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一种自动化、夹式、基于小型物联网摄像头的番茄花果监测与收获预测系统。

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

机构信息

Research Center for Agricultural Robotics, National Agriculture Food Research Organization (NARO), Tsukuba 305-0856, Japan.

Institute of Vegetable and Floriculture Science, National Agriculture Food Research Organization (NARO), Tsukuba 305-8519, Japan.

出版信息

Sensors (Basel). 2022 Mar 23;22(7):2456. doi: 10.3390/s22072456.

DOI:10.3390/s22072456
PMID:35408071
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9002604/
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.

摘要

利用图像分析进行自动化作物监测在园艺中被广泛应用。图像处理技术已经在多项研究中被用于监测作物生长、确定收获时间和估算产量。然而，由于花朵和果实位于植株集群中的个体植株上，因此除了跟踪它们的运动之外，对其进行准确监测是很困难的。在这项研究中，我们提出并设计了一种用于番茄种植的基于自动夹式物联网 (IoT) 摄像头的生长监测和收获日期预测系统。在温室的桁架上安装了多个夹式 IoT 摄像头，并使用基于深度学习的开花花朵和未成熟果实检测来监测番茄花朵和果实的生长情况。此外，还利用这些数据和温室内部的温度来计算收获日期。我们的系统经过了三个月的测试。使用我们的系统测量的收获日期与手动记录的数据相当。这些结果表明，该系统可以在番茄植株上准确检测开花期、未成熟果实的数量，并在 ±2.03 天的误差范围内预测收获日期。该系统可用于支持温室中的作物生长管理。

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