Machine learning approaches to automate weed detection by UAV based sensors

Etienne, Aaron; Saraswat, Dharmendra

doi:10.1117/12.2520536

Cited by 24 publications

(11 citation statements)

References 6 publications

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“…The overall accuracies achieved by this technique for BPNN and SVM were 96.601% and 95.078%, respectively. In [14], the authors proposed an automated weed detection framework that was able to detect weeds at the various phases of plant growth. Colour, multispectral and thermal images were captured by using UAV-based sensors.…”

Section: Literature Review On Machine Learning Algorithms For Weed Detectionmentioning

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: Literature Review On Machine Learning Algorithms For Weed Detectionmentioning

confidence: 99%

“…In this case, weeds are detected within mature crops; this requires finding out unique features that differentiate weeds and crops (spectral signature, shape, etc.). This can be quite challenging and sometimes requires laborious annotation/ground truthing [14]. For our paper, we have done the weed detection at early stage of crop growth.…”

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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“…DroneDeploy TM mobile flight mission planning software was utilized for data collection missions. DroneDeploy TM is "a flight automation software for unmanned aerial systems that allows users to set a predetermined flight path, speed, and percentage value of side and front overlap" [51]. Side and front overlap values were set at 75%.…”

Section: Methodsmentioning

confidence: 99%

Deep Learning-Based Object Detection System for Identifying Weeds Using UAS Imagery

et al. 2021

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Current methods of broadcast herbicide application cause a negative environmental and economic impact. Computer vision methods, specifically those related to object detection, have been reported to aid in site-specific weed management procedures for targeted herbicide application within a field. However, a major challenge to developing a weed detection system is the requirement for a properly annotated database to differentiate between weeds and crops under field conditions. This research involved creating an annotated database of 374 red, green, and blue (RGB) color images organized into monocot and dicot weed classes. The images were acquired from corn and soybean research plots located in north-central Indiana using an unmanned aerial system (UAS) flown at 30 and 10 m heights above ground level (AGL). A total of 25,560 individual weed instances were manually annotated. The annotated database consisted of four different subsets (Training Image Sets 1–4) to train the You Only Look Once version 3 (YOLOv3) deep learning model for five separate experiments. The best results were observed with Training Image Set 4, consisting of images acquired at 10 m AGL. For monocot and dicot weeds, respectively, an average precision (AP) score of 91.48 % and 86.13% was observed at a 25% IoU threshold (AP @ T = 0.25), as well as 63.37% and 45.13% at a 50% IoU threshold (AP @ T = 0.5). This research has demonstrated a need to develop large, annotated weed databases to evaluate deep learning models for weed identification under field conditions. It also affirms the findings of other limited research studies utilizing object detection for weed identification under field conditions.

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“…Finally, drone (UAV) imaging holds important potential for automating farm tasks since drones can easily cover large areas of uneven terrain [53]. Deep learning has been successfully applied to derive growth rate from nitrogen fertilization on drone images [20], estimate the emergence rate of seeds in the field [33], wheat density [25], weed detection [16], and land classification [11]. The main drawback when applying deep learning on drone images for plant phenotyping remains the difficulty of ground-truthing the images because of the large areas covered [53].…”

Section: Computer Vision For Agriculturementioning

confidence: 99%

Unsupervised domain adaptation and super resolution on drone images for autonomous dry herbage biomass estimation

Albert

Saadeldin

Narayanan

et al. 2022

2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)

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Herbage mass yield and composition estimation is an important tool for dairy farmers to ensure an adequate supply of high quality herbage for grazing and subsequently milk production. By accurately estimating herbage mass with a large amount of unlabeled drone images. We validate our results on a small held-out drone image test set to show the validity of our approach, which opens the way for automated dry herbage biomass monitoring www.github. com/PaulAlbert31/Clover_SSL.

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Machine learning approaches to automate weed detection by UAV based sensors

Cited by 24 publications

References 6 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

Deep Learning-Based Object Detection System for Identifying Weeds Using UAS Imagery

Unsupervised domain adaptation and super resolution on drone images for autonomous dry herbage biomass estimation

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