A solanaceae disease recognition model based on SE-Inception

Abstract

Aiming at the diseases of tomato and eggplant, we present a solanaceae disease recognition model based on SE-Inception. Our model uses batch normalization layer (BN) to accelerate network convergence. Besides, SE-Inception structure and multi-scale feature extraction module is adopted to improve accuracy of this model. Our sample data set consists of 4 disease categories including whitefly, powdery mildew, yellow smut, cotton blight. We also add healthy leaves into it. In order to reduce overfitting, the data set is expanded by the data enhancement method of translation, rotation and flip. Experiments show that the average recognition accuracy of this model is 98.29% and the model size is 14.68 MB on our constructed dataset. In addition, in order to verify the robustness of this model, it was also verified on the public data set of PlantVillage, and the top-1, top-5 accuracy and the size of our proposed model is 99.27%, 99.99% and 14.8 MB respectively. Moreover, we implemented a solanaceae disease image recognition system using this model based on the Android. The accuracy of average recognition and the recognition time of a single photo are 95.09% and 227 ms, respectively. Our constructed model has a small number of parameters with maintaining high accuracy, which can meet the needs of automatic recognition of disease images on mobile devices. Data and code are available at https://github.com/Jujube-sun/diseaseRecognition.

Plant phenotyping relevance

トマト・ナスの病害画像から植物の病気状態を推定する認識モデルを開発し、複数データセットで精度検証しているため、植物フェノタイピング手法が中心である。

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