Detecting creeping thistle in sugar beet fields using vegetation indices

Kazmi, Wajahat; García-Ruiz, Francisco; Nielsen, Jon; Rasmussen, Jesper; Andersen, Hans Jørgen

doi:10.1016/j.compag.2015.01.008

Cited by 66 publications

(22 citation statements)

References 27 publications

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“…These detected inter-row weeds have the potential to be used as training samples for intra-row weed detections. Kazmi et al [39] used conventional image processing algorithms and explored hand-crafted features with traditional machine learning techniques for creeping thistle weed detection in sugar beet fields. Although good accuracy was achieved with only using colour information, the use of hand-crafted features makes it difficult to guarantee the robustness of the developed model under changing environmental conditions and variations in plant development.…”

Section: Discussionmentioning

confidence: 99%

Deep convolutional neural networks for image-based Convolvulus sepium detection in sugar beet fields

et al. 2020

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Background: Convolvulus sepium (hedge bindweed) detection in sugar beet fields remains a challenging problem due to variation in appearance of plants, illumination changes, foliage occlusions, and different growth stages under field conditions. Current approaches for weed and crop recognition, segmentation and detection rely predominantly on conventional machine-learning techniques that require a large set of hand-crafted features for modelling. These might fail to generalize over different fields and environments. Results: Here, we present an approach that develops a deep convolutional neural network (CNN) based on the tiny YOLOv3 architecture for C. sepium and sugar beet detection. We generated 2271 synthetic images, before combining these images with 452 field images to train the developed model. YOLO anchor box sizes were calculated from the training dataset using a k-means clustering approach. The resulting model was tested on 100 field images, showing that the combination of synthetic and original field images to train the developed model could improve the mean average precision (mAP) metric from 0.751 to 0.829 compared to using collected field images alone. We also compared the performance of the developed model with the YOLOv3 and Tiny YOLO models. The developed model achieved a better trade-off between accuracy and speed. Specifically, the average precisions (APs@IoU0.5) of C. sepium and sugar beet were 0.761 and 0.897 respectively with 6.48 ms inference time per image (800 × 1200) on a NVIDIA Titan X GPU environment. Conclusion: The developed model has the potential to be deployed on an embedded mobile platform like the Jetson TX for online weed detection and management due to its high-speed inference. It is recommendable to use synthetic images and empirical field images together in training stage to improve the performance of models.

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Section: Discussionmentioning

confidence: 99%

Deep convolutional neural networks for image-based Convolvulus sepium detection in sugar beet fields

et al. 2020

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“…After the in-lab assessment of the 34 RGB indices, the 4 vegetation indices for which the best performance was found were tested in real field conditions, in a sugar beet plot located in Soto de Cerrato, Palencia, Spain (41 • [46]) was used as an alternative procedure to obtain new indices. Some literature indices were considered as inputs, and resultant indices were calculated by adding and removing variables one by one [44], using AIC (Akaike Information Criterion) method for variable selection [47]. Indices SLR1 to SLR5 and I2 (Table 1) were obtained using this procedure.…”

Section: Field Testmentioning

confidence: 99%

“…RGRI [12], VARI [41] and ExR [42] indices also showed high correlations (above 0.75). These last two indices have been used for vegetation extraction and artificial vision in agriculture [44]. The relationship of ExR and I PCA with the chlorophyll measurements during the experiment are shown in Figure S2.…”

Section: Indices From Other Sourcesmentioning

confidence: 99%

“…Problems in the image-taking process associated with the use of natural light have been reported in the literature. For instance, Kawashima et al [32] reported difficulties in the discrimination of leaf color under clear lighting conditions with direct solar radiation and only used pictures taken on cloudy days, El-Faki et al [52] recommended to take the images under dim illumination, and Kazmi et al [44] concluded that shooting under a shade should be preferred.…”

Section: Analysis Of Leaf Rgb Data Acquisition Proceduresmentioning

confidence: 99%

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Assessment of RGB Vegetation Indices to Estimate Chlorophyll Content in Sugar Beet Leaves in the Final Cultivation Stage

et al. 2020

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Estimation of chlorophyll content with portable meters is an easy way to quantify crop nitrogen status in sugar beet leaves. In this work, an alternative for chlorophyll content estimation using RGB-only vegetation indices has been explored. In a first step, pictures of spring-sown ‘Fernanda KWS’ variety sugar beet leaves taken with a commercial camera were used to calculate 25 RGB indices reported in the literature and to obtain 9 new indices through principal component analysis (PCA) and stepwise linear regression (SLR) techniques. The performance of the 34 indices was examined in order to evaluate their ability to estimate chlorophyll content and chlorophyll degradation in the leaves under different natural light conditions along 4 days of the canopy senescence period. Two of the new proposed RGB indices were found to improve the already good performance of the indices reported in the literature, particularly for leaves featuring low chlorophyll contents. The 4 best indices were finally tested in field conditions, using unmanned aerial vehicle (UAV)-taken photographs of a sugar beet plot, finding a reasonably good agreement with chlorophyll-meter data for all indices, in particular for I2 and (R−B)/(R+G+B). Consequently, the suggested RGB indices may hold promise for inexpensive chlorophyll estimation in sugar beet leaves during the harvest time, although a direct relationship with nitrogen status still needs to be validated.

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“…Kazmi et al [5] addressed the problem of detecting thistle in sugar beet fields. Classification of species was performed using Mahalanobis Distance and Linear Discriminant Analysis.…”

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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Detecting creeping thistle in sugar beet fields using vegetation indices

Cited by 66 publications

References 27 publications

Deep convolutional neural networks for image-based Convolvulus sepium detection in sugar beet fields

Deep convolutional neural networks for image-based Convolvulus sepium detection in sugar beet fields

Assessment of RGB Vegetation Indices to Estimate Chlorophyll Content in Sugar Beet Leaves in the Final Cultivation Stage

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

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