Disease detection in crops is one of major task that each farmer practice and takes necessary action for eradicating them as they're harmful to not only crops but also to farmers, consumers, and environment too. Plants are constantly exposure to pathogens such as virus, bacteria and fungi due to the environmental changes such as rainfall, temperature, the crop yield gets affected severely. It is difficult for human eye to detect the exact form of leaf disease which occurs on the leaf of plant. So, to protect the plant from leaf disease and to enhance the agriculture sector, modern technological approaches such as artificial intelligence, machine learning and deep learning algorithm have been utilized to increase the recognition rate and the accuracy of the results. Significant steps involved in disease prediction using image processing are image acquisition, data preprocessing, image segmentation; feature extraction and image classification are mentioned. Standard techniques also used in each step of image processing are reviewed along with various detection and classification techniques such as Support Vector Machine (SVM), Convolution Neural Network (CNN), K-Nearest Neighbors (KNN), K-Means Clustering, Deep Learning etc. These new technologies are capable to detect and analyze the accurate plant leaf disease.
₹10000 (INR)
NON IEEE-2022