Revolutionizing Crop Disease Detection: Harnessing Ensemble Learning and Computer Vision for Enhanced Accuracy

Crop diseases also remain a major threat to food security around the world. Pathogens are the main cause of for different crop diseases. However, rapid identification of the pathogens remains difficult in many developing world and countries such as Ethiopia due to a lack of technologies and infrastructure. In this research work, we proposed ensemble-based early crop disease detection model using computer vision. We have employed different pre-trained models like Inceptionv3, Resnet50, and VGG19 as a base model to conduct the experiments. More than 23,000 crop image datasets collected from different sources to train our model. The models are trained to classify the dataset into their respective categories. The selected base model is further combined to build ensemble learning model. Our main objective is to build more generable and robust machine learning model that can efficiently classify crop diseases. We have considered model explainability, scalability and drifting issues to improve the generalizability capability of the proposed model. From the experimental results, the ensemble-based learning model classified the training and validation with 96.14 %, and 96.85% accuracy respectively.