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Cotton trees are tropical and subtropical trees that flourish in warm climates. It is a popular, costly as well as a cash crop. Farmers face difficulties selling their products when their production is decreased due to diseases harming cotton plants. It is critical to manage any dangerous illnesses as soon as feasible in order to increase quality and production. This problem prompted the development of novel technologies for detecting and diagnosing cotton plant diseases, as well as expert systems for disease prevention. The use of computer vision in agriculture has grown in popularity as a result of its ability to give critical information in real time. To achieve our goal, we built a dataset that included 4 classes of diseased and leaf images. We have applied InceptionV3, DenseNet, and MobileNetV2 to our dataset. By using DenseNet, we achieve the best accuracy of 99.24%. Our system with the capability of identifying leaf diseases of cotton has been built. As a result, we believe that our initiative will help the user by allowing us to make more items, which will impact our economy.

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Deploying DenseNet for Cotton Leaf Disease Detection on Deep Learning

  • 会议论文

  • 发表日期：

    2025年

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  • Md. Basitur Rahman Bappi,
  • S. M. Masfequier Rahman Swapno,
  • M. M. Fazle Rabbi

摘要

Cotton trees are tropical and subtropical trees that flourish in warm climates. It is a popular, costly as well as a cash crop. Farmers face difficulties selling their products when their production is decreased due to diseases harming cotton plants. It is critical to manage any dangerous illnesses as soon as feasible in order to increase quality and production. This problem prompted the development of novel technologies for detecting and diagnosing cotton plant diseases, as well as expert systems for disease prevention. The use of computer vision in agriculture has grown in popularity as a result of its ability to give critical information in real time. To achieve our goal, we built a dataset that included 4 classes of diseased and leaf images. We have applied InceptionV3, DenseNet, and MobileNetV2 to our dataset. By using DenseNet, we achieve the best accuracy of 99.24%. Our system with the capability of identifying leaf diseases of cotton has been built. As a result, we believe that our initiative will help the user by allowing us to make more items, which will impact our economy.