Development of a Machine Vision System for Weed Detection During Both of Off-Season and in-Season in Broadacre No-Tillage Cropping Lands

Liu, Huajian; Lee, Sang‐Heon; Saunders, Chris

doi:10.3844/ajabssp.2014.174.193

Cited by 27 publications

(9 citation statements)

References 40 publications

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“…This transformation was firstly employed by Woebbecke et al (1995). Then similar equations were used in several studies (Sabancı 2013;Hlaing & Khaing 2014;Liu et al 2014).…”

Section: Methodsmentioning

confidence: 99%

Implementation of Patch Spraying Application to the Weed Areas Detected in Corn Lands

Karadöl¹,

Aybek²,

Üçgül³

2020

Tarım Bilimleri Dergisi

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Weed control is vital in agricultural production. Chemical control methods are generally preferred in weed control as they (1) affect quickly and (2) reduce the labour requirement. However, in conventional applications chemicals are generally applied to whole field surface. Therefore, non-targeted areas are also sprayed. This increases 1) amount of herbicide used and (2) risk of off-target chemical movement. In this study, a patch spraying system was developed to automatically detect and spray herbicides on weeds in the corn field based on weed density. In order to determine the weed regions, a digital camera was fitted in front of the tractor. The images taken using the camera were then simultaneously processed using an algorithm written in Matlab TM software. The results of the field study showed that at 4, 6 and 8 km h -1 forward speeds, application volumes decrease by 30.21%, 28.82% and 32.28%, respectively, when it is compared to the conventional application methods. It was also determined that the application accuracy rates were 80%, 81.66% and 75% respectively for 4, 6 and 8 km h -1 speeds.

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“…This transformation was firstly employed by Woebbecke et al (1995). Then similar equations were used in several studies (Sabancı 2013;Hlaing & Khaing 2014;Liu et al 2014).…”

Section: Methodsmentioning

confidence: 99%

Implementation of Patch Spraying Application to the Weed Areas Detected in Corn Lands

Karadöl¹,

Aybek²,

Üçgül³

2020

Tarım Bilimleri Dergisi

View full text Add to dashboard Cite

show abstract

“…The overall classification accuracy was improved. Liu et al [14] developed a new algorithm for detecting the green plant using the hybrid spectral indices. The computation speed of the inter-row weed detection algorithm was higher than the Hough transformation method.…”

Section: Related Workmentioning

confidence: 99%

A Novel Angular Texture Pattern (ATP) Extraction Method for Crop and Weed Discrimination Using Curvelet Transformation

Prema¹,

Murugan²

2016

ELCVIA

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Weed management is the most significant process in the agricultural applications to improve the crop productivity rate and reduce the herbicide application cost. Existing weed detection techniques does not yield better performance due to the complex background and illumination variation. Hence, there arises a need for the development of effective weed identification technique. To overcome this drawback, this paper proposes a novel Angular Texture Pattern (ATP) Extraction Method for crop and weed discrimination using curvelet transformation. In our proposed work, Adaptive Median Filter (AMF) is used for filtering the impulse noise from the image. Plant image identification is performed using green pixel extraction and K-means clustering. Wrapping based Curvelet transform is applied to the plant image. Feature extraction is performed to extract the angular texture pattern of the plant image. Particle Swarm Optimization (PSO)-based Differential Evolution Feature Selection (DEFS) approach is applied to select the optimal features. Then, the selected features are learned and passed through an RVM-based classifier to find out the weed. Edge detection and contouring is performed to identify the weed in the plant image. The fuzzy rule-based approach is applied to detect the low, medium and high levels of the weed patchiness. From the experimental results, it is clearly observed that the accuracy of the proposed approach is higher than the existing Support Vector Machine (SVM) based approaches. The proposed approach achieves higher accuracy, sensitivity, specificity, Hausdorff distance, Jaccard distance and Dice distance.

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“…However, the performance of computer vision algorithms is greatly dependent on the selection of an appropriate set of features [9]. Particularly, the key characteristics of vegetation (crops and weeds), which comprise biological morphology [10][11][12], spectral features [13][14][15], spatial contexts [16][17][18] and visual textures [19][20][21] can be extracted by applying different characterization methods. Each of these characteristics has its own advantages, and depends on the complexity of the generated datasets for plant species.…”

Section: Introductionmentioning

confidence: 99%

Performances of the LBP Based Algorithm over CNN Models for Detecting Crops and Weeds with Similar Morphologies

Ahderom

Alameh

2020

Sensors

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Weed invasions pose a threat to agricultural productivity. Weed recognition and detection play an important role in controlling weeds. The challenging problem of weed detection is how to discriminate between crops and weeds with a similar morphology under natural field conditions such as occlusion, varying lighting conditions, and different growth stages. In this paper, we evaluate a novel algorithm, filtered Local Binary Patterns with contour masks and coefficient k (k-FLBPCM), for discriminating between morphologically similar crops and weeds, which shows significant advantages, in both model size and accuracy, over state-of-the-art deep convolutional neural network (CNN) models such as VGG-16, VGG-19, ResNet-50 and InceptionV3. The experimental results on the “bccr-segset” dataset in the laboratory testbed setting show that the accuracy of CNN models with fine-tuned hyper-parameters is slightly higher than the k-FLBPCM method, while the accuracy of the k-FLBPCM algorithm is higher than the CNN models (except for VGG-16) for the more realistic “fieldtrip_can_weeds” dataset collected from real-world agricultural fields. However, the CNN models require a large amount of labelled samples for the training process. We conducted another experiment based on training with crop images at mature stages and testing at early stages. The k-FLBPCM method outperformed the state-of-the-art CNN models in recognizing small leaf shapes at early growth stages, with error rates an order of magnitude lower than CNN models for canola–radish (crop–weed) discrimination using a subset extracted from the “bccr-segset” dataset, and for the “mixed-plants” dataset. Moreover, the real-time weed–plant discrimination time attained with the k-FLBPCM algorithm is approximately 0.223 ms per image for the laboratory dataset and 0.346 ms per image for the field dataset, and this is an order of magnitude faster than that of CNN models.

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Development of a Machine Vision System for Weed Detection During Both of Off-Season and in-Season in Broadacre No-Tillage Cropping Lands

Cited by 27 publications

References 40 publications

Implementation of Patch Spraying Application to the Weed Areas Detected in Corn Lands

Implementation of Patch Spraying Application to the Weed Areas Detected in Corn Lands

A Novel Angular Texture Pattern (ATP) Extraction Method for Crop and Weed Discrimination Using Curvelet Transformation

Performances of the LBP Based Algorithm over CNN Models for Detecting Crops and Weeds with Similar Morphologies

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