Weed Classification for Site-Specific Weed Management Using an Automated Stereo Computer-Vision Machine-Learning System in Rice Fields

Abstract

Site-specific weed management and selective application of herbicides as eco-friendly techniques are still challenging tasks to perform, especially for densely cultivated crops, such as rice. This study is aimed at developing a stereo vision system for distinguishing between rice plants and weeds and further discriminating two types of weeds in a rice field by using artificial neural networks (ANNs) and two metaheuristic algorithms. For this purpose, stereo videos were recorded across the rice field and different channels were extracted and decomposed into the constituent frames. Next, upon pre-processing and segmentation of the frames, green plants were extracted out of the background. For accurate discrimination of the rice and weeds, a total of 302 color, shape, and texture features were identified. Two metaheuristic algorithms, namely particle swarm optimization (PSO) and the bee algorithm (BA), were used to optimize the neural network for selecting the most effective features and classifying different types of weeds, respectively. Comparing the proposed classification method with the K-nearest neighbors (KNN) classifier, it was found that the proposed ANN-BA classifier reached accuracies of 88.74% and 87.96% for right and left channels, respectively, over the test set. Taking into account either the arithmetic or the geometric means as the basis, the accuracies were increased up to 92.02% and 90.7%, respectively, over the test set. On the other hand, the KNN suffered from more cases of misclassification, as compared to the proposed ANN-BA classifier, generating an overall accuracy of 76.62% and 85.59% for the classification of the right and left channel data, respectively, and 85.84% and 84.07% for the arithmetic and geometric mean values, respectively.

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

“…It is predicted that the global population will reach nine billion by 2050, and therefore, agricultural production must double to meet the increasing demands [1]. However, agriculture is facing immense challenges from the growing threats of plant diseases, pests and weed infestation [2][3][4][5] . The weed infestations, pests and diseases reduce the yield and quality of food, fibre and biofuel value of crops.…”

“…A low-cost tool for identification and mapping of weeds at early growth stages will contribute to more effective, sustainable weed management approaches. Along with preventing the loss of crop yield by up to 34%, early weed control is also useful in reducing the occurrence of diseases and pests in crops [2,7]. Many approaches have been developed for managing weeds, and they normally consider current environmental factors.…”

Input Use Efficiency Management for Paddy Production Systems in India: A Machine Learning Approach

“…Various studies use different kinds of datasets, from satellite to multispectral images and generic field observation to extract information for smart agriculture applications. There are important studies for soil fertility prediction [20], soil moisture [21][22][23], clay prediction by portable multispectral cameras [24], prediction for the condition of indoor plants through partial least squares [25], disease detection [26][27][28], and weed detection [29,30]. These models use an array of machine learning algorithms, including artificial neural networks (ANN), SVM, RF, KNN, multiple linear regression (MLR), etc., for various crops to predict, including ANN, SVM, RF, KNN, and MLR, etc., for various crops to predict their yield.…”

A Review of Applications and Communication Technologies for Internet of Things (IoT) and Unmanned Aerial Vehicle (UAV) Based Sustainable Smart Farming

“…Hence, low cost smart tools for identification and mapping of weeds at early growth stages will contribute to more effective, sustainable weed management approaches. Existing studies [26,96,97] have shown some approaches to detect weeds using UAV images; however, they only could achieve less than 90% of accuracy, hence more accurate weed detection approaches are desirable. The authors in [4] presented a shielded band sprayer to spray herbicides in weed, avoiding to spray on crops, hence increase the food quality and reduce the use of plant protection products.…”