Black rot disease classification of apples and grapes using convolutional neural network and transfer learning

Collecting a dataset for a specific plant disease is a very complex, costly, and time-consuming process, involving the use of expensive equipment and monitoring the evolution of the disease on a plant-by-plant basis over several crop cycles. Overall, data collection will be simpler if we can form a classifier disease model for a single plant and then use it for other plants that may have the same disease and the same symptoms. The objective of this research is to explore the feasibility of creating and training a CNN-based classifier using a dataset comprising cases of black rot disease in a specific plant species. The ultimate goal is to generalize the application of this classifier to other plant species suffering from the same disease and displaying similar symptoms. The proposed work consists of a convolutional neural network (CNN) binary-based classification of black rot disease applied to apples and grapes. The employed methodology is based on training and testing six types of CNN models, namely efficientnet_v2, inception_resnet_v2, inceptionV3, mobilenet_v2, VGG-16, and our proposed CNN model for the purpose of black rot disease detection. We divided the experiment into 3 main approaches, providing respective accuracies of 100%, 97.43%, and 93.16%. The obtained results show that the CNN-based architectures allow the classification of apple and grape black rot leaf disease with high accuracy. The proposed approach is promising and encouraging in the context of developing CNN models capable of automatically generalizing learning data relating to a specific plant disease to all plants likely to be infected by that same disease. This approach will enable researchers involved in the early detection of plant diseases to speed up their work by optimizing computing resources during the model-learning phase and collecting a disease-specific database for a single plant as quickly as possible.