Pepper leaf disease recognition based on enhanced lightweight convolutional neural networks

Abstract

Pepper leaf disease identification based on convolutional neural networks (CNNs) is one of the interesting research areas. However, most existing CNN-based pepper leaf disease detection models are suboptimal in terms of accuracy and computing performance. In particular, it is challenging to apply CNNs on embedded portable devices due to a large amount of computation and memory consumption for leaf disease recognition in large fields. Therefore, this paper introduces an enhanced lightweight model based on GoogLeNet architecture. The initial step involves compressing the Inception structure to reduce model parameters, leading to a remarkable enhancement in recognition speed. Furthermore, the network incorporates the spatial pyramid pooling structure to seamlessly integrate local and global features. Subsequently, the proposed improved model has been trained on the real dataset of 9183 images, containing 6 types of pepper diseases. The cross-validation results show that the model accuracy is 97.87%, which is 6% higher than that of GoogLeNet based on Inception-V1 and Inception-V3. The memory requirement of the model is only 10.3 MB, which is reduced by 52.31%-86.69%, comparing to GoogLeNet. We have also compared the model with the existing CNN-based models including AlexNet, ResNet-50 and MobileNet-V2. The result shows that the average inference time of the proposed model decreases by 61.49%, 41.78% and 23.81%, respectively. The results show that the proposed enhanced model can significantly improve performance in terms of accuracy and computing efficiency, which has potential to improve productivity in the pepper farming industry.

Plant phenotyping relevance

コショウ葉の病徴を画像から認識するCNNモデルを開発・比較し、精度と計算効率を検証しているため、植物病害状態の画像ベースフェノタイピング手法が中心です。

Code and data availability

The paper describes a self-collected pepper leaf disease dataset (9,183 images from Xijiang Agro-ecological Park) and a GoogLeNet-EL model, but no supplied block contains any data availability statement, public repository deposit, or author-provided URL for the dataset, images, code, or trained model. No qualifying,loc

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