Real-Time Machine-Learning Based Crop/Weed Detection and Classification for Variable-Rate Spraying in Precision Agriculture

Alam, Mansoor; Alam, Muzaffar; Roman, Muhammad; Tufail, Muhammad; Khan, Muhammad Umer; Khan, Muhammad Tahir

doi:10.1109/iceee49618.2020.9102505

Cited by 86 publications

(48 citation statements)

References 17 publications

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“…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.…”

Section: Introductionmentioning

confidence: 99%

“…This scenario involved a chilli producing farm (AustChilli Group Australia) which is located in Bundaberg, Australia. As found in many studies [3,[9][10][11] in the literature, the quality and quantity of the crop yields were being hampered due to the water and nutrients consumed by weed. Therefore, an effective solution for weed control in early stages of the crops is much needed.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2021

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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.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

et al. 2021

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“…On the other hand, Xu et al [ 55 ] and Alam et al [ 56 ] report that in systems of artificial vision for pulverization at a variable rate, even low precision values can make a contribution to a reduction in the volume of pesticides to be applied. In this situation, considering only the overall accuracy, the algorithms based on support vector machines and closest neighbors would be the most highly indicated for the development of a system for professional use.…”

Section: Resultsmentioning

confidence: 99%

Identification of Cotton Leaf Lesions Using Deep Learning Techniques

Caldeira

Santiago

Teruel

2021

Sensors

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The use of deep learning models to identify lesions on cotton leaves on the basis of images of the crop in the field is proposed in this article. Cultivated in most of the world, cotton is one of the economically most important agricultural crops. Its cultivation in tropical regions has made it the target of a wide spectrum of agricultural pests and diseases, and efficient solutions are required. Moreover, the symptoms of the main pests and diseases cannot be differentiated in the initial stages, and the correct identification of a lesion can be difficult for the producer. To help resolve the problem, the present research provides a solution based on deep learning in the screening of cotton leaves which makes it possible to monitor the health of the cotton crop and make better decisions for its management. With the learning models GoogleNet and Resnet50 using convolutional neural networks, a precision of 86.6% and 89.2%, respectively, was obtained. Compared with traditional approaches for the processing of images such as support vector machines (SVM), Closest k-neighbors (KNN), artificial neural networks (ANN) and neuro-fuzzy (NFC), the convolutional neural networks proved to be up to 25% more precise, suggesting that this method can contribute to a more rapid and reliable inspection of the plants growing in the field.

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“…The biggest drawback of agrochemicals for weed and pest control is that they are very harmful to the environment and population that thereby makes their over-utilization a menace. Most of the farmers in Pakistan face health issues because of exposure to the agrochemicals while they are applied to crops/weeds [9]. Arielly F.B.…”

Section: Introductionmentioning

confidence: 99%

Design and Implementation of Low-Cost Field Crop Sprayer Electronic Flow Control System

İrsel

2021

Gazi University Journal of Science

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Real-Time Machine-Learning Based Crop/Weed Detection and Classification for Variable-Rate Spraying in Precision Agriculture

Cited by 86 publications

References 17 publications

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

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

Identification of Cotton Leaf Lesions Using Deep Learning Techniques

Design and Implementation of Low-Cost Field Crop Sprayer Electronic Flow Control System

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