Work Planning and Scheduling in Farming

Nag, Pranab Kumar; Gite, L. P.

doi:10.1007/978-981-15-7269-2_5

Cited by 2 publications

(1 citation statement)

References 52 publications

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“…The percentage load on hand muscles against MVC was determined and presented in Figure 18. The higher percentage load of 40.51–59.06 on the hand muscles under investigation was observed for the traditional way of fruit handling, which is higher than the permissible muscle load of 35% (Nag & Gite, 2020). While, in the case of machine operation, the percentage load on the muscles ranged 28.67–34.31, which is within the permissible limit.…”

Section: Resultsmentioning

confidence: 84%

AI‐enabled farm‐friendly automatic machine for washing, image‐based sorting, and weight grading of citrus fruits: Design optimization, performance evaluation, and ergonomic assessment

Chakraborty

Subeesh

Potdar

et al. 2023

Journal of Field Robotics

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The modernization of postharvest operations and penetration of emerging technologies in horticultural processing have provided intelligent solutions for reducing postharvest losses. Work environmental and occupational health issues require immediate attention as the awkward posture and continuous drudgeryprone on-farm sorting and grading activities may lead to musculoskeletal disorders.The main objective of this study was to develop an automatic farm-friendly machine for real-time citrus fruit washing, image-based sorting, and weight grading; designed optimally and equipped with an embedded system comprising a lightweight convolutional neural network (CNN) model. Also included in this study was a thorough ergonomic assessment of the developed machine in a real work environment. The parametric choice of the fruit washing and singulation system was performed by employing computational fluid dynamics modeling and response surface methodology designed optimization. It was observed that under steady-state conditions, the water jet would arrive at a velocity of 11.36 m/s which would eventually suit a singulation conveyor with a slope of 25°. A noninvasive grading and sorting approach for citrus fruits is presented in this paper that leverages deep learning to classify the fruits into "accept" and "reject" classes. The custom lightweight CNN model "SortNet" has shown excellent classification results with an overall accuracy of 97.6%. The ergonomic evaluation shows that the average body part discomfort score in case of operating an automatic fruit grading machine was much lower (12.3 ± 2.0) than the traditional method (30.9 ± 3.3). Further, in the case of machine operation, the percentage load on the muscles ranged from 28.67 to 34.31 reflecting that subjects can work for longer duration on the machine without fatigue as compared with the traditional manual operation.

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Section: Resultsmentioning

confidence: 84%

AI‐enabled farm‐friendly automatic machine for washing, image‐based sorting, and weight grading of citrus fruits: Design optimization, performance evaluation, and ergonomic assessment

Chakraborty

Subeesh

Potdar

et al. 2023

Journal of Field Robotics

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Ergonomic Evaluation of a Walk‐Behind AI‐Based Cotton Fertilizer Applicator

Chouriya,

Soni,

Hota

et al. 2024

Journal of Field Robotics

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The mechanization of agricultural operations greatly influences both the efficiency of farming operations and the health and safety of farm workers. Human exposure to working environments represents a significant hazard associated with machines and the environment. This research focuses on evaluating the ergonomic aspects of the newly developed artificial intelligence (AI)‐based cotton fertilizer applicator, aiming to assess the potential impact on workers' health, safety, and overall exertion. Ergonomic factors such as heart rate (HR), oxygen consumption rate (OCR), energy expenditure rate (EER), physiological work cost, as well as discomfort and posture during use, were assessed at three different operating speeds of the applicator. Results showed that HRs, OCR, and EER ranged from 86 to 109 beats/min, 0.44 to 0.83 L/min, and 9.3 to 17.45 kJ/min, respectively, indicating that the physical demands on workers were within manageable limits. Discomfort was predominantly noted in the palms, attributed to the necessity of controlling the applicator with both hands. Analysis of cardiac effort revealed that 93.75%, 92.70%, and 94.67% of effort was expended during work at speeds of 0.5, 0.8, and 1 km/h, respectively, with the remainder during recovery periods. The workload on the middle deltoid muscle during field operation varied between 10.15%–14.58% for left‐hand controls and 1.85%–3.5% for right‐hand controls. Statistical analysis highlighted significant differences in physiological responses among participants, with ergonomic parameters closely linked to the applicator's forward speed. The study concludes using the developed AI‐based cotton fertilizer applicator does not pose significant health risks to operators, suggesting its ergonomic design effectively mitigates physical strain.

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Work Planning and Scheduling in Farming

Cited by 2 publications

References 52 publications

AI‐enabled farm‐friendly automatic machine for washing, image‐based sorting, and weight grading of citrus fruits: Design optimization, performance evaluation, and ergonomic assessment

AI‐enabled farm‐friendly automatic machine for washing, image‐based sorting, and weight grading of citrus fruits: Design optimization, performance evaluation, and ergonomic assessment

Ergonomic Evaluation of a Walk‐Behind AI‐Based Cotton Fertilizer Applicator

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