Development of Inter-Leaves Weed and Plant Regions Identification Algorithm using Histogram of Oriented Gradient and K-Means Clustering

Saha, Dheeman; Hamer, George; Lee, Ji Young

doi:10.1145/3129676.3129700

Cited by 5 publications

(4 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…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

View full text Add to dashboard Cite

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.

show abstract

Section: Introductionmentioning

confidence: 99%

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

Bakhshipour

2021

IEEE Access

View full text Add to dashboard Cite

show abstract

“…In addition, the overall accuracy obtained by SVM is 88%. In a related paper [ 100 ], the authors discussed the problem of overlapping weed and carrots leaves. In the initial stage of plant development, the color of both plant and weed are the same, which makes it more challenging to identify the weed and plant.…”

Section: Summary Of Identified Articles In Slrmentioning

confidence: 99%

Weed Detection Using Deep Learning: A Systematic Literature Review

Murad

Mahmood

Forkan

et al. 2023

Sensors

View full text Add to dashboard Cite

Weeds are one of the most harmful agricultural pests that have a significant impact on crops. Weeds are responsible for higher production costs due to crop waste and have a significant impact on the global agricultural economy. The importance of this problem has promoted the research community in exploring the use of technology to support farmers in the early detection of weeds. Artificial intelligence (AI) driven image analysis for weed detection and, in particular, machine learning (ML) and deep learning (DL) using images from crop fields have been widely used in the literature for detecting various types of weeds that grow alongside crops. In this paper, we present a systematic literature review (SLR) on current state-of-the-art DL techniques for weed detection. Our SLR identified a rapid growth in research related to weed detection using DL since 2015 and filtered 52 application papers and 8 survey papers for further analysis. The pooled results from these papers yielded 34 unique weed types detection, 16 image processing techniques, and 11 DL algorithms with 19 different variants of CNNs. Moreover, we include a literature survey on popular vanilla ML techniques (e.g., SVM, random forest) that have been widely used prior to the dominance of DL. Our study presents a detailed thematic analysis of ML/DL algorithms used for detecting the weed/crop and provides a unique contribution to the analysis and assessment of the performance of these ML/DL techniques. Our study also details the use of crops associated with weeds, such as sugar beet, which was one of the most commonly used crops in most papers for detecting various types of weeds. It also discusses the modality where RGB was most frequently used. Crop images were frequently captured using robots, drones, and cell phones. It also discusses algorithm accuracy, such as how SVM outperformed all machine learning algorithms in many cases, with the highest accuracy of 99 percent, and how CNN with its variants also performed well with the highest accuracy of 99 percent, with only VGGNet providing the lowest accuracy of 84 percent. Finally, the study will serve as a starting point for researchers who wish to undertake further research in this area.

show abstract

“…The Feret diameter is defined as (1) the longest distance between two points on the contour, and (2) the shortest distance across the shape. Some of these properties are also used by Lottes et al [32] and Saha et al [44]. 2 Object-Oriented Programming.…”

Section: Shape Particlementioning

confidence: 99%

“…The HoG descriptors for Histogram of Oriented Gradient was used by Saha et al [44] for weed discrimination. It is often used in computer vision, both for object detection and texture discrimination because of its properties of geometric invariance.…”

Section: Spectral Hogmentioning

confidence: 99%

Towards the characterization of crop and weeds at leaf scale: A large comparison of shape, spatial and textural features

Vayssade

Jones

Paoli

2023

Smart Agricultural Technology

View full text Add to dashboard Cite

Development of Inter-Leaves Weed and Plant Regions Identification Algorithm using Histogram of Oriented Gradient and K-Means Clustering

Cited by 5 publications

References 8 publications

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

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

Weed Detection Using Deep Learning: A Systematic Literature Review

Towards the characterization of crop and weeds at leaf scale: A large comparison of shape, spatial and textural features

Contact Info

Product

Resources

About