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Proceedings ArticleDOI

Novel Method for Weed Classification in Maize Field Using Otsu and PCA Implementation

01 Feb 2015-pp 534-537
TL;DR: The combination of Otsu method and the PCA enable us to not only detect weed in crop rows but also classify this weed from crop, better suited for the real time applications pertaining to weed detection.
Abstract: This paper proposes two methods, oriented to crop row detection in images from agriculture fields with high weed pressure and to further distinguish between weed and crop. Firstly, for crop row detection the image processing consists of three main processes: image segmentation, double thresholding based on the 3D-Otsu's method, and crop row detection. Secondly, further classification between weed and crop, is carried out by compressing the three dimension vectors of an image to one dimension using the principal component analysis (PCA) method. Finally the combination of Otsu method and the PCA enable us to not only detect weed in crop rows but also classify this weed from crop. Hence it is better suited for the real time applications pertaining to weed detection.
Citations
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Journal ArticleDOI
TL;DR: Wavelet texture features were examined to verify their potential in weed detection in a sugar beet crop and demonstrated that they were able to effectively discriminate weeds among the crops even when there was significant amount of occlusion and leaves overlapping.

108 citations

Journal ArticleDOI
TL;DR: The proposed segmentation method provides an effective and robust segmentation means for sorting and grading apples in cucumber disease diagnosis, and it can be easily adapted for other imaging-based agricultural applications.

81 citations

Journal ArticleDOI
01 Mar 2020
TL;DR: This article provides a mini-review of all the different emerging and popular weed detection techniques for selective spraying, and summarizes the trends in this area in the past several years.
Abstract: Weed detection systems are important solutions to one of the existing agricultural problems—unmechanized weed control. Weed detection also helps provide a means of reducing or eliminating herbicide use, mitigating agricultural environmental and health impact, and improving sustainability. Deep learning-based techniques are replacing traditional machine learning techniques to detect weeds in real time with the development of new models and increasing computational power. More hybrid machine learning models are emerging, utilizing benefits from different techniques. More large-scale crop and weed image datasets are available online now, and this provides more data and opportunities for researchers and engineers to join and contribute to this field. This article provides a mini-review of all the different emerging and popular weed detection techniques for selective spraying, and summarizes the trends in this area in the past several years.

79 citations

Journal ArticleDOI
Shihan Mao1, Yuhua Li1, You Ma1, Baohua Zhang1, Jun Zhou1, Kai Wang1 
TL;DR: A novel cucumber region detection method using multi-path convolutional neural network (MPCNN), combined with color component selection and support vector machine (SVM), demonstrating the satisfactory performance of the proposed method and highlighting its promising applications in mechanical cucumber harvesting.

51 citations

Journal ArticleDOI
TL;DR: In this article , a vision-based weed detection system using deep learning models that effectively detect weed within a soybean plantation was proposed, including MobileNetV2, ResNet50, and three custom Convolutional Neural Network (CNN) Models.
Abstract: Weed detection has become an integral part of precision farming that leverages the IoT framework. Weeds have become responsible for 45% of the agriculture industry's crop losses due mainly to the competition with crops. An efficient weed detection method can reduce this percentage. This paper proposes a vision-based weed detection system using deep learning models that effectively detect weed within a soybean plantation. Five deep learning models are used, including MobileNetV2, ResNet50, and three custom Convolutional Neural Network (CNN) Models. The MobileNetV2 and ResNet50 were deployed on a Raspberry PI controller for comparison purposes. Based on a dataset with 400 images and 1536 total segments, the custom 5-layer CNN architecture shows high detection accuracy of 97.7% and the lowest latency & memory usage with 1.78 GB and 22.245 ms respectively. Utilizing the proposed custom deep learning CNN model with high accuracy can positively impact efficiency, time, and overall production within the soybean industry.

28 citations

References
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Journal ArticleDOI

37,017 citations


"Novel Method for Weed Classificatio..." refers methods in this paper

  • ...The previous color vegetation indices (also called color indices) used are color space transformations from RGB to a space onedimensional[1-9]....

    [...]

  • ...Here a fast three-dimensional 3-D Otsu’s thresholding algorithm is proposed wherein a 3-D observation space is first constructed[1-16]....

    [...]

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TL;DR: 1. Fundamentals of Image Processing, 2. Intensity Transformations and Spatial Filtering, and 3. Frequency Domain Processing.
Abstract: 1. Introduction. 2. Fundamentals. 3. Intensity Transformations and Spatial Filtering. 4. Frequency Domain Processing. 5. Image Restoration. 6. Color Image Processing. 7. Wavelets. 8. Image Compression. 9. Morphological Image Processing. 10. Image Segmentation. 11. Representation and Description. 12. Object Recognition.

6,306 citations


"Novel Method for Weed Classificatio..." refers methods in this paper

  • ...The previous color vegetation indices (also called color indices) used are color space transformations from RGB to a space onedimensional[1-9]....

    [...]

  • ...Here a fast three-dimensional 3-D Otsu’s thresholding algorithm is proposed wherein a 3-D observation space is first constructed[1-16]....

    [...]

Journal ArticleDOI
TL;DR: In this article, an improved vegetation index, Excess Green minus Excess Red (ExG-ExR) was compared to the commonly used ExG, and the normalized difference (NDI) indices.

646 citations

Journal ArticleDOI
TL;DR: A review of the current status of the four core technologies (guidance, detection and identification, precision in-row weed control, and mapping) required for the successful development of a general-purpose robotic system for weed control can be found in this article.

630 citations


"Novel Method for Weed Classificatio..." refers background in this paper

  • ...It is sensitive to the relative scaling of the original variables [16-21]....

    [...]

  • ...The purpose of PCA is to present the information of original data as the linear combination of certain linear irrelevant variables[18-21] ....

    [...]

Journal ArticleDOI
TL;DR: A new automatic approach is proposed for segmenting these main textures of green plants, soil and sky and also to refine the identification of sub-textures inside the main ones, that exploits the performance of existing strategies by combining them.

264 citations


"Novel Method for Weed Classificatio..." refers background in this paper

  • ...Based on geometrical considerations and on the knowledge about the system configuration, a template is previously built, containing those parts where crop lines are expected....

    [...]