Development, performance evaluation and prediction of optimal operational conditions for a double-row sugarcane harvester using deep learning

Simple item page
dc.contributor.authorElwakeel, Abdallah Elshawadfy
dc.contributor.authorElden, Abdallah Zein
dc.contributor.authorAhmed, Saad F.
dc.contributor.authorIssa, Sali
dc.contributor.authorLi, Changyou
dc.contributor.authorAli, Khaled Abdeen Mousa
dc.contributor.authorHanafy, Waleed M.
dc.contributor.authorAli, Guma
dc.contributor.authorAlzahrani, Fawaz
dc.contributor.authorFathy, Atef
dc.date.accessioned2025-12-17T17:48:52Z
dc.date.available2025-12-17T17:48:52Z
dc.date.issued2025-12-01
dc.descriptionThe 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.
dc.description.abstractSugarcane 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.
dc.description.sponsorshipDeanship of Graduate Studies and Scientific Research, Taif University, Kingdom of Saudi Arabia.
dc.identifier.citation2. 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.
dc.identifier.issn2045-2322
dc.identifier.urihttps://dir.muni.ac.ug/handle/20.500.12260/838
dc.language.isoen
dc.publisherSpringer Nature
dc.subjectSugarcane harvesting
dc.subjectArtificial intelligence (AI)
dc.subjectFarm machinery
dc.subjectAgricultural engineering
dc.subjectNeural networks
dc.titleDevelopment, performance evaluation and prediction of optimal operational conditions for a double-row sugarcane harvester using deep learning
dc.typeArticle

Files

Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
Guma_Article_2025_01122025.pdf
Size:
5.2 MB
Format:
Adobe Portable Document Format
Download
License bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
license.txt
Size:
4.17 KB
Format:
Item-specific license agreed upon to submission
Description:
Download

Collections

Research Articles