Retracted: Classification of Pepper Bell into Healthy and Bacterial Spot Using Deep Learning
Praveen Kumar Mannepalli, Ayesha Khan, Priya Chugh, Sadia Patka, R. Ponmalar
Abstract
This study makes a strong case for dividing bell pepper leaves into two broad groups: those that are healthy and those plagued by disease centers. The system uses advanced machine learning techniques to achieve a classification accuracy of over 95 %. The importance of correct classification in agriculture cannot be denied because it plays an important role in early detection of diseases, preventing losses and ensuring product safety. Bacterial diseases pose a threat to pepper crops, so accurate identification is important for effective disease control. Our approach requires the use of deep learning models to classify bell peppers. The model adapts to different situations using different data, including both healthy and disease-affected leaves. Classification accuracy above 95 % demonstrates the effectiveness of our method. This high level of accuracy is required to develop disease on real farms. This advance allows farmers and experts to quickly implement intervention plans that protect crops and all the benefits.