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Given the recent rate of population expansion, it is anticipated that by 2050, global crop productivity will need to double. Getting this production result is significantly hampered by pests and diseases. It is essential to offer efficient methods for automatically diagnosing, classifying, and foretelling diseases and pests in agricultural crops. Machine learning techniques can be applied to the data being worked on to extract information and relationships in order to automate this process. Deep learning techniques are employed in the study, which focuses on the categorization, detection, and pest and disease prediction tasks in the agricultural industry. We seek to advance precision agriculture and smart farming by supporting the development of techniques that will allow farmers to use less pesticides and chemicals while protecting and improving the quality and yield of their crops. We’ll employ image classification method, in which the user uploads a photo of the diseased plant or crop, and the system uses deep learning techniques to identify the disease type. And this disease identification will offer advice on how to treat or avoid that crop. The goal is to create a uniform approach for all features and deliver results for all crops throughout the majority of regions in India that are as accurate as possible.

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Enhancing Multi-crop Disease Prediction Using Convolutional Neural Networks: A Global Perspective

  • 会议论文

  • 发表日期：

    2025年

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  • Nagababu Garigipati,
  • Bathini Nivrithi,
  • Dachireddy ShreshtaReddy,
  • Mummaneni Eesha,
  • Patlavath Nikitha

摘要

Given the recent rate of population expansion, it is anticipated that by 2050, global crop productivity will need to double. Getting this production result is significantly hampered by pests and diseases. It is essential to offer efficient methods for automatically diagnosing, classifying, and foretelling diseases and pests in agricultural crops. Machine learning techniques can be applied to the data being worked on to extract information and relationships in order to automate this process. Deep learning techniques are employed in the study, which focuses on the categorization, detection, and pest and disease prediction tasks in the agricultural industry. We seek to advance precision agriculture and smart farming by supporting the development of techniques that will allow farmers to use less pesticides and chemicals while protecting and improving the quality and yield of their crops. We’ll employ image classification method, in which the user uploads a photo of the diseased plant or crop, and the system uses deep learning techniques to identify the disease type. And this disease identification will offer advice on how to treat or avoid that crop. The goal is to create a uniform approach for all features and deliver results for all crops throughout the majority of regions in India that are as accurate as possible.