Precision Weed Identification In Groundnut Crops Using Image Processing Techniques

Abstract

Weed Infestation Is One Of The Major Problems Affecting Crop Growth And Reducing Agricultural Productivity In Groundnut Cultivation. Early Identification And Management Of Weeds Are Essential To Improve Crop Yield. In This Study, An Image-based System Was Developed To Identify Weeds In Groundnut Fields. Images Of Groundnut Crops And Weeds Were Collected Using A Mobile Camera Under Natural Field Conditions. The Collected Images Were Pre-processed Using Resizing And Enhancement Techniques To Improve Image Quality. A Web-based Application Was Developed To Make The System Easy To Use. The Application Allows Users To Upload Field Images And Automatically Analyzes Them To Identify Whether The Image Contains Crop Plants Or Weeds. A Convolutional Neural Network (CNN) Model Was Used To Classify Crop And Weed Images Accurately. Based On The Detection Results, The System Also Provides Suitable Herbicide Recommendations To Control The Identified Weeds Effectively. The Results Are Displayed Through A Simple And User-friendly Interface. This Developed Web System Helps Farmers Quickly Detect Weeds And Select Appropriate Herbicides, Thereby Supporting Better Decision-making, Reducing Manual Labor, Minimizing Excessive Herbicide Usage, And Improving Weed Management Practices In Groundnut Cultivation.

Keywords