# Phenology-based learning framework for yield estimation and harvest forecasting of raspberry fruits

arXiv: https://arxiv.org/abs/2411.00967 · HTML: https://mlsanity.com/paper/2411.00967/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 15 of its 85 references that are in the map.
- First posted on arXiv 1 Nov 2024.

## Builds on (most cited first, 15 of 15)

- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [Mask R-CNN](https://mlsanity.com/paper/1703.06870/) (2017)
- [LVIS: A Dataset for Large Vocabulary Instance Segmentation](https://mlsanity.com/paper/1908.03195/) (2019)
- [A Comprehensive Survey on Transfer Learning](https://mlsanity.com/paper/1911.02685/) (2019)
- [Object Detection in 20 Years: A Survey](https://mlsanity.com/paper/1905.05055/) (2019)
- [YOLOv10: Real-Time End-to-End Object Detection](https://mlsanity.com/paper/2405.14458/) (2024)
- [MinneApple: A Benchmark Dataset for Apple Detection and Segmentation](https://mlsanity.com/paper/1909.06441/) (2019)
- [Grape detection, segmentation and tracking using deep neural networks and three-dimensional association](https://mlsanity.com/paper/1907.11819/) (2019)
- [A Sentinel-2 multi-year, multi-country benchmark dataset for crop classification and segmentation with deep learning](https://mlsanity.com/paper/2204.00951/) (2022)
- [Localizing Small Apples in Complex Apple Orchard Environments](https://mlsanity.com/paper/2202.11372/) (2022)
- [Fuzzy clustering for the within-season estimation of cotton phenology](https://mlsanity.com/paper/2211.14099/) (2022)
- [CitDet: A Benchmark Dataset for Citrus Fruit Detection](https://mlsanity.com/paper/2309.05645/) (2023)
- [Di-NeRF: Distributed NeRF for Collaborative Learning with Relative Pose Refinement](https://mlsanity.com/paper/2402.01485/) (2024)
- [3D shape sensing and deep learning-based segmentation of strawberries](https://mlsanity.com/paper/2111.13663/) (2021)

## Abstract (arXiv)

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.