Crop Weed Identification System Based On Convolutional Neural Network

Abstract

Crop and weed classification is a critical task in 
precision agriculture, aiding in the efficient 
management of crops and reducing herbicide use. 
Traditional methods of classification rely heavily on 
manual labor and are often time-consuming and 
subjective. In recent years, deep learning neural 
networks have emerged as powerful tools for 
automating classification tasks in various domains. In 
this study, we explore the application of deep learning 
neural networks for crop and weed classification 
using image data. 
We propose a novel approach that leverages 
convolutional 
neural 
networks 
(CNNs) to 
automatically extract features from images of crops 
and weeds. We train the CNN on a large dataset of 
annotated images, enabling it to learn discriminative 
features that distinguish between different crop and 
weed species. Additionally, we employ data 
augmentation techniques to enhance the model's 
generalization 
capabilities 
performance on unseen data. 
and improve its 
Experimental results demonstrate the effectiveness of 
our approach in accurately classifying crops and 
weeds across different environmental conditions and 
growth stages. The proposed deep learning-based 
system offers several advantages over traditional 
methods, including scalability, adaptability, and 
automation. Moreover, it has the potential to significantly reduce the time and resources required for crop and weed classification, thereby facilitating 
more sustainable and environmentally friendly 
agricultural practice