Abstract

Agricultural sector being the foundation of food supply and raw material production contributes significantly to the GDP growth and value chain. Thus, the effective elimination of weed in modern day agriculture is essential as the current world scenario demands for efficient and resourceful ways for crop cultivation and harvesting. The urgent need for elimination of weeds arises due to their tendency to extricate all the essential minerals and moisture that the crops require for their appropriate growth. The main objective of this study is to successfully acquire live video feed as input, classification into categories of crop, weed and none. Finally, upon detection of weed the spraying mechanism releases a predetermined amount of herbicide upon the weed. A total of 5471 image samples were captured to train the CNN model. The prototype mentioned in this paper uses Convolutional Neural Network (CNN) technique for feature extraction, Fully Connected Layers or Dense Layers (FCLs) for classification using SoftMax as the activation function respectively. The CNN technique achieved an accuracy of 95.50% whereas YOLOv4 achieved 91.00%. Finally, the F1 Score was evaluated to be 96.25% and 91.96% respectively.

Graphical Abstract

Novelty Statement

A robust weed detection and elimination system needed to efficiently boost up the agriculture sector.