aiqbench analysis
This research presents a deep learning framework leveraging computer vision to automate raspberry maturity detection and yield forecasting, addressing critical supply chain challenges in high-value agriculture. By curating a novel phenology-based dataset with 6,907 annotations, the study enables state-of-the-art object detection models to predict harvest times up to one month in advance for optimized resource utilization.
Phenology-based learning framework for yield estimation and harvest forecasting of raspberry fruits
arXiv:2411.00967v2 Announce Type: replace-cross Abstract: The future of agriculture is intertwined with automation. Accurate fruit detection, yield estimation, and harvest time prediction are crucial for efficient supply chain management by optimizing resources and logistic utilization. Computer vision can automate these tasks to reduce labour costs and improve efficiency by training deep learning models on appropriate data to perform knowledge-based tasks. Although fruit detection has been the focus of literature, yield prediction and harvest time estimation remain practical challenges for farmers. This is particularly important for high-value, highly perishable crops, such as raspberries, where contractual obligations require precise harvest timing. This paper addresses this gap by providing a learning-based framework for raspberry maturity detection and harvest time estimation. For this end, a phenology study of raspberry plants from three different cultivars was carried out, and seven development stages were identified based on the BBCH-scale; for the first time, a phenology-based dataset of developing raspberry flowers and fruit was curated and made available publicly, which contains 1,853 high-resolution images and 6,907 manually labelled annotations. A comprehensive benchmark was developed from state-of-the-art object detection models, and finally, a tailored deep learning model capable of real-time inference was established that achieved 92.2% detection accuracy in the vertical farm field test. This paper provides a model that can estimate raspberry yield 28 to 33 days in advance of harvesting, a dataset that can be used by other researchers for object detection and segmentation model development, and a framework that can be extended for precise yield and harvest time estimation.
Abstract:The future of agriculture is intertwined with automation. Accurate fruit detection, yield estimation, and harvest time prediction are crucial for efficient supply chain management by optimizing resources and logistic utilization. Computer vision can automate these tasks to reduce labour costs and improve efficiency by training deep learning models on appropriate data to perform knowledge-based tasks. Although fruit detection has been the focus of literature, yield prediction and harvest time estimation remain practical challenges for farmers. This is particularly important for high-value, highly perishable crops, such as raspberries, where contractual obligations require precise harvest timing. This paper addresses this gap by providing a learning-based framework for raspberry maturity detection and harvest time estimation. For this end, a phenology study of raspberry plants from three different cultivars was carried out, and seven development stages were identified based on the BBCH-scale; for the first time, a phenology-based dataset of developing raspberry flowers and fruit was curated and made available publicly, which contains 1,853 high-resolution images and 6,907 manually labelled annotations. A comprehensive benchmark was developed from state-of-the-art object detection models, and finally, a tailored deep learning model capable of real-time inference was established that achieved 92.2% detection accuracy in the vertical farm field test. This paper provides a model that can estimate raspberry yield 28 to 33 days in advance of harvesting, a dataset that can be used by other researchers for object detection and segmentation model development, and a framework that can be extended for precise yield and harvest time estimation.
Computer Vision and Pattern Recognition (cs.CV); Robotics (cs.RO)
arXiv:2411.00967 [cs.CV]
(or arXiv:2411.00967v2 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2411.00967 Focus to learn more arXiv-issued DOI via DataCite
https://doi.org/10.1007/s41315-026-00564-5 Focus to learn more DOI(s) linking to related resources
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