An automatic visible-range video weed detection, segmentation and classification prototype in potato field

Sabzi, Sajad; Abbaspour‐Gilandeh, Yousef; Arribas, Juan Ignacio

doi:10.1016/j.heliyon.2020.e03685

Cited by 50 publications

(33 citation statements)

References 20 publications

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“…The Hu moment invariants are independent of geometric translation, scaling, and rotation, providing a high discrimination power to discriminate different morphological classes of objects [13]. These features and their calculation formulas are described by Rhouma, et al [55], Fatma and Dash [15] and Sabzi, et al [56].…”

Section: Shape Featuresmentioning

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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Section: Shape Featuresmentioning

confidence: 99%

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

Bakhshipour

2021

IEEE Access

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“…Compared with single-feature recognition, this multi-feature decision fusion recognition method has better stability and a higher recognition accuracy. Sabzi et al [ 5 ] proposed a machine vision prototype based on video processing and meta-heuristic classifiers based on Gray-level Co-occurrence Matrix (GLCM), color feature, texture feature, invariant moment, and shape feature. They used them to identify and classify 4299 samples from potatoes and five weed species online, achieving high accuracy.…”

Section: Traditional Machine Learning Weed Detection Methodsmentioning

confidence: 99%

“…Methods for realizing field weed detection by using computer vision technology mainly include traditional image processing and deep learning. When weed detection is conducted with traditional image-processing technology, extracting features, such as color, texture, and shape, of the image and combining with traditional machine learning methods, such as random forest or Support Vector Machine (SVM) algorithm, for weed identification are necessary [ 5 ]. These methods need to design features manually and have high dependence on image acquisition methods, preprocessing methods, and the quality of feature extraction.…”

Section: Introductionmentioning

confidence: 99%

Review of Weed Detection Methods Based on Computer Vision

Chen

Zhao

et al. 2021

Sensors

172

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Weeds are one of the most important factors affecting agricultural production. The waste and pollution of farmland ecological environment caused by full-coverage chemical herbicide spraying are becoming increasingly evident. With the continuous improvement in the agricultural production level, accurately distinguishing crops from weeds and achieving precise spraying only for weeds are important. However, precise spraying depends on accurately identifying and locating weeds and crops. In recent years, some scholars have used various computer vision methods to achieve this purpose. This review elaborates the two aspects of using traditional image-processing methods and deep learning-based methods to solve weed detection problems. It provides an overview of various methods for weed detection in recent years, analyzes the advantages and disadvantages of existing methods, and introduces several related plant leaves, weed datasets, and weeding machinery. Lastly, the problems and difficulties of the existing weed detection methods are analyzed, and the development trend of future research is prospected.

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“…They used Near-Infrared (NIR) image cues with those features. Sabzi et al (2020) extracted eight texture features based on the grey level co-occurrence matrix (GLCM), two spectral descriptors of texture, thirteen different colour features, five moment-invariant features, and eight shape features.…”

Section: Traditional Ml-vs Dl-based Weed Detection Methodsmentioning

confidence: 99%

A Survey of Deep Learning Techniques for Weed Detection from Images

Hasan¹,

Sohel²,

Diepeveen³

et al. 2021

Preprint

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The rapid advances in Deep Learning (DL) techniques have enabled rapid detection, localisation, and recognition of objects from images or videos. DL techniques are now being used in many applications related to agriculture and farming. Automatic detection and classification of weeds can play an important role in weed management and so contribute to higher yields. Weed detection in crops from imagery is inherently a challenging problem because both weeds and crops have similar colours ('green-on-green'), and their shapes and texture can be very similar at the growth phase. Also, a crop in one setting can be considered a weed in another. In addition to their detection, the recognition of specific weed species is essential so that targeted controlling mechanisms (e.g. appropriate herbicides and correct doses) can be applied. In this paper, we review existing deep learning-based weed detection and classification techniques. We cover the detailed literature on four main procedures, i.e., data acquisition, dataset preparation, DL techniques employed for detection, location and classification of weeds in crops, and evaluation metrics approaches. We found that

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An automatic visible-range video weed detection, segmentation and classification prototype in potato field

Cited by 50 publications

References 20 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

Review of Weed Detection Methods Based on Computer Vision

A Survey of Deep Learning Techniques for Weed Detection from Images

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