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The role of agricultural growth in the country’s economy is very important. However, the presence of many plant diseases poses a major obstacle to the growth rate and quality of the crop. This study focuses on mango plant leave diseases. Accurate detection and classification of mango plant leaves disease is a challenging and time-consuming task due to small differences data in example input. Also changes in size, location and shape plants were partially diseased and there is noise and negative effects in the input images. The collected database is accurately classified which includes mango plant species as the training data and predict the output with possible accuracy. Convolutional Neural Network (CNN) model based EfficientNetV2 is used in this study which consists of several layers used in the prediction process. Extracted features from the mango leaves drone images are evaluated and their incorporation in EfficientNetV2 model is the key findings of this paper. This proposed model outperforms than other techniques in problems related to classification and detection of mango plant diseases. It can handle complex challenges under harsh imaging conditions.

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EfficientNetV2 Based Approach for Disease Detection in Mango Plant Leaves Using Drone and Camera Images

  • 会议论文

  • 发表日期：

    2026年

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  • Pankaj Pratap Singh,
  • Devanshu Kumar,
  • Aakriti Srivastava,
  • Madhusmita Basumatary,
  • Shitala Prasad

摘要

The role of agricultural growth in the country’s economy is very important. However, the presence of many plant diseases poses a major obstacle to the growth rate and quality of the crop. This study focuses on mango plant leave diseases. Accurate detection and classification of mango plant leaves disease is a challenging and time-consuming task due to small differences data in example input. Also changes in size, location and shape plants were partially diseased and there is noise and negative effects in the input images. The collected database is accurately classified which includes mango plant species as the training data and predict the output with possible accuracy. Convolutional Neural Network (CNN) model based EfficientNetV2 is used in this study which consists of several layers used in the prediction process. Extracted features from the mango leaves drone images are evaluated and their incorporation in EfficientNetV2 model is the key findings of this paper. This proposed model outperforms than other techniques in problems related to classification and detection of mango plant diseases. It can handle complex challenges under harsh imaging conditions.