ArticlePloS one2024
Advanced design and Engi-economical evaluation of an automatic sugarcane seed cutting machine based RGB color sensor.
Article in PloS one, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Drying kinetics, power consumption, economic and environmental analysis of pomegranate peels drying using a hybrid solar dryer compared with oven dryer.Scientific reports · 2026Article
- Modeling, environmental and economic analysis of drying of orange slice in an automatic indirect mixed mode solar dryer.Scientific reports · 2026Article
- Development, performance evaluation and prediction of optimal operational conditions for a double-row sugarcane harvester using deep learning.Scientific reports · 2025Article
- Effect of different levels of operating pressure and drying temperature on thin layer modeling and drying costs of mint leaves.Scientific reports · 2025Article
- Automated vacuum drying kinetics, thermodynamics, and economic analysis of sage leaves.Scientific reports · 2025Article
- Correction: Advanced design and Engi-economical evaluation of an automatic sugarcane seed cutting machine based RGB color sensor.PloS one · 2025Article
- Optimized design and performance evaluation of a highly precise variable rate mis-planting and replanting potato electronic-metering mechanism.Frontiers in plant science · 2025Article
Corrections and comments
- Erratum issued
Authors and funding
12 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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.
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