Machine Learning Based Weed Crop Classification Using Raspberry PI

Machine Learning Based Weed Crop Classification Using Raspberry PI

Shruthi V, Chandana G S, Rakshitha H N , Sanjay D, Manoj Kumar A

CHAPTER 1

Introduction

Weed-crop classification using machine learning is an emerging area of research in precision agriculture that aims to automate the process of identifying and distinguishing weeds from crops in agricultural fields. Weeds compete with crops for essential resources such as sunlight, water, and nutrients, ultimately reducing crop yield and quality. Traditional weed control methods, including manual weeding and blanket application of herbicides, are often labor-intensive, time-consuming, and environmentally harmful. To address these challenges, machine learning techniques, particularly supervised learning algorithms, have been increasingly applied to develop intelligent systems capable of performing accurate and efficient weed detection and classification.

          In this approach, machine learning models are trained on labeled datasets containing images of both crops and weeds. These models learn to extract relevant features and patterns that differentiate one plant type from another. Convolutional Neural Networks (CNNs), a popular deep learning architecture, are particularly effective in handling image-based classification tasks due to their ability to automatically learn hierarchical features from raw image data. Once trained, these models can be deployed on embedded systems or mobile platforms to perform real-time classification in the field. This enables precision spraying or mechanical removal of weeds without harming the crops, thereby improving agricultural productivity and sustainability.