is performed in evaluation mode using torch.no_ grad(). The output logits are converted into class probabilities using softmax, and the predicted disease class with its confidence score is returned to the client as a JSON response. The main communication and deployment settings are summarized in Table 8.
End-to-end latency was measured over ten independent trials using client-side timestamps recorded before image transmission and after receiving the server response. The measured latency includes image upload, preprocessing, model inference, and response delivery:
The system achieved an average response time of 516 ms with a standard deviation of 23.1 ms, indicating consistent sub-second performance. Although IoT-RiceMobileNet can be exported to ONNX and TensorFlow Lite formats, cloud-based inference was adopted to reduce mobile application size, simplify model updates, and improve compatibility across heterogeneous devices. A pilot field validation was conducted using ten rice leaf images captured under real agricultural conditions through the ESP32-CAM device and the mobile application. The system processed all submissions without communication failure and correctly classified seven of the ten field samples, indicating preliminary feasibility under real agricultural conditions. Although this pilot evaluation is limited in scale, it provides preliminary evidence of practical field usability under real deployment conditions.
The battery operating time of the ESP32-CAM device, powered by an 11.1 V, 1000 mAh LiPo battery through an LM2596S buck converter, was estimated using the measured current consumption and the converter efficiency characteristics reported in the manufacturer’s datasheet [49]. The average operating current, determined from the measured current consumption during deep sleep, Wi-Fi idle, and active transmission over a 30-second image capture interval, was approximately 100 mA. Accordingly, the expected operating time was calculated as
This corresponds to approximately two 8-hour field sessions per charge, supporting sustained field operation under typical agricultural monitoring scenarios.
Proposed model detailed analysis and comparison
The IoT-RiceMobileNet model achieved test accuracies of 99.19% and 99.25% on the constructed RLD dataset and the multi-source dataset, respectively. The multi-source dataset was used as an additional heterogeneous benchmark, where all models were trained, validated, and tested using the same experimental protocol. Table 9 reports the training accuracy, validation accuracy, and test performance metrics of IoT-RiceMobileNet across both datasets.
The confusion matrices and ROC-AUC curves in Fig 13 further show the classification behavior of the model across all six rice leaf classes. Grad-CAM visualizations in Fig 14 show that the model mainly focuses on visible lesions and affected leaf regions for diseased samples. For healthy samples, the attention maps are more diffuse, reflecting the absence of localized disease symptoms. These visualizations support the interpretability of the proposed model.
(a): Confusion matrix for the constructed RLD dataset. (b): Confusion matrix for the multi-source dataset. (c): ROC-AUC curve for the constructed RLD dataset. (d): ROC-AUC curve for the multi-source dataset.
A: Diseased samples. B: Healthy samples. Warmer colors indicate higher influence on classification decisions.
Table 10 compares IoT-RiceMobileNet with recent rice disease detection studies. Although these studies differ in datasets, class numbers, and experimental settings, IoT-RiceMobileNet achieved a competitive accuracy of 99.19% across six classes while also supporting an end-to-end IoT deployment framework.
Discussion
The IoT-RiceMobileNet model demonstrates viable deployment of lightweight, real-time disease detection in agricultural environments with limited computing resources. The model achieved 99.19% test accuracy on the constructed RLD dataset and 99.25% test accuracy on the multi-source dataset, while maintaining 2.418M parameters, 9.501 MB model size, and 85.17 FPS, demonstrating strong performance across both evaluation settings. The results support the objective of this study: developing a lightweight, accurate, and deployable rice leaf disease detection framework for smart agriculture. Transfer learning effectiveness varied across architectures; however, IoT-RiceMobileNet achieved the strongest test-set performance across both evaluation settings while maintaining a substantially smaller model size than heavier transfer learning baselines. ResNet50 TL generally ranked second in the updated comparative experiments, but its larger parameter count and storage requirement make it less suitable for resource-constrained IoT deployment. The stable convergence behavior suggests that the selected optimization strategy and learning-rate scheduling contributed to effective model training.
Although IoT-RiceMobileNet achieved strong performance across the constructed RLD dataset and the multi-source benchmark dataset, several limitations remain. The field evaluation was limited to a small number of real-time trials and should be interpreted as a preliminary feasibility assessment rather than a large-scale deployment validation. Moreover, 95% confidence intervals are reported for the cross-validation experiments, formal pairwise statistical significance tests between IoT-RiceMobileNet and the baseline models were not conducted, as the baselines were evaluated using a single best-checkpoint protocol. Consequently, the observed performance differences, while consistent, are not accompanied by significance-based confirmation; pairwise testing, such as McNemar’s test on test-set predictions or paired t-tests across cross-validation folds, is left to future work. Furthermore, while the Model development section explains the rationale for each architectural component, a controlled ablation isolating their individual contributions was not conducted, leaving their specific quantitative impact uncharacterized as a limitation left to future work. In addition, the implemented prototype relied on cloud-based inference, which may be affected by network availability and latency in real-world agricultural settings, particularly in rural regions with poor or intermittent connectivity where reliance on a remote server may reduce reliability. Real-world farm environments vary significantly in lighting, image quality, and environmental conditions. Extensive field validation across regions and seasons is needed to assess practical performance. Additionally, expansion to more disease categories and integration with agricultural decision-support platforms would enhance utility.
Future work should focus on expanding the dataset across additional rice varieties, geographic regions, and environmental conditions; optimizing the model through quantization and pruning to support offline on-device edge deployment for low-connectivity regions; integrating environmental sensor data; improving explainability using SHAP or attention-based methods; conducting a controlled ablation study to quantify the contribution of each architectural component; performing pairwise statistical significance testing across models; and conducting larger field trials to further assess robustness under real deployment conditions.
Conclusion
This study introduces IoT-RiceMobileNet, a compact deep learning system for real-time rice leaf disease detection that achieved 99.19% test accuracy on the constructed RLD dataset while maintaining computational efficiency for mobile and IoT-based deployment. Additional evaluation on a heterogeneous multi-source dataset of 14,758 images across six classes further demonstrated strong generalization, with IoT-RiceMobileNet achieving 99.25% test accuracy. Grad-CAM analysis indicates that the model focuses on disease-relevant leaf regions during classification, while the end-to-end IoT integration supports the practical feasibility of the proposed system for agricultural deployment. The proposed architecture requires substantially fewer parameters than conventional deep learning models, enabling scalable and low-cost disease monitoring in resource-constrained farming communities. This work contributes toward practical digital agriculture by supporting scalable, low-cost rice disease monitoring in resource-constrained farming communities. With further field validation and dataset expansion, IoT-RiceMobileNet can serve as a practical foundation for accessible rice disease monitoring and decision support in precision agriculture.
Supporting information
S1 Appendix. Cross-validation results for the multisource dataset.
https://doi.org/10.1371/journal.pone.0356383.s001
(PDF)
References
- 1. Sokra I, Somaly S, Meta H, Sarun H, Molikoy C. Factors affecting rice production: A systematic review. J Agric Technol. 2026;2(1):19–46.
- 2. Shahriar Zaman Abid M, Jahan B, Mamun AA, Jakir Hossen M, Hossain Mazumder S. Bangladeshi crops leaf disease detection using YOLOv8. Heliyon. 2024;10(18):e36694. pmid:39309932
- 3. Jamal MR, Kristiansen P, Kabir MJ, Lobry de Bruyn L. Challenges and Adaptations for Resilient Rice Production under Changing Environments in Bangladesh. Land. 2023;12(6):1217.
- 4. Conde S, Catarino S, Ferreira S, Temudo MP, Monteiro F. Rice Pests and Diseases Around the World: Literature-Based Assessment with Emphasis on Africa and Asia. Agriculture. 2025;15(7):667.
- 5. Naresh Kumar B, Sakthivel S. Rice leaf disease classification using a fusion vision approach. Sci Rep. 2025;15(1):8692. pmid:40082482
- 6. Salka TD, Hanafi MB, Rahman SMSAA, Zulperi DBM, Omar Z. Plant leaf disease detection and classification using convolution neural networks model: a review. Artif Intell Rev. 2025;58(10).
- 7. Mukherjee R, Ghosh A, Chakraborty C, De JN, Mishra DP. Rice leaf disease identification and classification using machine learning techniques: A comprehensive review. Eng Appl Artif Intell. 2025;139:109639.
- 8. Simhadri CG, Kondaveeti HK, Vatsavayi VK, Mitra A, Ananthachari P. Deep learning for rice leaf disease detection: A systematic literature review on emerging trends, methodologies and techniques. Inform Process Agric. 2025;12(2):151–68.
- 9. Bijoy MH, Hasan N, Biswas M, Mazumdar S, Jimenez A, Ahmed F, et al. Towards Sustainable Agriculture: A Novel Approach for Rice Leaf Disease Detection Using dCNN and Enhanced Dataset. IEEE Access. 2024;12:34174–91.
- 10. Pai P, Amutha S, Patil S, Shobha T, Basthikodi M, Shafeeq BMA, et al. Deep learning-based automatic diagnosis of rice leaf diseases using ensemble CNN models. Sci Rep. 2025;15(1):27690. pmid:40730662
- 11. Upadhyay SK, Dwivedi AK. Rice Plant Disease Diagnosis Using SqueezeNet and Deep Transfer Learning. J Phytopathol. 2025;173(3).
- 12. Jafar A, Bibi N, Naqvi RA, Sadeghi-Niaraki A, Jeong D. Revolutionizing agriculture with artificial intelligence: plant disease detection methods, applications, and their limitations. Front Plant Sci. 2024;15:1356260. pmid:38545388
- 13. Upadhyay A, Chandel NS, Singh KP, Chakraborty SK, Nandede BM, Kumar M, et al. Deep learning and computer vision in plant disease detection: a comprehensive review of techniques, models, and trends in precision agriculture. Artif Intell Rev. 2025;58(3).
- 14. Akter S, Sumon RI, Ali H, Kim H-C. Utilizing Convolutional Neural Networks for the Effective Classification of Rice Leaf Diseases Through a Deep Learning Approach. Electronics. 2024;13(20):4095.
- 15. Gülmez B. Advancements in rice disease detection through convolutional neural networks: A comprehensive review. Heliyon. 2024;10(12):e33328. pmid:39021980
- 16. Shafik W, Tufail A, De Silva Liyanage C, Apong RAAHM. Using transfer learning-based plant disease classification and detection for sustainable agriculture. BMC Plant Biol. 2024;24(1):136. pmid:38408925
- 17. Sambana B, Nnadi HS, Wajid MA, Fidelia NO, Camacho-Zuñiga C, Ajuzie HD, et al. An efficient plant disease detection using transfer learning approach. Sci Rep. 2025;15(1):19082. pmid:40447666
- 18.
Masud KI, Bhottacharjee P, Saeid A, Mostofa MdG, Rahman MdO. Design and Development of an Efficient IoT-based Egg Incubation System. In: 2023 International Conference on Next-Generation Computing, IoT and Machine Learning (NCIM). IEEE; 2023. p. 1–6.
- 19. Nyakuri JP, Nkundineza C, Gatera O, Nkurikiyeyezu K, Mwitende G. AI and IoT-powered edge device optimized for crop pest and disease detection. Sci Rep. 2025;15(1):22905. pmid:40593124
- 20. Yu P, Teng F, Zhu W, Shen C, Chen Z, Song J. Cloud-edge-device collaborative computing in smart agriculture: architectures, applications, and future perspectives. Front Plant Sci. 2025;16:1668545. pmid:41164258
- 21. Ibrahim AS, Mohsen S, Selim IM, Alroobaea R, Alsafyani M, Baqasah AM, et al. AI-IoT based smart agriculture pivot for plant diseases detection and treatment. Sci Rep. 2025;15(1):16576. pmid:40360734
- 22. Dhaka VS, Kundu N, Rani G, Zumpano E, Vocaturo E. Role of Internet of Things and Deep Learning Techniques in Plant Disease Detection and Classification: A Focused Review. Sensors (Basel). 2023;23(18):7877. pmid:37765934
- 23.
Samanta R, Saha B. Affordable Precision Agriculture: A Deployment-Oriented Review of Low-Cost, Low-Power Edge AI and TinyML for Resource-Constrained Farming Systems. arXiv preprint arXiv:260315085. 2026.
- 24. Liu W, Duan B, Fan Z, Chen M, Qiu Z. A lightweight edge-deployable framework for intelligent rice disease monitoring based on pruning and distillation. Sensors. 2025;26(1):35.
- 25. Sethy PK, Barpanda NK, Rath AK, Behera SK. Deep feature based rice leaf disease identification using support vector machine. Comput Electron Agric. 2020;175:105527.
- 26. Chaudhary S, Kumar U. Automated detection and classification of rice crop diseases using advanced image processing and machine learning techniques. Trait Signal. 2024;41(2).
- 27. P. Narmadha R, Sengottaiyan N, J. Kavitha R. Deep Transfer Learning Based Rice Plant Disease Detection Model. Intell Autom Soft Comput. 2022;31(2):1257–71.
- 28. Li Y, Chen X, Yin L, Hu Y. Deep Learning-Based Methods for Multi-Class Rice Disease Detection Using Plant Images. Agronomy. 2024;14(9):1879.
- 29. Li P, Zhou J, Sun H, Zeng J. RDRM-YOLO: A High-Accuracy and Lightweight Rice Disease Detection Model for Complex Field Environments Based on Improved YOLOv5. Agriculture. 2025;15(5):479.
- 30. Al-Gaashani MSAM, Samee NA, Alnashwan R, Khayyat M, Muthanna MSA. Using a Resnet50 with a Kernel Attention Mechanism for Rice Disease Diagnosis. Life (Basel). 2023;13(6):1277. pmid:37374060
- 31. Haruna Y, Qin S, Mbyamm Kiki MJ. An Improved Approach to Detection of Rice Leaf Disease with GAN-Based Data Augmentation Pipeline. Appl Sci. 2023;13(3):1346.
- 32. Simhadri CG, Kondaveeti HK. Automatic Recognition of Rice Leaf Diseases Using Transfer Learning. Agronomy. 2023;13(4):961.
- 33. R P, Kalaiselvan P, Thomas N, T S. Detection of Rice Leaf Diseases Using Convolutional Neural Network. ECS Trans. 2022;107(1):5069–80.
- 34.
Sriram A, Ramasubramanian B, Parameswaran G, Krishnarani M, Raja E, Sundaresan S. IoT and Inception V3 for Precision Rice Disease Detection. In: 2025 International Conference on Modern Sustainable Systems (CMSS). IEEE; 2025. p. 1129–34.
- 35. Verma G, Saxena AK, Rai M, Shaheen M, Naaz S. IoT integrated CNN framework for automated detection and quantification of rice and potato crop diseases. Sci Rep. 2025;15(1):38199. pmid:41174145
- 36.
Saputra DID. Rice Leafs Disease Dataset. Kaggle. 2021. Accessed: 2026-03-14. Available from: https://www.kaggle.com/datasets/dedeikhsandwisaputra/rice-leafs-disease-dataset
- 37. Wang J, Ma S, Wang Z, Ma X, Yang C, Chen G, et al. Improved Lightweight YOLOv8 Model for Rice Disease Detection in Multi-Scale Scenarios. Agronomy. 2025;15(2):445.
- 38.
Masud KI, Rahman MdM, Jannat MstR, Hasan MM, Islam MdS, Rahman MdO. A CNN-based Model to Detect and Classify the Tomato Leaf Disease using Transfer Learning. In: 2024 27th International Conference on Computer and Information Technology (ICCIT). IEEE; 2024. p. 2823–8.
- 39.
Pal KK, Sudeep KS. Preprocessing for image classification by convolutional neural networks. In: 2016 IEEE International Conference on Recent Trends in Electronics, Information & Communication Technology (RTEICT). IEEE; 2016. p. 1778–81.
- 40. Haridasan A, Thomas J, Raj ED. Deep learning system for paddy plant disease detection and classification. Environ Monit Assess. 2022;195(1):120. pmid:36399232
- 41. Li R, Li Y, Qin W, Abbas A, Li S, Ji R, et al. Lightweight Network for Corn Leaf Disease Identification Based on Improved YOLO v8s. Agriculture. 2024;14(2):220.
- 42.
anshulm257. Rice Disease Dataset. Kaggle. 2020. Accessed: 2025-11-10. Available from: https://www.kaggle.com/datasets/anshulm257/rice-disease-dataset
- 43.
Hasan M, Khatun S, Raihan MA, Uddin AH. Rice Leaf Bacterial and Fungal Disease Dataset (Version 2). Mendeley Data. 2023. Accessed: 2025-11-10. Available from: https://data.mendeley.com/datasets/hx6f852hw4/2
- 44.
Goodfellow I, Bengio Y, Courville A. Deep learning. MIT Press; 2016. Available from: http://www.deeplearningbook.org
- 45. Yosinski J, Clune J, Bengio Y, Lipson H. How transferable are features in deep neural networks? Adv Neural Inform Process Syst. 2014;27.
- 46. Rachman RK, Setiadi DRIM, Susanto A, Nugroho K, Islam HMM. Enhanced Vision Transformer and Transfer Learning Approach to Improve Rice Disease Recognition. J Comput Theor Appl. 2024;1(4):446–60.
- 47.
Deng J, Dong W, Socher R, Li LJ, Li K, Fei-Fei L. Imagenet: A large-scale hierarchical image database. In: 2009 IEEE conference on computer vision and pattern recognition. IEEE; 2009. p. 248–55.
- 48. Lu J, Liu X, Ma X, Tong J, Peng J. Improved MobileNetV2 crop disease identification model for intelligent agriculture. PeerJ Comput Sci. 2023;9:e1595. pmid:37810352
- 49.
Texas Instruments. LM2596 SIMPLE SWITCHER Power Converter 150-kHz 3-A Step-Down Voltage Regulator; 2023. Available from: https://www.ti.com/lit/ds/symlink/lm2596.pdf
- 50. N K, Narasimha Prasad LV, Pavan Kumar CS, Subedi B, Abraha HB, V E S. Rice leaf diseases prediction using deep neural networks with transfer learning. Environ Res. 2021;198:111275. pmid:33989629
- 51. Patil RR, Kumar S, Chiwhane S, Rani R, Pippal SK. An Artificial-Intelligence-Based Novel Rice Grade Model for Severity Estimation of Rice Diseases. Agriculture. 2022;13(1):47.
- 52. Yang L, Guo F, Zhang H, Cao Y, Feng S. Research on Lightweight Rice False Smut Disease Identification Method Based on Improved YOLOv8n Model. Agronomy. 2024;14(9):1934.