Comparison of mango leaf diseases identification using different convolutional neural network layers

Aim: The main aim of this paper is to recognize mango leaf disease using convolutional neural networks in google co-laboratory. Materials and Methods: A dataset of 120 photographs of diseased and normal mango leaves was used to distinguish mango leaves using the CNN technique which is based on a deep learning algorithm. Results: The proposed 5D CNN model has a 98.67 % accuracy and 2D CNN has a 89.05% accuracy in leaf disease classification, so the novel 5D Convolution Neural Network algorithm appears to be better than 2D CNN standard deviation is 1. Conclusion: In mango leaf disease detection, the proposed novel 5D Convolution Neural Network based leaf disease recognition model shows less training time and high training accuracy. This demonstrates the adaptability of CNN models in real-time implementations.