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

Abstract

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.

Plant phenotyping relevance

トマトの花・果実の検出、開花時期と未熟果数の推定、収穫日の予測を行うカメラ型フェノタイピングシステムの設計・検証が研究の中心である。

Code and data availability

The paper describes a clip-type IoT camera system for tomato flower/fruit monitoring and harvest prediction, but no public dataset, image collection, trained model, or author analysis code is deposited or linked. The only URLs in the text are the license, a third-party 3D-printed trellis clip design, the M5Camera (M5)

No evidence-backed public reproduction asset is currently recorded.