Accuracy Assessment of Tomato Harvest Working Time Predictions from Panoramic Cultivation Images

• Published in: Agriculture (Basel) 2024-12, Vol.14 (12), p.2257
• Main Authors: Naito, Hiroki, Ota, Tomohiko, Shimomoto, Kota, Hosoi, Fumiki, Fukatsu, Tokihiro
• Format: Article
• Language: English
• Subjects: Accuracy; Agricultural production; Agriculture; Algorithms; Artificial neural networks; Cost allocation; Cultivation; Deep learning; Error analysis; Fruit cultivation; Fruits; Greenhouses; Harvest; harvest working time; Horticulture; Labor; Labor costs; Machine learning; Mask R-CNN; Monitoring systems; Neural networks; Planning; prediction; Predictions; Production costs; tomato; Tomatoes; Vegetable industry; Working hours; Workloads
• ISSN: 2077-0472; 2077-0472
• DOI: 10.3390/agriculture14122257

The scale of horticultural facilities in Japan is expanding, making the efficient management of labor costs essential, particularly in large-scale tomato production. This study developed a consistent and practical system for predicting harvest working time and estimating the quantity and weight of harvested fruit using panoramic images of cultivation rows. The system integrates a deep learning model, the Mask ResNet-50 convolutional neural network, to count harvestable fruits from images and a predictive algorithm to estimate working time based on the fruit count. The results indicated that the average for all workers could be predicted with an error margin of 30.1% when predicted three days before the harvest date and 15.6% when predicted on the harvest date. The trial also revealed that the accuracy of the predictions varied based on workers’ experience and cultivation methods. This study highlights the system’s potential to optimize harvesting plans and labor allocation, providing a novel tool for reducing labor costs while maintaining efficiency in large-scale tomato greenhouse production.