Early Weed Detection Using Image Processing and Machine Learning Techniques in an Australian Chilli Farm

Nahina Islam,Cheng-Yuan Xu,Md Mamunur Rashid,Ahsan Morshed,Sk Mostafizur Rahman,Santoso Wibowo,Saleh A Wasimi,Steven Moore

doi:10.3390/agriculture11050387

Nahina Islam, Cheng-Yuan Xu + Show 6 more

Open Access

https://doi.org/10.3390/agriculture11050387

Copy DOI

Abstract

This paper explores the potential of machine learning algorithms for weed and crop classification from UAV images. The identification of weeds in crops is a challenging task that has been addressed through orthomosaicing of images, feature extraction and labelling of images to train machine learning algorithms. In this paper, the performances of several machine learning algorithms, random forest (RF), support vector machine (SVM) and k-nearest neighbours (KNN), are analysed to detect weeds using UAV images collected from a chilli crop field located in Australia. The evaluation metrics used in the comparison of performance were accuracy, precision, recall, false positive rate and kappa coefficient. MATLAB is used for simulating the machine learning algorithms; and the achieved weed detection accuracies are 96% using RF, 94% using SVM and 63% using KNN. Based on this study, RF and SVM algorithms are efficient and practical to use, and can be implemented easily for detecting weed from UAV images.

Highlights

The rapid growth of global population compounded by climate change is putting enormous pressure on the agricultural sector to increase the quality and quantity of food production
Weed detection is crucial in agricultural productivity, as weeds act as a pest to crops
The unmanned aerial vehicles (UAVs) images were collected from an Australian chilli farm, and these images were pre-processed using image processing techniques

Summary

Introduction

The rapid growth of global population compounded by climate change is putting enormous pressure on the agricultural sector to increase the quality and quantity of food production. The weed infestations, pests and diseases reduce the yield and quality of food, fibre and biofuel value of crops. Weeds are undesired plants which compete against productive crops for space, light, water and soil nutrients, and propagate themselves either through seeding or rhizomes. They are generally poisonous, produce thorns and burrs and hamper crop management by contaminating crop harvests. Weed control is an important aspect of horticultural crop management, as failure to adequately control weeds leads to reduced yields and product quality [6]. Along with preventing the loss of crop yield by up to 34%, early weed control is useful in reducing the occurrence of diseases and pests in crops [2,7]. This is because a standard data collection and classification require significant amounts of manual effort for segment size tuning, feature selection and rule-based classifier design

Objectives

Methods

Findings

Conclusion