Comparison of Supervised Classifiers and Image Features for Crop Rows Segmentation on Aerial Images

Júnior, Paulo César Pereira; Monteiro, Alexandrina; Ribeiro, Rafael da Luz; Sobieranski, Antonio Carlos; Wangenheim, Aldo von

doi:10.1080/08839514.2020.1720131

Cited by 10 publications

(13 citation statements)

References 36 publications

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“…Our experiment tested the accuracy of the model on the test set when the minibatch size was 32, 64, 128 and 256. The performance was measured using mean average precision (mAP) [ 6 ]. The results are shown in Table 1 .…”

Section: Methodsmentioning

confidence: 99%

A hybrid CNN–SVM classifier for weed recognition in winter rape field

Tao

Wei

2022

Plant Methods

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Background Weed recognition is key for automatic weeding, which is a challenging problem. Weed recognition is mainly based on different features of crop images. The extracted image features mainly include color, texture, shape, etc. The designed features depend on manual work, which is blind to some extent. Meanwhile these features have poor generalization performance on a sample set. The final discrimination results tend to have a greater difference. The current study proposed a deep convolutional neural network (CNN) with support vector machine (SVM) classifier which aims to improve the classification accuracy of winter rape seeding and weeds in fields. Results The VGG network model was adopted, which received a true color image (224 × 224 pixels) of rape/weed as the input. The proposed VGG-SVM model was able to identify rape/weeds with average accuracies of 99.2% in the training procedures and 92.1% in the test procedures, respectively. A comparative experiment was conducted using the proposed VGG-SVM model and five other methods. The proposed VGG-SVM model obtained a higher classification accuracy, greater robustness and real time. Conclusions The VGG-SVM weed classification model proposed in this study is effective. The model can be further applied to the recognition of multi-sample mixed crop images in fields.

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Section: Methodsmentioning

confidence: 99%

A hybrid CNN–SVM classifier for weed recognition in winter rape field

Tao

Wei

2022

Plant Methods

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“…A typical ML-based weed classification technique follows five key steps: image acquisition, preprocessing such as image enhancement, feature extraction or with feature selection, applying an ML-based classifier and evaluation of the performance (Bini et al, 2020;César Pereira Júnior et al, 2020;Liakos et al, 2018;B. Liu & Bruch, 2020).…”

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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“…Table 2 illustrates some of the possible vegetation indices that can be derived. Edge detectors can also be useful and are commonly used, such as Gaussian, Laplacian, and Canny filters [52]; Gabor filters, gray-level co-occurrence matrix (GLCM) [53], and geometric and statistical features [54] are among other useful features used in precision farming.…”

Section: Image Features Used In Uav Datamentioning

confidence: 99%

“…Support vector machines (SVM) were used for classifying vegetation by health status [52], classifying trees by type [61], identifying and classifying weeds to generate weed maps [62], and lastly, segmenting crop rows [53].…”

Section: Support Vector Machines (Svm)mentioning

confidence: 99%

Machine Learning for Precision Agriculture Using Imagery from Unmanned Aerial Vehicles (UAVs): A Survey

Zualkernan

Abuhani

Hussain

et al. 2023

Drones

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Unmanned aerial vehicles (UAVs) are increasingly being integrated into the domain of precision agriculture, revolutionizing the agricultural landscape. Specifically, UAVs are being used in conjunction with machine learning techniques to solve a variety of complex agricultural problems. This paper provides a careful survey of more than 70 studies that have applied machine learning techniques utilizing UAV imagery to solve agricultural problems. The survey examines the models employed, their applications, and their performance, spanning a wide range of agricultural tasks, including crop classification, crop and weed detection, cropland mapping, and field segmentation. Comparisons are made among supervised, semi-supervised, and unsupervised machine learning approaches, including traditional machine learning classifiers, convolutional neural networks (CNNs), single-stage detectors, two-stage detectors, and transformers. Lastly, future advancements and prospects for UAV utilization in precision agriculture are highlighted and discussed. The general findings of the paper demonstrate that, for simple classification problems, traditional machine learning techniques, CNNs, and transformers can be used, with CNNs being the optimal choice. For segmentation tasks, UNETs are by far the preferred approach. For detection tasks, two-stage detectors delivered the best performance. On the other hand, for dataset augmentation and enhancement, generative adversarial networks (GANs) were the most popular choice.

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Comparison of Supervised Classifiers and Image Features for Crop Rows Segmentation on Aerial Images

Cited by 10 publications

References 36 publications

A hybrid CNN–SVM classifier for weed recognition in winter rape field

A hybrid CNN–SVM classifier for weed recognition in winter rape field

A Survey of Deep Learning Techniques for Weed Detection from Images

Machine Learning for Precision Agriculture Using Imagery from Unmanned Aerial Vehicles (UAVs): A Survey

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