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.

Keywords

• horticulture
• tomato cultivation
• deep learning
• harvest date estimation
• flowers and fruits detection
• internet of things