Paper Key : IRJ************888
Author: Nikhil Bare,Rohan Doifode,Godavari Kadam,Bhakti Shirsat,Prof. Mayuri Sutar
Date Published: 04 Nov 2024
Abstract
Abstract Weed detection using deep learning is a cutting-edge application of artificial intelligence in agriculture. This technology offers a sophisticated solution to the age-old problem of weed management, aiming to revolutionize farming practices worldwide. The process begins with the acquisition of image data depicting agricultural fields, captured through various means. These images serve as the raw material for training the CNN model, providing a rich source of information about the crops and the surrounding environment, including the presence of weeds. The essence of CNNs lies in their ability to automatically learn and extract intricate patterns and features from images. Through multiple layers of convolution and pooling, these neural networks transform raw pixel data into meaningful representations, enabling them to discern subtle differences between crops and weeds. Training a CNN model for weed detection involves a complex interplay of data preprocessing, model architecture selection, and optimization. The dataset is carefully curated, normalized, and augmented to ensure diversity and robustness.Key Words: weed detection, Image processing, deep learning; CNN.
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