Fast Detection of Tomato Sucker Using Semantic Segmentation Neural Networks Based on RGB-D Images

Abstract

Tomato sucker or axillary shoots should be removed to increase the yield and reduce the disease on tomato plants. It is an essential step in the tomato plant care process. It is usually performed manually by farmers. An automated approach can save a lot of time and labor. In the literature review, we see that semantic segmentation is a process of recognizing or classifying each pixel in an image, and it can help machines recognize and localize tomato suckers. This paper proposes a semantic segmentation neural network that can detect tomato suckers quickly by the tomato plant images. We choose RGB-D images which capture not only the visual of objects but also the distance information from objects to the camera. We make a tomato RGB-D image dataset for training and evaluating the proposed neural network. The proposed semantic segmentation neural network can run in real-time at 138.2 frames per second. Its number of parameters is 680, 760, much smaller than other semantic segmentation neural networks. It can correctly detect suckers at 80.2%. It requires low system resources and is suitable for the tomato dataset. We compare it to other popular non-real-time and real-time networks on the accuracy, time of execution, and sucker detection to prove its better performance.

Plant phenotyping relevance

トマトの腋芽をRGB-D画像からセマンティックセグメンテーションで検出・局在化する手法を開発し、データセット作成と性能比較・検証を行っており、植物形態の取得が中心である。

Code and data availability

The paper describes a custom RGB-D tomato sucker dataset (600+ images) and a proposed semantic segmentation network, but no block contains any public deposit, availability statement, or authors' URL for the dataset, code, or trained models. The only URL present is the CC BY license link, which is not a paper-specific资产

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