Identification of Fungi infected Leaf Diseases using Deep Learning Techniques
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Abstract
This paper presents CNN based model to identify fungi-affected leaf diseases of Psidium guajava. Identifying these leaf diseases at an early stage will help the farmers take significant precautions and prevent the disease from spread to other parts of the plant and the neighbouring plants. The dataset is collected from real environment of the farms. It has four categories of infected leaves (3971 in number) and they are Pseudocercospora leaf spot, Rust, Insect eaten leaf, and another category is of the healthy leaf of Guava (Psidium guajava). Then, applied CNN, AlexNet and SqueezeNet architectures to identify 3971 fungi infected leaves of Guava. SqueezeNet architecture shown 75.9% recognition accuracy as compared to the other two architectures.
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