Analysis of Cotton Leaf Curl Diseases Using Advanced Learning Model
Keywords:
Diseases, Deep Learning, Agriculture, Crop YieldingAbstract
Plant diseases primarily affect society and farmers. Cotton plant diseases have become more complex for cotton farmers and may cause massive damage. Identifying cotton plant diseases in the early stages dramatically impacts the prevention of conditions for plants. Based on living organisms, various diseases cause damage to cotton plants or crops; the other name for this is pathogens when they infect plants. Many state-of-art algorithms failed to detect and recognize cotton plant diseases in the early stages. The most widely used deep learning (DL) domain to find accurate patterns belongs to cotton plant diseases, including bacterial blight, Bacteria, Phytoplasmas, Viruses, leaf curl, and viroids. This paper describes a advanced Learning model to find the specific leaf curl disease or illness in the early stages—ResNet50 is used as a training model to train the cotton plant disease datasets. Image filters remove noise from the input images to improve the disease detection rate. The dataset is the Cotton Dataset collected from the UCI repository. The comparison between the traditional algorithms and ADL is shown in this paper and analyzes the performance.
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