Developments in Automated Harvesting Equipment for the Apple in the orchard: Review

Tianjing, Yi; Mhamed, Mustafa

doi:10.1016/j.atech.2024.100491

Smart Agricultural Technology

2024

DOI: 10.1016/j.atech.2024.100491

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Developments in Automated Harvesting Equipment for the Apple in the orchard: Review

Yi Tianjing,

Mustafa Mhamed

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Cited by 3 publications

References 94 publications

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Design and development of machine vision robotic arm for vegetable crops in hydroponics

Khan,

Farooq,

Saleem

et al. 2024

Smart Agricultural Technology

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Design and development of machine vision robotic arm for vegetable crops in hydroponics

Khan,

Farooq,

Saleem

et al. 2024

Smart Agricultural Technology

View full text Add to dashboard Cite

Apple varieties and growth prediction with time series classification based on deep learning to impact the harvesting decisions

Mhamed,

Zhang,

Hua

et al. 2025

Computers in Industry

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Autonomous Yield Estimation System for Small Commercial Orchards Using UAV and AI

Kodors,

Zarembo,

Lācis

et al. 2024

Drones

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In the context of precision horticulture, decision support tools play a significant role in providing fruit growers with insights into orchard conditions, facilitating informed decisions regarding orchard management practices. This study presents the development of an autonomous yield estimation system designed to provide decision support to small commercial orchards. Autonomous yield estimation is based on the application of UAVs and AI. AI is used to identify and quantify fruitlets and fruits in photographs collected by UAV. In this article, we present our prototype of an autonomous yield estimation system. The adapted “4+1” architecture was applied to design a system with a holistic approach analyzing software, hardware, and ecosystem requirements. Six datasets are presented, which contain the images of fruitlets and fruits of apples, pears, and cherries. Three CNN models were trained: YOLOv8m, YOLOv9m, and YOLOv10m. The experiment showed that the most accurate was YOLOv9m, which achieved mean accuracies of 0.896 mAP@50 and 0.510 mAP@50:95 for all datasets.

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Developments in Automated Harvesting Equipment for the Apple in the orchard: Review

Cited by 3 publications

References 94 publications

Design and development of machine vision robotic arm for vegetable crops in hydroponics

Design and development of machine vision robotic arm for vegetable crops in hydroponics

Apple varieties and growth prediction with time series classification based on deep learning to impact the harvesting decisions

Autonomous Yield Estimation System for Small Commercial Orchards Using UAV and AI

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