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

Hiroki Naito (), Tomohiko Ota, Kota Shimomoto, Fumiki Hosoi and Tokihiro Fukatsu
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Hiroki Naito: Graduate School of Agricultural and Life Sciences, The University of Tokyo, Tokyo 1138657, Japan
Tomohiko Ota: Research Center for Agricultural Robotics, National Agriculture and Food Research Organization, Tsukuba 3050856, Japan
Kota Shimomoto: Institute of Agricultural Machinery, National Agriculture and Food Research Organization, Tsukuba 3050856, Japan
Fumiki Hosoi: Graduate School of Agricultural and Life Sciences, The University of Tokyo, Tokyo 1138657, Japan
Tokihiro Fukatsu: Institute of Agricultural Machinery, National Agriculture and Food Research Organization, Tsukuba 3050856, Japan

Agriculture, 2024, vol. 14, issue 12, 1-13

Abstract: 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.

Keywords: tomato; prediction; harvest working time; deep learning; Mask R-CNN (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2024
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