基于卷积神经网络的多植物疾病诊断方法

从最大能力限制植物的疾病被定义为植物疾病。从农业的角度来看，诊断疾病至关重要，因为疾病通常会限制植物的生产能力。..

A Multi-Plant Disease Diagnosis Method using Convolutional Neural Network

A disease that limits a plant from its maximal capacity is defined as plant disease. From the perspective of agriculture, diagnosing plant disease is crucial, as diseases often limit plants' production capacity.However, manual approaches to recognize plant diseases are often temporal, challenging, and time-consuming. Therefore, computerized recognition of plant diseases is highly desired in the field of agricultural automation. Due to the recent improvement of computer vision, identifying diseases using leaf images of a particular plant has already been introduced. Nevertheless, the most introduced model can only diagnose diseases of a specific plant. Hence, in this chapter, we investigate an optimal plant disease identification model combining the diagnosis of multiple plants. Despite relying on multi-class classification, the model inherits a multilabel classification method to identify the plant and the type of disease in parallel. For the experiment and evaluation, we collected data from various online sources that included leaf images of six plants, including tomato, potato, rice, corn, grape, and apple. In our investigation, we implement numerous popular convolutional neural network (CNN) architectures. The experimental results validate that the Xception and DenseNet architectures perform better in multi-label plant disease classification tasks. Through architectural investigation, we imply that skip connections, spatial convolutions, and shorter hidden layer connectivity cause better results in plant disease classification.