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Deep neural networks (DNNs) have made substantial progress in the categorization of many plant diseases. Plant diseases are the main factor associated with agricultural risk and crop yield. Farming faces difficulties with plant disease due to is not well identified and managed. There has a negative influence on both the amount and the excellence of agricultural products. The percentage of sick plant leaves is displayed by the model once it has been trained. Consequently, the early detection of the plant disease had improved crop quality and reduced production-related harm. A modern come close to has been developed by mounting a method that considers various kinds of images and image capturing. Among these methods is the DNN model, where is employed to ascertain the model’s reliability, precision, recall, and F1 scaling. The diversity of the result indicates the health or bacterial status of the plant leaf. The suggested approach is used on the leaf surfaces of two distinct plants: pepper and potato plants.

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Deep Neural Networks for the Classification and Recognition of Plant Leaf Diseases

  • 会议论文

  • 发表日期：

    2025年

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  • E. Krishnaveni,
  • D. Tulasiram,
  • Sandhyarani,
  • Mahesh Kotha,
  • M. Naveen Kumar,
  • T. Sarika

摘要

Deep neural networks (DNNs) have made substantial progress in the categorization of many plant diseases. Plant diseases are the main factor associated with agricultural risk and crop yield. Farming faces difficulties with plant disease due to is not well identified and managed. There has a negative influence on both the amount and the excellence of agricultural products. The percentage of sick plant leaves is displayed by the model once it has been trained. Consequently, the early detection of the plant disease had improved crop quality and reduced production-related harm. A modern come close to has been developed by mounting a method that considers various kinds of images and image capturing. Among these methods is the DNN model, where is employed to ascertain the model’s reliability, precision, recall, and F1 scaling. The diversity of the result indicates the health or bacterial status of the plant leaf. The suggested approach is used on the leaf surfaces of two distinct plants: pepper and potato plants.