Design and Development of Computational Methodologies for Agricultural Informatics

Agriculture informatics is the branch of engineering that combines agriculture engineering with information technology. Agriculture is the backbone of India and the evolution of agricultural technologies is encouraged by using different civilization technologies. Earlier, agriculture has been developed by improving yield, replacing man-made fertilizers, etc. Nowadays, the environmental causes are eliminated therefore macrobiotic and sustainable agricultural activities are improved. The largest agricultural productivity is achieved by predicting techniques that are utilized for protecting agricultural productivity. Plant diseases have become a major issue since they can significantly reduce the quality and quantity of agricultural products. As a result of improper care not being done in this area, plants suffer serious effects that have an impact on the quality, quantity, or productivity of the corresponding product. Consequently, the most important agricultural disease prediction is the one for leaf disease. Different data mining techniques are developed for predicting leaf diseases based on various parameters. A Multi-channel Multimodal Concatenation-based CNN with Long Short-Term Memory (M2C2NN-LSTM) model is suggested in order to accurately forecast both leaf diseases and the associated parameters of the soil, for helping farmers in quickly diagnosing leaf illnesses and soil characteristics, avoid leaf diseases by growing crops in accordance with those characteristics, and increase agricultural output productivity and to improve our nation’s economic situation and prevent yield loss. This research work is focusing on the development of various techniques for the prediction of leaf disease.