An Artificial Intelligence Framework for Multi-Class Identification of Rice Leaf and Crop Diseases
Agriculture plays a great role in ensuring food security in the world but there are serious threats in rice production due to different types of bacterial and fungal diseases. Manual diagnosis is subjective and labor intensive and may be delayed. The proposed paper suggests a multi-class identification of ten different pathologies related to rice leaves through a multi-class Artificial Intelligence framework using the dataset of 15,023 images. Three methodological paradigms that use mobile net version two with XGBoost, a custom Convolutional Neural Network (CNN), and a novel Hybrid CNN-LSTM model that views patterns of diseases as feature sequences are benchmarked. Whereas the XGBoost classifier has the highest accuracy of 91.75 percent, the deep learning models have high feature extraction capacity. The proposed Ensemble model that incorporates both spatial and sequential learning achieves 97 percent accuracy. This paper supports the hypothesis that deep learning hybrid models have a great impact on diagnostic accuracy, which can be successfully used as an automated instrument to provide disease control in precision agriculture.