The Distributed Deep Learning Paradigms for Detection of Weeds from Crops in Indian Agricultural Farms

Abstract

Weeds are a major threat to crops, making early detection critical for maintaining agricultural productivity. Weeds are generally toxic, equipped with thorns and burrs, and can disrupt crop management by contaminating harvests. This research aims to identify weeds in a field using image processing and deep learning techniques. Images were collected from an Indian farm and pre-processed using image processing techniques. The images were then analysed to extract features that distinguish between weed and crop properties. Traditional crop weed identification methods mainly focused on identifying weeds directly but weed species can vary significantly. This study proposes a method that combines deep learning and image processing technology. Identifying weeds in crops is a challenging task that has been addressed through image processing, feature extraction, and image labelling to train deep learning algorithms. The study examines the performance of various deep learning algorithms and convolution neural networks to detect weeds using images obtained from an Indian crop field. Once the input image is identified as a weed or not, the crop class prediction is made. These results could have significant implications for optimizing agricultural fertilizer usage, leading to increased crop yields and less environmental impact.