Plant Disease Identification Based on Vision for Autonomous Crop Management Systems

• Emanuelle S. Gil UFAM
• Lucas M. A. Dias UFAM
• Alternei S. Brito UFAM
• Felipe G. Oliveira UFAM

DOI: https://doi.org/10.5753/connect.2025.12342

Abstract

The integration of robotics in agriculture is revolutionizing traditional practices to enable intelligent plant health monitoring and disease detection. This work proposes a new approach for plant disease identification, using the ConvNeXt deep learning model to extract and classify plant visual features across species and disease types. Two datasets were used: Plant Village and Plant Pathology 2020. The proposed method achieved high accuracy, with 99.47% in Plant Village and 93.83% in Plant Pathology 2020, outperforming comparative approaches. The model was shown to be robust and scalable to independent robots, supporting more sustainable agriculture.

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