MFC-CNN: An automatic grading scheme for light stress levels of lettuce (Lactuca sativa L.) leaves

Abstract

The identification and control of light stress is the key to the high-yield and high-quality production of leafy vegetables in a controlled environment. As a widely grown vegetable in the plant factory, lettuce is responsive to light intensity. Strong or weak light will seriously affect its yield and quality. These differences can be articulated by images and then classified by fine-grained classification methods. Deep learning is commonly used in crop image classification due to its fast and convenient advantages, but conventional fine-grained recognition approaches are still extremely challenging in dealing with this kind of inter-species classification. To address this issue, this work takes lettuce as the research object and established a set of leaf images for lettuce light stress grading. The leaves were divided into four categories depending on the changes in shoot fresh weight. Then, a hierarchical fusion convolutional neural network architecture (MFC-CNN) based on multi-scale input was constructed to grade the light stress. We firstly separate leaf patches from complete leaf and construct four-scale input to expand local leaf vein and texture information. The multi-scale dataset is fed into the main network at different depths according to the characteristics of CNN in feature extraction. Finally, the network is tested through comparative experiments. The results show that the proposed model has obvious advantages in light stress dataset and generalized dataset.

Plant phenotyping relevance

レタス葉画像から光ストレスレベルを推定するCNNとマルチスケールデータセットを開発・比較評価しており、植物状態の取得・判定手法が研究の中心である。

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