Pure mathematics

Biology

Transfer of learning

Field (mathematics)

Contextual image classification

Leaf spot

Computer science

Pattern recognition (psychology)

Mathematics

Image (mathematics)

Agronomy

Artificial intelligence

Cercospora

Medicine

Deep learning

Artificial neural network

Feature extraction

Disease

Convolutional neural network

Machine learning

Pathology

Convolution (computer science)

Every year, the crop output of India is significantly affected due to delayed detection of diseases in crops. This research is a contribution to the farmers in their battle against coffee plants diseases. It will help in timely detection of diseases, resulting in increased coffee production output of India. Many coffee plant diseases like Leaf Rust, Cercospora Spots have clear visual symptoms and thus can be extracted and their classification can be done. Convolutional Neural Networks (CNNs) has proved its efficiency and accuracy in the field of image classification and pattern recognition. Hence it can act as a powerful tool in the diagnosis of coffee leaves diseases since these symptoms have clearly distinguishable patterns. Thus, a Convolution Neural Network model is proposed which utilizes the technique of Transfer Learning, reducing the training time taken by the model significantly. Further, to achieve a higher success rate, Data Augmentation technique is applied to enlarge the dataset used to train the network. The proposed model has achieved a high accuracy of 97.61%.

Disease Detection in Coffee Plants Using Convolutional Neural Network