Fig 1.
Composition of sugarcane stalk after cleaning.
Fig 2.
a. Main components of the ASSCM [30]. b. Laboratory test of the ASSCM prototype [30].
Fig 3.
The main dimensions of the machine frame.
Fig 4.
The main parts of the feeding system.
Fig 5.
The detailed views and dimensions of the feeding system.
Fig 6.
Signals flow from the color sensor to the data processing unit (Arduino mega board).
Fig 7.
The principle of the color sensor operation is in measuring the object colors.
Fig 8.
Operation principle of the sugarcane seed monitoring system.
Fig 9.
Specifications of different electronic parts used in the current study.
Fig 10.
The correct electrical connections for various electronic components used in the current study.
Fig 11.
The operating algorithm for the optical scanning system and automatic cutting system used in the ASSCM.
Fig 12.
Operating algorithm of the sugarcane seed monitoring system.
Fig 13.
Operating algorithm of the Wi-Fi module.
Fig 14.
Classification of damage caused to sugarcane seeds, according to [43, 46].
Fig 15.
Calibration of the color sensors: a. red channel; b. green channel; and c. blue channel.
Fig 16.
Calibration results for ultrasonic sensor.
Table 1.
Classification of sugarcane stalks used in the current study based on stalk diameters.
Fig 17.
The relationship between ID and the cutting time, where SD means without damage, PD means partial damage, and ED means extreme damage.
Fig 18.
Frequency of damage as a function of cutting time, where d1 = 2.03 cm, d2 = 2.72 cm, d3 = 3.42 cm, d4 = 3.94 cm, T1 = 1000 ms, T2 = 1500 ms, T3 = 2000 ms, T4 = 2500 ms, and T5 = 3000 ms.
Table 2.
Different costs related to the ASSCM and PV system.
Table 3.
Economic analysis of the ASSCM and PV system.
Table 4.
Comparison of the ASSCM with other technologies *.