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We witness numerous farmer suicides in our daily lives as a result of crop damage and low yields. Although a variety of factors influence the farming process, plant diseases and yield estimation are some of the main issues. Timely treatment of plant diseases is crucial in order to prevent their spread across the entire crop. Manual techniques are more expensive and time-consuming. For this, an automated system for identifying plant diseases is required. Several types of plant leaf images are captured from different places to build the model. Deep learning technique is the most effective solution to this which is the scanning of images and identifying the disease. CNN is used, which is composed of several layers that help in analyzing plant leaf images and are used to create a model for disease identification. With the help of CNN, we have achieved an accuracy of 93%, which is greater compared to other algorithms like SVM and random forest. For yield estimation, we have used a random forest algorithm trained on previous years data to predict the yield of a specific crop in that year.

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Enhancing Crop Health and Yield Estimation Using Deep Learning Techniques

  • 会议论文

  • 发表日期：

    2025年

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  • Priya N. Parkhi,
  • Rohit Pillai,
  • Ayush Chandak,
  • Priyanshu Sharma,
  • Aatishsingh Kachhaway

摘要

We witness numerous farmer suicides in our daily lives as a result of crop damage and low yields. Although a variety of factors influence the farming process, plant diseases and yield estimation are some of the main issues. Timely treatment of plant diseases is crucial in order to prevent their spread across the entire crop. Manual techniques are more expensive and time-consuming. For this, an automated system for identifying plant diseases is required. Several types of plant leaf images are captured from different places to build the model. Deep learning technique is the most effective solution to this which is the scanning of images and identifying the disease. CNN is used, which is composed of several layers that help in analyzing plant leaf images and are used to create a model for disease identification. With the help of CNN, we have achieved an accuracy of 93%, which is greater compared to other algorithms like SVM and random forest. For yield estimation, we have used a random forest algorithm trained on previous years data to predict the yield of a specific crop in that year.