Deep Neural Network (DNN) Mechanism for Identification of Diseased and Healthy Plant Leaf Images Using Computer Vision

Identifying and controlling diseases present in plants is very necessary and useful to have healthy growth in plants and to get products of good quality. In this paper, we proposed a novel model to detect whether a plant is diseased or healthy. This model was developed with a deep neural network (DNN) that extracts and evaluates features from plant leaf images. The proposed DNN model is trained on two popular datasets: New Plant Diseases (Augmented) and Rice Leaf, with 38 and 4 classes of plant leaf images, respectively. The model extracts twelve features from a leaf image. They are: total area, infected area, perimeter, x-centroid, y-centroid, mean intensity, equivalent diameter, entropy, eccentricity, energy, homogeneity, and dissimilarity. We observed that considering these many features for evaluation yield good results. The model has exhibited good performance on the two datasets. The model proposed is trained by setting different values for the following parameters: epoch, batch size, activation function, and dropout. When the model was applied to the validation dataset, it showed good performance. After considerable recreation, the proposed model achieved 96% to 99% classification accuracy for certain classes. When compared to traditional machine learning models, the proposed model achieves better accuracy. The proposed model is also tested for consistency and reliability.