AgroVisionNet Learning Deep Patterns for Resilient Crop Health Analysis
DOI:
https://doi.org/10.15662/IJEETR.2026.0802100Keywords:
Deep Learning, EfficientNet, Plant Disease Detection, Multimodal Learning, Precision Agriculture, Feature Fusion, Environmental Data Integration, Crop Health MonitoringAbstract
Agriculture remains one of the most important sectors for global food production, but crop diseases continue to cause major losses in yield. Traditional ways of detecting diseases depend largely on manual inspection, which is slow and can lead to mistakes. In recent years, deep learning has shown great promise in automating the detection of plant diseases using image analysis. However, most existing methods focus only on visual cues and do not consider environmental factors that significantly affect disease spread. This study introduces AgroVisionNet, an improved deep learning framework that combines image-based disease detection with environmental data like temperature and humidity. The system uses the EfficientNet architecture to extract detailed features from leaf images and then merges these features with climate data through a feature fusion process. This combination allows for more accurate and context-aware disease prediction. The model was trained and tested using a publicly available plant disease dataset. The results show that AgroVisionNet performs better than traditional convolutional neural network models, offering higher accuracy and better adaptability. In addition to identifying diseases, the system also provides preventive advice, which makes it useful for real-world farming. This approach helps in making agriculture more intelligent and sustainable
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