Convolutional Neural Networks Based Optimal Management of Agricultural Crops

Abstract

Given the importance of agriculture, food supply, and food security, as well as population growth, the use of state-of-the-art technologies to increase agricultural productivity and mechanization with the least amount of loss and damage to crops and human beings has been highly prioritized. A great body of research has been conducted on and many solutions have been adopted for agricultural mechanization and reduced and optimized consumption of the available herbicides. Using convolutional neural networks and deep learning, this study sought to increase the accuracy of detecting grapes in the vineyard and of weeds in fields. For this purpose, the VGG16 Standard was utilized. The results indicated a 99% learning accuracy in the learning section for grape and weed detection. The validation and the final accuracy of detection for the machine designed was 63% for grapes detection and 95% for weeds. It was also demonstrated that the proposed method outperformed the KNN, decision tree, and random forest algorithms compared to the other algorithms and methods.