Advanced design and Engi-economical evaluation of an automatic sugarcane seed cutting machine based RGB color sensor

• Published in: PloS one Vol. 19; no. 10; p. e0306584
• Main Authors: Elwakeel, Abdallah Elshawadfy, Nasrat, Loai S, Badawy, Mohamed Elshahat, Elzein, I M, Mahmoud, Mohamed Metwally, Kitmo, Hussein, Mahmoud M, Hussein, Hany S, El-Messery, Tamer M, Nyambe, Claude, Elsayed, Salah, Ourapi, Manar A
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 17.10.2024
• Subjects: Accuracy; Agricultural production; Agriculture - economics; Agriculture - instrumentation; Agriculture - methods; Analysis; Automation; Biology and Life Sciences; Cleaning process; Cleaning systems; Color; Color vision; Complex systems; Cost analysis; Cutlery; Cutting equipment; Cutting machines; Cutting parameters; Cutting speed; Damage assessment; Damage detection; Deep learning; Design; Diameters; Economic analysis; Efficiency; Engineering and Technology; Equipment and supplies; Equipment Design; Extreme values; Image processing; Internet of Things; Knives; Machine Learning; Machine vision; Medicine and Health Sciences; Neural networks; Operating costs; Performance evaluation; Saccharum; Seeds; Sensors; Social Sciences; Sugar industry; Sugarcane; Technology application; Technology assessment; Vision systems; Egypt
• DOI: 10.1371/journal.pone.0306584
• ISSN: 1932-6203

There are many problems related to the use of machine learning and machine vision technology on a commercial scale for cutting sugarcane seeds. These obstacles are related to complex systems and the way the farmers operate them, the possibility of damage to the buds during the cleaning process, and the high cost of such technology. In order to address these issues, a set of RGB color sensors was used to develop an automated sugarcane seed cutting machine (ASSCM) capable of identifying the buds that had been manually marked with a unique color and then cutting them mechanically, and the sugarcane seed exit chute was provided with a sugarcane seed monitoring unit. The machine's performance was evaluated by measuring the damage index at sugarcane stalk diameters of 2.03, 2.72, 3.42, and 3.94 cm. where two different types of rotary saw knives had the same diameter of 7.0 in/180 mm the two knives had 30 and 80 teeth, also we used five cutting times of 1000, 1500, 2000, 2500, and 3000 ms. All tests were done at a fixed cutting speed of 12000 rpm. In addition, the machine's performance was evaluated by conducting an economic analysis. The obtained results showed that the most damage index values were less than 0.00 for all cutting times and sugarcane stalk diameters under testing, while the DI values were equal zero (partial damage) for sugarcane stalk diameter of 3.42 cm at cutting times of 2000 ms and 2500 ms, in addition to the DI values being equal zero (extreme damage) for sugarcane stalk diameter of 3.94 cm at cutting times of 1500 ms and 2000 ms. The economic analysis showed that the total cost of sugarcane seeds per hectare is 70.865 USD. In addition, the ASSCM can pay for itself in a short period of time. The payback time is 0.536 years, which means that the ASSCM will save enough money to pay for itself in about 6.43 months. Finally, we suggest using a rotary saw knife with 80 teeth and a cutting time of 2000 ms to cut sugarcane stacks with an average diameter of 2.72 cm. This will result in higher performance and lower operating costs for the ASSCM.