Fruit Monitoring and Harvest Date Prediction Using On-Tree Automatic Image Tracking

• Published in: IEEE transactions on agrifood electronics 2024-06, p.1-13
• Main Authors: Gimenez-Gallego, Jaime, Martinez-del-Rincon, Jesus, Blaya-Ros, Pedro J., Navarro-Hellin, Honorio, Navarro, Pedro J., Torres-Sanchez, Roque
• Format: Article
• Language: English
• Subjects: Artificial intelligence (AI); Cameras; deep learning; fruit ripeness; fruit segmentation; Image color analysis; internet of things; Manuals; Monitoring; precision agriculture; Sensors; Temperature measurement; Temperature sensors
• ISSN: 2771-9529; 2771-9529
• DOI: 10.1109/TAFE.2024.3408912

Fruit harvest date prediction is crucial to optimize resource management, maximize quality, and minimize waste of this food. For this purpose, it is necessary to monitor the fruit ripening stage. However, current measurement procedures pose drawbacks for widespread field deployment: laboratory trials are manual, destructive and expensive; measurements with hand-held portable equipment in the field are very time consuming; and the use of remote sensing mobile platforms has a high operating cost. In this article, a low-cost autonomous fixed sensor for continuous on-tree monitoring of pomegranates is proposed. It is based on a computer vision system able to extract reliable fruit color and size estimations automatically. In addition, an empirical quantitative and qualitative study on the effectiveness of using image-based monitoring in comparison with in situ manual and lab-based measurements for pomegranates is provided in this work. Another contribution of this article is a harvest date prediction model that employs the fruit information collected from the images. Furthermore, a thorough quantitative evaluation of the proposed prediction model for the fruit harvest date was performed, being the median error of the best model of 3.5 days.