Agricultural Engineering Department, Faculty of Agriculture and Natural Resources, Aswan University, Aswan, Egypt.
Electrical Power Engineering Department, Faculty of Engineering, Aswan University, Aswan, Egypt.
PLoS One. 2024 Oct 17;19(10):e0306584. doi: 10.1371/journal.pone.0306584. eCollection 2024.
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.
在商业规模上使用机器学习和机器视觉技术切割甘蔗种子存在许多问题。这些障碍与复杂的系统以及农民操作它们的方式、在清洁过程中损坏芽的可能性以及这种技术的高成本有关。为了解决这些问题，我们使用了一组 RGB 颜色传感器来开发一种能够识别用独特颜色手动标记的芽的自动甘蔗种子切割机 (ASSCM)，然后用机械方式切割它们，并在甘蔗种子出口滑槽上提供了一个甘蔗种子监测单元。通过测量甘蔗茎直径为 2.03、2.72、3.42 和 3.94 cm 时的损伤指数来评估机器的性能。两种不同类型的旋转锯刀具有相同的 7.0 英寸/180 毫米直径，两种锯刀分别有 30 个和 80 个齿，我们还使用了 1000、1500、2000、2500 和 3000 毫秒的 5 个切割时间。所有测试均在固定切割速度 12000 rpm 下进行。此外，我们还通过经济分析评估了机器的性能。结果表明，在所有切割时间和测试的甘蔗茎直径下，损伤指数的最大值都小于 0.00，而在 2000 毫秒和 2500 毫秒的切割时间下，直径为 3.42 厘米的甘蔗茎的 DI 值为零（部分损伤），此外，在 1500 毫秒和 2000 毫秒的切割时间下，直径为 3.94 厘米的甘蔗茎的 DI 值为零（严重损伤）。经济分析表明，每公顷甘蔗种子的总成本为 70.865 美元。此外，ASSCM 可以在短时间内收回成本。投资回报时间为 0.536 年，这意味着 ASSCM 将在大约 6.43 个月内节省足够的资金来支付自己的费用。最后，我们建议使用带有 80 个齿的旋转锯刀和 2000 毫秒的切割时间来切割平均直径为 2.72 厘米的甘蔗堆。这将提高 ASSCM 的性能并降低运营成本。
