To prevent the loss of the yield of food crops and to attain sustainable agricultural growth, accurate detection of plant disease at an early stage is crucial. However, the extraction of crucial features from infected plant leaves to differentiate the properties associated with different diseases is a complex task, as the diseases exhibit huge variations, which insists on the need for developing precise disease detection. Hence in this research, the early detection of plant disease is performed by utilizing a Modified political optimization adapted deep Neural Network (MPO-adapted deep NN) model, in which the continuous learning capability of the deep NN classifier helps in the deeper analysis of the information in the image and identifies the plant disease more accurately. Identification of the plant disease posse’s challenges due to complexities present in the image and the neural networks effectively dwells with the complex relationships and the non-linear characteristics of the network help in achieving adaptability and makes the system more suitable for real-time applications. The main contribution relies on the modified political optimization algorithm that efficiently tunes the parameters of the deep NN classifier to analyze the disease patterns effectively and provides disease detection with high accuracy. Further, the Adaptive K-means algorithm is utilized for the effective segmentation of diseased parts, and the Grey level co-occurrence matrix (GLCM) features are extracted in the method that enhances the accuracy of the detection. When compared to the existing techniques, the MPO-adapted deep NN model attains high accuracy, sensitivity, and specificity values of 98.95%, 97.45%, and 98.95% for cotton leaf, 94.47%, 94.58%, 94.54% for cotton root, 99.10%, 99.10%, 99.10% for cotton stem, respectively concerning the k-fold. Analysis demonstrating the superiority of the research's metrics values measurement. When compared to existing methods, detecting the disease in cotton stems is very effective.