Digital assessment of post-harvest Nendran banana for faster grading: CNN-based ripeness classification model

Arunima, P.L.; Gopinath, Pratheesh P.; Geetha Lekshmi, P.R.; Esakkimuthu, M.

doi:10.1016/j.postharvbio.2024.112972

Postharvest Biology and Technology

2024

DOI: 10.1016/j.postharvbio.2024.112972

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Digital assessment of post-harvest Nendran banana for faster grading: CNN-based ripeness classification model

P.L. Arunima,

Pratheesh P. Gopinath,

P.R. Geetha Lekshmi

et al.

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Cited by 3 publications

References 32 publications

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Fruits and vegetables preservation based on AI technology: Research progress and application prospects

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Computers and Electronics in Agriculture

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Fruits and vegetables preservation based on AI technology: Research progress and application prospects

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In-line semantic segmentation of kimchi cabbage deterioration using YOLOv8n and DeepLabv3+

Yang,

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Postharvest Biology and Technology

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Design and Experimental Study of Banana Bunch Transportation Device with Lifting Mechanism and Automatic Bottom-Fixing Fruit Shaft

Li,

Yang,

et al. 2024

Agriculture

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In addressing the challenges of high labor intensity, cost, and potential mechanical damage to banana fruit in orchards, this study presents the design of a banana bunch transport device featuring a lifting mechanism and an automatic fruit shaft bottom-fixing system. The device is tailored to the planting and morphological characteristics of banana bunches, aiming for efficient, low-loss, and labor-saving mechanized transport. Key design considerations included the anti-overturning mechanism and the lifting system based on transportation conditions and the physical dimensions of banana bunches. A dynamic simulation was conducted to analyze the angular velocity and acceleration during the initial conveying stages, forming the basis for the fruit shaft bottom-fixation mechanism. A novel horizontal multi-point scanning method was developed to accurately identify and secure the fruit shaft bottom, complemented by an automated control system. Experimental results showed a 95.83% success rate in identification and fixation, validated by field trials that confirmed the necessity and stability of the fixation mechanism. To enhance the durability of the fruit shaft bottom-fixation mechanism, a multi-factor test was conducted, optimizing the device’s maximum travel speed and minimizing the banana bunch’s oscillation angle. Field tests showed an oscillation angle of 8.961°, closely matching the simulated result of 9.526°, demonstrating the reliability of the response surface analysis model. This study offers a practical and efficient solution for banana bunch transport in orchards, showcasing significant practical value and potential for wider adoption.

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Digital assessment of post-harvest Nendran banana for faster grading: CNN-based ripeness classification model

Cited by 3 publications

References 32 publications

Fruits and vegetables preservation based on AI technology: Research progress and application prospects

Fruits and vegetables preservation based on AI technology: Research progress and application prospects

In-line semantic segmentation of kimchi cabbage deterioration using YOLOv8n and DeepLabv3+

Design and Experimental Study of Banana Bunch Transportation Device with Lifting Mechanism and Automatic Bottom-Fixing Fruit Shaft

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