Predictive Analysis of Monitoring System to Enhance Crop Yield using LSTM Deep Learning Technique

Abstract

Farming is the back bone of agriculture where there is lot of challenges faced by farmers. When agriculture deals with the various steps as per methodologies for increasing productivity such as testing the soil type, seed sowing, irrigation and so on, the process takes a several attention to regularize the growth in better manner such as development of planting, protection for fertilizing etc. A prediction system utilizing a deep learning technique is proposed with multilayer perceptron (MLP) for classification prediction and also from observations of soil type to increase the dimensionality. The benefit of using the deep learning technique Long-short term memory (LSTM) is to maintain the observation during the rate of learning which has the mapping relationship to avoid the indirect mapping of features during sequence connection from agriculture dataset. Based on the interrelationship between the previous knowledge that has trained from the aspects of soil quality, the system predicts the plan propagation. The accuracy level is compared with the CNN technique where the natural growing of crops is increased based on the planting after the germination. LSTM extracts features, as number of values can be mentioned for input and avoids single step classification during the training of data.