Abstract
The major concern in Pakistani agriculture is the reduction of growing weed. This research aims to provide a weed detection tool for future agri-robots. The weed detection tool incorporates the use of machine-learning procedure explicitly implementing Support Vector Machines (SVMs) and blob analysis for the effective classification of crop and weed. Weed revealing is based on characteristic features i.e. red green blue (RGB) components which differentiate soil and plant. Morphological features—centroidand length aid to distinguish shape of crop and weed leaves. Following feature extraction, the positive and negative margins are separated by a hyper-plane. The separating hyper-plane acts as the decision surface. Sample input consists of multiple digital field images of carrot crops. Training samples of seventy two images are taken. Accuracy of the outcomes discloses that SVM and blob analysis attain above 50-95% accuracy.
Highlights
One of the major concerns of an economy is accelerating agricultural development
Agriculture is a vital sector of the economy of Pakistan and recently accounted for a 5.28% growth according to government estimates [1]
Taking photographs can be done by attaching a camera in the front of the tractor/machine or manually
Summary
Weeds are unwanted plants that can survive and reproduce in agricultural fields. They hinder agricultural development by disturbing production and quality through competing with crops for water, light, soil nutrients, and space. Agriculture is a vital sector of the economy of Pakistan and recently accounted for a 5.28% growth according to government estimates [1]. Up to 60% of yield is dependent on soil fertility, and weeds that rob nutrients from crops limit yield potential [2]. Removal of weed from cultivated fields during early stages of crop growth is essential for a healthy and good harvest. For weed detection we need field photographs with good precision. By applying image processing techniques to that image by using MATLAB we can detect the weed in the field. Our main aim is detection and removal of the weed using image processing
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