Classification of crops and weeds from digital images: A support vector machine approach

Ahmed, Faiz; Al-Mamun, Hawlader Abdullah; Bari, Amna; Hossain, Emam; Kwan, Paul

doi:10.1016/j.cropro.2012.04.024

Cited by 161 publications

(67 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Table 3 shows the comparative analysis of the accuracy of the proposed ATP extraction method and existing techniques. The proposed method is compared with the existing SVM approach for classification of crops and weeds from the digital image [10] and SVM based Crop/weed classification in maize fields [27]. The overall accuracy of the proposed approach is 99.3 %, which is relatively higher than the existing techniques.…”

Section: Hausdorff Distancementioning

confidence: 99%

“…The accuracy level of the proposed method is compared with the accuracy levels of the existing techniques such as SVM approach for classification of crops and weeds from digital image [10] and SVM based Crop/weed classification in maize fields [27].…”

Section: • Hausdorff Distancementioning

confidence: 99%

“…The nightshade plants were identified correctly within a short period. Ahmed et al [10] investigated the usage of the machine-learning algorithm for the weed and crop classification. Testing of the features was performed to find the optimal combination of the significant features that ensures the highest classification rate.…”

Section: Related Workmentioning

confidence: 99%

See 2 more Smart Citations

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

Prema¹,

Murugan²

2016

ELCVIA

View full text Add to dashboard Cite

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.

show abstract

Section: Hausdorff Distancementioning

confidence: 99%

Section: • Hausdorff Distancementioning

confidence: 99%

See 1 more Smart Citation

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

Prema¹,

Murugan²

2016

ELCVIA

View full text Add to dashboard Cite

show abstract

“…By doing so, any ambient visible light was blocked (Ahmed et al 2012;Å strand and Baerveldt 2002;Haug et al 2014;Lee et al 1999). Constant illumination under the cover was then obtained using artificial lighting (Nieuwenhuizen et al 2010;Polder et al 2014).…”

Section: Introductionmentioning

confidence: 99%

Improved vegetation segmentation with ground shadow removal using an HDR camera

2017

View full text Add to dashboard Cite

A vision-based weed control robot for agricultural field application requires robust vegetation segmentation. The output of vegetation segmentation is the fundamental element in the subsequent process of weed and crop discrimination as well as weed control. There are two challenging issues for robust vegetation segmentation under agricultural field conditions: (1) to overcome strongly varying natural illumination; (2) to avoid the influence of shadows under direct sunlight conditions. A way to resolve the issue of varying natural illumination is to use high dynamic range (HDR) camera technology. HDR cameras, however, do not resolve the shadow issue. In many cases, shadows tend to be classified during the segmentation as part of the foreground, i.e., vegetation regions. This study proposes an algorithm for ground shadow detection and removal, which is based on color space conversion and a multilevel threshold, and assesses the advantage of using this algorithm in vegetation segmentation under natural illumination conditions in an agricultural field. Applying shadow removal improved the performance of vegetation segmentation with an average improvement of 20, 4.4, and 13.5% in precision, specificity and modified accuracy, respectively. The average processing time for vegetation segmentation with shadow removal was 0.46 s, which is acceptable for real-time application (\1 s required). The proposed ground shadow detection and removal method enhances the performance of vegetation segmentation under natural illumination conditions in the field and is feasible for real-time field applications.

show abstract

“…Wu & Wen (2009) proposed a support vector machine classifier to identify weeds in corn fields during early crop stages, using the co-occurrence matrix in gray levels and statistical histogram properties to extract texture features with greater accuracy to 92%. Similarly, Ahmed, et al (2012) evaluate fourteen colour, size and moment invariant features to get an optimal combination that provides the highest classification rate; their result achieves above 97% accuracy.…”

Section: Introductionmentioning

confidence: 99%

Weed recognition by SVM texture feature classification in outdoor vegetable crops images

Rojas¹,

Guzmán²,

Toledo³

2017

ing.inv.

View full text Add to dashboard Cite

This paper presents a classification system for weeds and vegetables from outdoor crop images. The classifier is based on Support Vector Machine (SVM) with its extension to the nonlinear case, using the Radial Basis Function (RBF) and optimizing its scale parameter σ to smooth the boundary decision. The feature space is the result of Principal Component Analysis (PCA) for 10 texture measurements calculated from Gray Level Co-occurrence Matrices (GLCM). The results indicate that classifier performance is above 90%, validated with specificity, sensitivity and precision calculations.

show abstract

Classification of crops and weeds from digital images: A support vector machine approach

Cited by 161 publications

References 17 publications

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

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

Improved vegetation segmentation with ground shadow removal using an HDR camera

Weed recognition by SVM texture feature classification in outdoor vegetable crops images

Contact Info

Product

Resources

About