Development, performance evaluation and prediction of optimal operational conditions for a double-row sugarcane harvester using deep learning
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Date
2025-12-01
Journal Title
Journal ISSN
Volume Title
Publisher
Springer Nature
Abstract
Sugarcane is a vital global crop, serving as a primary source of sugar, biofuel, and renewable energy. Advancements in harvesting are critical to meeting rising demand, enhancing profitability, and supporting eco-friendly agricultural practices in the sugarcane sector. Based on the current challenges of sugarcane harvesting in developed countries, the current study aimed to develop a semiautomatic whole-stalk sugarcane harvester (SWSH) for harvesting two rows of sugarcane stalks at a time and to be front-mounted on a classic four-wheel agricultural tractor. Then performance evaluation and prediction of optimal operational conditions for a double-row sugarcane harvester using Feedforward Neural Network (FNN) and Deep Neural Network (DNN) at different levels of forward speeds (3, 3.5, 4.5, and 5 km/h), row spacing (71, 78.89, and 88.75 cm), cutting heights (0, 2, and 4 cm), and numbers of knives (2 and 4) of the cutting systems. The obtained results showed that the cutting efficiency of the developed SWSH reached 100%. Where the higher cutting efficiency was observed at a cutting height equal to zero (ground level), forward speed of 3 km/hand row spacing of 71 cm using both 2 and 4 knives. The minimum total operating cost of the developed SWSH was about 4.42 USD/ha, and it was detected when using a forward speed of 4.5 km/h, row spacing of 88.75 cm, a cutting height of 4 cm, and two knives only on the cutting disk. Furthermore, at a row spacing of 88.75 cm, the maximum field capacity of the developed SWSH was 0.554 ha/h, observed at a forward speed of 4.5 km/h.
Description
The study develops and evaluates a deep-learning–enhanced double-row sugarcane harvester that integrates Feedforward and Deep Neural Networks to predict optimal operational conditions (forward speed, row spacing, cutting height, and knife count). Field trials demonstrated 100% cutting efficiency at ground-level cutting, 3 km/h, and 71 cm spacing, with minimal cost (~USD 4.42/ha) and peak field capacity (0.554 ha/h) at 4.5 km/h, 88.75 cm spacing, 4 cm cutting height, and two knives. This innovation fosters mechanization, boosts agricultural productivity, reduces labor demands, and curbs energy use—advancing SDG 2 (zero hunger), SDG 8 (decent work & economic growth), SDG 9 (industry & innovation), and SDG 12 (sustainable consumption), aligning with Uganda’s NDP IV goals for agro-industrial development, technology adoption, and rural livelihoods enhancement.
Keywords
Sugarcane harvesting, Artificial intelligence (AI), Farm machinery, Agricultural engineering, Neural networks
Citation
2. Elwakeel, A. E., Elden, A. Z., Ahmed, S. F., Issa, S., Li, C., Ali, K. A. M., Hanafy, W. M., Ali, G., Alzahrani, F., & Ahmed, A. F. (2025). Development, performance evaluation and prediction of optimal operational conditions for a double-row sugarcane harvester using deep learning. Scientific Reports, 15, 1–24.