An Automated, Clip-Type, Small Internet of Things Camera-Based Tomato Flower and Fruit Monitoring and Harvest Prediction System

Sensors

Tomomi Sugiyama¹  Dong-Hyuk Ahn¹  Md Parvez Islam²  Yasushi Kawasaki²  Unseok Lee²  Kenichi Tokuda²  Hiroki Naito²  Nobuo Kochi²  Tomohiko Ota²  Yuka Nakano² 

[1] Institute of Vegetable and Floriculture Science, National Agriculture Food Research Organization (NARO), Tsukuba 305-8519, Japan;Research Center for Agricultural Robotics, National Agriculture Food Research Organization (NARO), Tsukuba 305-0856, Japan;

关键词: horticulture;    tomato cultivation;    deep learning;    harvest date estimation;    flowers and fruits detection;    internet of things;   

DOI  :  10.3390/s22072456

来源: DOAJ

【 摘 要 】

Automated crop monitoring using image analysis is commonly used in horticulture. Image-processing technologies have been used in several studies to monitor growth, determine harvest time, and estimate yield. However, accurate monitoring of flowers and fruits in addition to tracking their movements is difficult because of their location on an individual plant among a cluster of plants. In this study, an automated clip-type Internet of Things (IoT) camera-based growth monitoring and harvest date prediction system was proposed and designed for tomato cultivation. Multiple clip-type IoT cameras were installed on trusses inside a greenhouse, and the growth of tomato flowers and fruits was monitored using deep learning-based blooming flower and immature fruit detection. In addition, the harvest date was calculated using these data and temperatures inside the greenhouse. Our system was tested over three months. Harvest dates measured using our system were comparable with the data manually recorded. These results suggest that the system could accurately detect anthesis, number of immature fruits, and predict the harvest date within an error range of ±2.03 days in tomato plants. This system can be used to support crop growth management in greenhouses.

【 授权许可】

Unknown