Weed segmentation using texture features extracted from wavelet sub-images

Bakhshipour, Adel; Jafari, Abdolabbas; Nassiri, Seyed Mehdi; Zare, Dariush

doi:10.1016/j.biosystemseng.2017.02.002

Cited by 157 publications

(84 citation statements)

References 34 publications

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“…One may wonder how these classification results compare toward the literature on weed detection in less dense culture cited in the introduction section [12][13][14][15][16][17][18][19][20][21]. The performance in this literature varies from 75% to 99% of good detection of weed.…”

Section: Discussionmentioning

confidence: 93%

“…Bayesian classifier was used in [16] for plant and weed discrimination. Shape, texture features [12,[17][18][19] or wavelet transform [20,21] coupled with various classifiers including support vector machine (SVM), relevance vector machine (RVM), fuzzy classifier, or random forests were also shown to provide successful pipelines to discriminate between plant and weeds.…”

Section: Introductionmentioning

confidence: 99%

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Supervised Image Classification by Scattering Transform with Application to Weed Detection in Culture Crops of High Density

et al. 2019

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In this article, we assess the interest of the recently introduced multiscale scattering transform for texture classification applied for the first time in plant science. Scattering transform is shown to outperform monoscale approaches (gray-level co-occurrence matrix, local binary patterns) but also multiscale approaches (wavelet decomposition) which do not include combinatory steps. The regime in which scatter transform also outperforms a standard CNN architecture in terms of data-set size is evaluated (10 4 instances). An approach on how to optimally design the scatter transform based on energy contrast is provided. This is illustrated on the hard and open problem of weed detection in culture crops of high density from the top view in intensity images. An annotated synthetic data-set available under the form of a data challenge and a simulator are proposed for reproducible science. Scatter transform only trained on synthetic data shows an accuracy of 85% when tested on real data.3 of 16 the weeds included in the data-set Figure 3 are much more indented. Therefore, this computer vision problem is well adapted to test scatter transform which is a texture-based technique. Camera High density cropRemote Sens. 2019, 11, 249 7 of 16 no other anatomical items larger than the leaf itself. The number of layers tested was up to M = 4 as proposed in [3] since the energy after some layers although none zero is logically vanishing.Interestingly, these specific properties of the scatter transform match the intrinsic multiscale textural 127 nature of our weed detection problem which therefore constitutes an appropriate use case to assess 128 the potential of the scatter transform in practice. A visualization of output images for various filter 129 scale j at m = 2 for a given orientation is shown in Fig. 7. It clearly appears in Fig. 7 that the various 130 scales (texture of the limb and veins at j=3, border shape at j=4 and global leaf shape at j=8-not shown) 131 presented in section 2.2 can be captured with the different scaling factor applied on the wavelet. In our 132 study, we empirically picked L = 8 orientations and investigated up to J = 8 scales since there are 133 no other anatomical items larger than the leaf itself. The number of layers tested was up to M = 4 as 134 proposed in [3] since the energy after some layers although none zero is logically vanishing. 135

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Section: Discussionmentioning

confidence: 93%

Section: Introductionmentioning

confidence: 99%

Supervised Image Classification by Scattering Transform with Application to Weed Detection in Culture Crops of High Density

et al. 2019

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“…Segmenting plant leaves in the outdoors poses a unique challenge compared with the urban or interior environment commonly used for image segmentation [15]. Obtaining a strict leaf boundary is difficult because of the dynamic illumination conditions, leaf occlusion, and geometric variability between individual leaves [16].…”

Section: Data Preprocessingmentioning

confidence: 99%

Estimating Maize-Leaf Coverage in Field Conditions by Applying a Machine Learning Algorithm to UAV Remote Sensing Images

Zhou

et al. 2019

Applied Sciences

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Leaf coverage is an indicator of plant growth rate and predicted yield, and thus it is crucial to plant-breeding research. Robust image segmentation of leaf coverage from remote-sensing images acquired by unmanned aerial vehicles (UAVs) in varying environments can be directly used for large-scale coverage estimation, and is a key component of high-throughput field phenotyping. We thus propose an image-segmentation method based on machine learning to extract relatively accurate coverage information from the orthophoto generated after preprocessing. The image analysis pipeline, including dataset augmenting, removing background, classifier training and noise reduction, generates a set of binary masks to obtain leaf coverage from the image. We compare the proposed method with three conventional methods (Hue-Saturation-Value, edge-detection-based algorithm, random forest) and a frontier deep-learning method called DeepLabv3+. The proposed method improves indicators such as Qseg, Sr, Es and mIOU by 15% to 30%. The experimental results show that this approach is less limited by radiation conditions, and that the protocol can easily be implemented for extensive sampling at low cost. As a result, with the proposed method, we recommend using red-green-blue (RGB)-based technology in addition to conventional equipment for acquiring the leaf coverage of agricultural crops.

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“…In [21], authors used texture features extracted from wavelet sub-images to detect and characterize four types of weeds in a sugar beet field. Neural networks have been applied as classifier.…”

Section: Related Workmentioning

confidence: 99%

Deep Learning with Unsupervised Data Labeling for Weeds Detection on UAV Images

Bah¹,

Hafiane²,

Canals³

2018

Preprint

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In modern agriculture, usually weeds control consists in spraying herbicides all over the agricultural field. This practice involves significant waste and cost of herbicide for farmers and environmental pollution. One way to reduce the cost and environmental impact is to allocate the right doses of herbicide at the right place and at the right time (Precision Agriculture). Nowadays, Unmanned Aerial Vehicle (UAV) is becoming an interesting acquisition system for weeds localization and management due to its ability to obtain the images of the entire agricultural field with a very high spatial resolution and at low cost. Despite the important advances in UAV acquisition systems, automatic weeds detection remains a challenging problem because of its strong similarity with the crops. Recently Deep Learning approach has shown impressive results in different complex classification problem. However, this approach needs a certain amount of training data but, creating large agricultural datasets with pixel-level annotations by expert is an extremely time consuming task. In this paper, we propose a novel fully automatic learning method using Convolutional Neuronal Networks (CNNs) with unsupervised training dataset collection for weeds detection from UAV images. The proposed method consists in three main phases. First we automatically detect the crop lines and using them to identify the interline weeds. In the second phase, interline weeds are used to constitute the training dataset. Finally, we performed CNNs on this dataset to build a model able to detect the crop and weeds in the images. The results obtained are comparable to the traditional supervised training data labeling. The accuracy gaps are 1.5% in the spinach field and 6% in the bean field.

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Weed segmentation using texture features extracted from wavelet sub-images

Cited by 157 publications

References 34 publications

Supervised Image Classification by Scattering Transform with Application to Weed Detection in Culture Crops of High Density

Supervised Image Classification by Scattering Transform with Application to Weed Detection in Culture Crops of High Density

Estimating Maize-Leaf Coverage in Field Conditions by Applying a Machine Learning Algorithm to UAV Remote Sensing Images

Deep Learning with Unsupervised Data Labeling for Weeds Detection on UAV Images

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