Shape classification technology of pollinated tomato flowers for robotic implementation.

Abstract

Three pollination methods are commonly used in the greenhouse cultivation of tomato. These are pollination using insects, artificial pollination (by manually vibrating flowers), and plant growth regulators. Insect pollination is the preferred natural technique. We propose a new pollination method, using flower classification technology with Artificial Intelligence (AI) administered by drones or robots. To pollinate tomato flowers, drones or robots must recognize and classify flowers that are ready to be pollinated. Therefore, we created an AI image classification system using a machine learning convolutional neural network (CNN). A challenge is to successfully classify flowers while the drone or robot is constantly moving. For example, when the plant is shaking due to wind or vibration caused by the drones or robots. The AI classifier was based on an image analysis algorithm for pollination flower shape. The experiment was performed in a tomato greenhouse and aimed for an accuracy rate of at least 70% for sufficient pollination. The most suitable flower shape was confirmed by the fruiting rate. Tomato fruit with the best shape were formed by this method. Although we targeted tomatoes, the AI image classification technology is adaptable for cultivating other species for a smart agricultural future.

Plant phenotyping relevance

トマト花の受粉適期を画像形状から分類するCNNシステムの開発と、結実率による検証が研究の中心であり、植物状態を抽出するフェノタイピング手法に該当する。

Code and data availability

The paper describes a CNN-based tomato flower image classification system for robotic pollination, but no public dataset, image collection, code repository, or trained model is deposited. The only availability statement directs readers to contact the corresponding author, so the paper-specific flower image dataset and

No evidence-backed public reproduction asset is currently recorded.