Weed Detection Approach Using Feature Extraction and KNN Classification

Khurana, Gurpreet; Bawa, Navneet

doi:10.1007/978-981-15-5463-6_60

Cited by 10 publications

(5 citation statements)

References 14 publications

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“…Asad et al [17] made performance comparison of deep learning metaarchitectures like SegNet and UNET and encoder blocks like VGG16 and ResNet-50 on high-resolution color images of canola fields. Textural feature analysis and morphological scanning were applied to sugar beet plant by Khurana et al [18], and then a KNN classifier was used to classify weed plant from field crop. Arun et al [19] evaluated and compared two object detection models, namely, Faster RCNN and the Single Shot Detector (SSD), over UAV imagery for weed detection in soybean fields.…”

Section: Introductionmentioning

confidence: 99%

Weed Identification Using Deep Learning and Image Processing in Vegetable Plantation

Che²,

2021

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Weed identification in vegetable plantation is more challenging than crop weed identification due to their random plant spacing. So far, little work has been found on identifying weeds in vegetable plantation. Traditional methods of crop weed identification used to be mainly focused on identifying weed directly; however, there is a large variation in weed species. This paper proposes a new method in a contrary way, which combines deep learning and image processing technology. Firstly, a trained CenterNet model was used to detect vegetables and draw bounding boxes around them. Afterwards, the remaining green objects falling out of bounding boxes were considered as weeds. In this way, the model focuses on identifying only the vegetables and thus avoid handling various weed species. Furthermore, this strategy can largely reduce the size of training image dataset as well as the complexity of weed detection, thereby enhancing the weed identification performance and accuracy. To extract weeds from the background, a color index-based segmentation was performed utilizing image processing. The employed color index was determined and evaluated through Genetic Algorithms (GAs) according to Bayesian classification error. During the field test, the trained CenterNet model achieved a precision of 95.6%, a recall of 95.0%, and a " score of 0.953, respectively. The proposed index-19R + 24G-2B ≥ 862 yields high segmentation quality with a much lower computational cost compared to the wildly used ExG index. These experiment results demonstrate the feasibility of using the proposed method for the ground-based weed identification in vegetable plantation.

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Section: Introductionmentioning

confidence: 99%

Weed Identification Using Deep Learning and Image Processing in Vegetable Plantation

Che²,

2021

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“…Different supervised and unsupervised classifier algorithms including Artificial Neural Networks (ANN), Support Vector Machine (SVM), Decision Tree (DT), Principal Component Analysis (PCA), Bayesian Classifier (BC), Linear Discriminant Analysis (LDA), K-Means, k-Nearest Neighbors (kNN), etc. have been applied for distinguishing the crops from weeds [7,13,[30][31][32][33][34][35][36][37][38].…”

Section: Introductionmentioning

confidence: 99%

Cascading Feature Filtering and Boosting Algorithm for Plant Type Classification Based on Image Features

Bakhshipour

2021

IEEE Access

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Crop and weeds identification is of important steps towards the development of efficient automotive weed control systems. The higher the accuracy of plant detection and classification, the higher the performance of the weeding machine. In this study, the capability of two popular boosting methods including Adaboost.M1 and LogitBoost algorithms was evaluated to enhance the plant classification performance of four classifiers, namely Multi-Layer Perceptron (MLP), k-Nearest Neighbors (kNN), Random Forest (RF), and Support Vector Machine (SVM). Four feature filtering techniques including Correlationbased Feature Selection (CFS), Information Gain (IG), Gain Ratio (GR), and OneR were applied to the imageextracted features and 10 of the most significant features were selected and fed into single and boosted classifiers. The RF model trained by IG selected features (IG-RF) was the most appropriate classifier among the evaluated models whether in single or boosted modes. It was also found that boosting of IG-RF by using Adaboost.M1 and LogitBoost algorithms improved the classification accuracy. Regarding the performance values, the LogitBoost-IG-RF structure, which provided a classification accuracy of 99.58%, a kappa (k) of 0.9948, and a Root Mean Squared Error (RMSE) of 0.0688 on training dataset, was selected as the most appropriate classifier for plant discrimination in peanut fields. The accuracy, k, and RMSE criteria of this combination on test dataset were 95.00%, 0.9375, and 0.1591, respectively. It was concluded that combination of boosting algorithms and feature selection methods can promote plant type discrimination accuracy, which is a crucial factor in the development of precision weed control systems.

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“…As mentioned earlier, random forest (RF) [47], Bayesian decision [48], K-means [49], SVM [50,51] and k-nearest neighbour (KNN) [52] have been widely used for weed and crop classification [53]. Other algorithms including naive Bayes [54], artificial neural networks(ANNs), and Ada-Boost [31] have been used in weed detection.…”

Section: Classifiermentioning

confidence: 99%

Review of Current Robotic Approaches for Precision Weed Management

Zhang

et al. 2022

Curr Robot Rep

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Purpose of ReviewThe goal of this review is to provide an overview of current robotic approaches to precision weed management. This includes an investigation into applications within this field during the past 5 years, identifying which major technical areas currently preclude more widespread use, and which key topics will drive future development and utilisation. Recent Findings Studies combining computer vision with traditional machine learning and deep learning are driving progress in weed detection and robotic approaches to mechanical weeding. Integrating key technologies for perception, decisionmaking, and control, autonomous weeding robots are emerging quickly. These effectively save effort while reducing environmental pollution caused by pesticide use. Summary This review assesses different weed detection methods and weeder robots used in precision weed management and summarises the trends in this area in recent years. The limitations of current systems are discussed, and ideas for future research directions are proposed.

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Weed Detection Approach Using Feature Extraction and KNN Classification

Cited by 10 publications

References 14 publications

Weed Identification Using Deep Learning and Image Processing in Vegetable Plantation

Weed Identification Using Deep Learning and Image Processing in Vegetable Plantation

Cascading Feature Filtering and Boosting Algorithm for Plant Type Classification Based on Image Features

Review of Current Robotic Approaches for Precision Weed Management

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