System of counting green oranges directly from trees using artificial intelligence

Abstract

Abstract Agriculture is one of the most essential activities for humanity. Systems capable of automatically harvesting a crop using robots or performing a reasonable production estimate can reduce costs and increase production efficiency. With the advancement of computer vision, image processing methods are becoming increasingly viable in solving agricultural problems. Thus, this work aims to count green oranges directly from the trees through video footage filmed in line along a row of orange trees on the plantation. For the video image processing flow, a solution was proposed integrating the YOLOv4 network with object tracking algorithms. In order to compare the performance of the counting algorithm using the YOLOv4 network, an optimal object detector was simulated in which frame-by-frame corrected detections were used in which all oranges in all video frames were detected, and there were no erroneous detections. The use of YOLOv4 together with object detectors managed to reduce the number of double counting error and obtained a count close to the actual number of oranges visible in the video. The study also resulted in a database with an amount of 644 images with 43109 annotated oranges that can be used in future works.

Plant phenotyping relevance

樹上の果実数という植物器官の形質を、動画・YOLOv4・追跡アルゴリズムで自動抽出する手法を開発・評価しており、画像データセットも作成しているため、方法が中心的である。

Code and data availability

The paper describes a paper-specific green-orange image dataset (644 images, 43,109 annotated oranges) and a YOLOv4-based counting system, but the Data Availability statement says the datasets are available only from the corresponding author on reasonable request. The only public URL mentioned (github.com/theAIGuysCode

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