106. Deep learning for almond fruit detection at different growth stages for orchards pest management using unmanned aerial vehicles

Chen, Assaf; Nagar, Y.C.; Shoshani, Gil; Sharon, Rakefet

doi:10.3920/978-90-8686-947-3_106

Precision Agriculture ’23 2023

DOI: 10.3920/978-90-8686-947-3_106

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106. Deep learning for almond fruit detection at different growth stages for orchards pest management using unmanned aerial vehicles

Assaf Chen

Y.C. Nagar

Gil Shoshani

et al.

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2024

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Cited by 2 publications

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An improved pistachio detection approach using YOLO-v8 Deep Learning Models

Gökalp,

Mübarek Mazhar

2024

BIO Web Conf.

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Pistachios are an agricultural product widely used in the food industry. It is very important that pistachios are presented to the consumer in good quality on time. At the same time, whether the shells of pistachios are open or closed is an important criterion from a commercial industrial point of view. Pistachios with their shells open have a high unsaturated fat content, a high maturity level and an expensive market value. In this study, the open or closed status of pistachios was determined by using Artificial Intelligence-based deep learning models. For pistachio detection, 423 image data belonging to the Pesteh dataset were classified using models of the Yolov8 algorithm, which detects objects using convolutional neural networks. The data set is divided into 80% training, 10% validation and 10% testing. The performances of the models were evaluated with precision, recall, F1 and mAP score metrics. The highest test mAP value of the Yolov8 algorithm, which was run with image data consisting of pistachios, was obtained with the Yolov8-m model with 94.8%. The Yolov8-m model achieved a very successful result with 49.6 MB weight size, 11.0 ms inference time value and 0.33 hours training time value. In addition, the model's fast classification performance and small file size facilitate its applicability in the industrial field. The results show that the classification and detection of open and closed shell pistachios has been successfully carried out with Yolo models.

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An improved pistachio detection approach using YOLO-v8 Deep Learning Models

Gökalp,

Mübarek Mazhar

2024

BIO Web Conf.

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Development of a Drone-Based Phenotyping System for European Pear Rust (Gymnosporangium sabinae) in Orchards

Maß,

Seidl-Schulz,

Leipnitz

et al. 2024

Agronomy

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Computer vision techniques offer promising tools for disease detection in orchards and can enable effective phenotyping for the selection of resistant cultivars in breeding programmes and research. In this study, a digital phenotyping system for disease detection and monitoring was developed using drones, object detection and photogrammetry, focusing on European pear rust (Gymnosporangium sabinae) as a model pathogen. High-resolution RGB images from ten low-altitude drone flights were collected in 2021, 2022 and 2023. A total of 16,251 annotations of leaves with pear rust symptoms were created on 584 images using the Computer Vision Annotation Tool (CVAT). The YOLO algorithm was used for the automatic detection of symptoms. A novel photogrammetric approach using Agisoft’s Metashape Professional software ensured the accurate localisation of symptoms. The geographic information system software QGIS calculated the infestation intensity per tree based on the canopy areas. This drone-based phenotyping system shows promising results and could considerably simplify the tasks involved in fruit breeding research.

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106. Deep learning for almond fruit detection at different growth stages for orchards pest management using unmanned aerial vehicles

Cited by 2 publications

References 7 publications

An improved pistachio detection approach using YOLO-v8 Deep Learning Models

An improved pistachio detection approach using YOLO-v8 Deep Learning Models

Development of a Drone-Based Phenotyping System for European Pear Rust (Gymnosporangium sabinae) in Orchards

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