CBAM + ASFF-YOLOXs: An improved YOLOXs for guiding agronomic operation based on the identification of key growth stages of lettuce

Abstract

To the optimal time to conduct farming operations in the traditional agricultural production process mainly depends on human observation and planting experience, which is time-consuming and laborious, and makes it easy to miss the best agricultural operation opportunities. In this study, our main objective is to accurately detect the key growth stages of lettuce to guide the timely implementation of corresponding agricultural operations. Firstly, the dataset was collected for the growth stage with important agricultural operations in the growth process of multi-variety lettuce, to lay the data foundation for the construction of the model. Secondly, considering the difference in plant growth, we compared many methods and selected the optimal modeling method YOLOXs to identify the key growth stages of multi-variety lettuce (mAP = 98.75 %). Finally, to ensure the applicability of the detection model in complex agricultural scenes, we tried to improve the effect of YOLOXs by three attention mechanisms and one multi-scale feature fusion method, and proposed a new method CBAM + ASFF-YOLOXs (mAP = 99.04 %). The results showed that this method is expected to replace human eye observation and experience in planting, to provide accurate technical feedback on relevant agricultural operation time, and to provide technical support for the unmanned operation of agriculture. At the same time, the limitations, challenges, and prospects of this method are discussed.

Plant phenotyping relevance

レタスの生育段階という植物状態を画像認識で抽出する手法を開発・改良し、データセットと精度評価も行っており、農作業への応用だけでなく表現型取得手法自体が中心である。

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