An artificial intelligence based approach for increasing agricultural yield

Abstract

Background/Objectives: Identification of a suitable crop based on soil and climatic condition along with plant disease detection are very much essential as they boost the agricultural yield. Monitoring these parameters is not largely carried out as they are very laborious and require expertise in the field, thus curtails the overall productivity. To address these challenges an artificial intelligence based techniques were applied to predict a suitable crop and also to detect the plant leaf disease at an early stage are been presented. Methods/Statistical analysis: Crop predictions are carried out by leveraging two machine learning approaches, Logistic Regression (LR) and Support Vector Machine (SVM) on the agriculture dataset. Convolution Neural Network (CNN) with ResNet152 architecture is used for the detection of plant disease. An open dataset of 54,306 photos of healthy and diseased crops are considered during the performance evaluation. Findings: Crop predictions based on LR and SVM have achieved an accuracy of 93% and 97% respectively across a class of 13 crops. CNN based model predicts disease among 38 different categories from 14 specific crops with an accuracy of 96%. Novelty/Applications: The proposed approach will be beneficial to farmers to identify suitable crops and have plant leaf disease under control.