A review on weed detection using ground-based machine vision and image processing techniques

A feature selection method for online detection of cotton foreign fiber is presented.The proposed method efficiently finds the excellent subset in multi-character feature sets.The selected sets by proposed method efficiently reduce the time of online detection.The proposed method improves the performance of industrial equipment. Feature selection plays an important role in the machine-vision-based online detection of foreign fibers in cotton because of improvement detection accuracy and speed. Feature sets of foreign fibers in cotton belong to multi-character feature sets. That means the high-quality feature sets of foreign fibers in cotton consist of three classes of features which are respectively the color, texture and shape features. The multi-character feature sets naturally contain a space constraint which lead to the smaller feature space than the general feature set with the same number of features, however the existing algorithms do not consider the space characteristic of multi-character feature sets and treat the multi-character feature sets as the general feature sets. This paper proposed an improved ant colony optimization for feature selection, whose objective is to find the (near) optimal subsets in multi-character feature sets. In the proposed algorithm, group constraint is adopted to limit subset constructing process and probability transition for reducing the effect of invalid subsets and improve the convergence efficiency. As a result, the algorithm can effectively find the high-quality subsets in the feature space of multi-character feature sets. The proposed algorithm is tested in the datasets of foreign fibers in cotton and comparisons with other methods are also made. The experimental results show that the proposed algorithm can find the high-quality subsets with smaller size and high classification accuracy. This is very important to improve performance of online detection systems of foreign fibers in cotton.

Feature selection based on improved ant colony optimization for online detection of foreign fiber in cotton

This paper proposes a new method, oriented to crop row detection in images from maize fields with high weed pressure. The vision system is designed to be installed onboard a mobile agricultural vehicle, i.e. submitted to gyros, vibrations and undesired movements. The images are captured under image perspective, being affected by the above undesired effects. The image processing consists of three main processes: image segmentation, double thresholding, based on the Otsu's method, and crop row detection. Image segmentation is based on the application of a vegetation index, the double thresholding achieves the separation between weeds and crops and the crop row detection applies least squares linear regression for line adjustment. Crop and weed separation becomes effective and the crop row detection can be favorably compared against the classical approach based on the Hough transform. Both gain effectiveness and accuracy thanks to the double thresholding that makes the main finding of the paper.

Automatic detection of crop rows in maize fields with high weeds pressure

This paper proposes an automatic expert system for accuracy crop row detection in maize fields based on images acquired from a vision system. Different applications in maize, particularly those based on site specific treatments, require the identification of the crop rows. The vision system is designed with a defined geometry and installed onboard a mobile agricultural vehicle, i.e. submitted to vibrations, gyros or uncontrolled movements. Crop rows can be estimated by applying geometrical parameters under image perspective projection. Because of the above undesired effects, most often, the estimation results inaccurate as compared to the real crop rows. The proposed expert system exploits the human knowledge which is mapped into two modules based on image processing techniques. The first one is intended for separating green plants (crops and weeds) from the rest (soil, stones and others). The second one is based on the system geometry where the expected crop lines are mapped onto the image and then a correction is applied through the well-tested and robust Theil-Sen estimator in order to adjust them to the real ones. Its performance is favorably compared against the classical Pearson product-moment correlation coefficient.

Automatic expert system based on images for accuracy crop row detection in maize fields

A two-stage feature selection method with its application

https://www.tpbin.com/Uploads/Subjects/f709c33d-147a-4d16-ac2c-b22f9dca1509.pdf

Crop-row detection algorithm based on Random Hough Transformation

Classification and identification of foreign fibers in cotton on the basis of a support vector machine

The invention provides a method for detecting the quality of cotton. The method provided by the invention comprises the following steps: optimum combination wave band selection: detecting the optimum detection thickness and optimum combination wave band of cotton on the basis of multi-element statistic analysis; and image direction: performing image detection on foreign fibers in the optimum detection thickness detected in the step of optimum combination wave band selection so as to detect the foreign fibers in the cotton. By the adoption of the method for detecting the quality of the cotton provided by the invention, the foreign fibers in a cotton layer can be detected; different types of foreign fibers are effectively distinguished; the multi-spectral characteristics of the foreign fibers in a cotton layer can be determined, thereby establishing an theoretical base for the detection based on the multi-spectral characteristics of the foreign fibers; and various foreign fibers in the cotton layer can be detected; and the method better meets actual conditions of foreign fiber distribution in the cotton processing process and has high practical value.

Method for detecting quality of cotton

Mark-Spectra: A convolutional neural network for quantitative spectral analysis overcoming spatial relationships

Simple Summary Forecasting rabbit house environmental variables is critical to achieving intensive rabbit breeding and rabbit house environmental regulation. As a result, this paper proposes a decomposition-based multi-step forecasting model for rabbit houses using a time series decomposition algorithm and a deep learning combinatorial model. The experimental results demonstrated that the proposed method could provide accurate decisions for rabbit house environmental regulation. Abstract To improve prediction accuracy and provide sufficient time to control decision-making, a decomposition-based multi-step forecasting model for rabbit house environmental variables is proposed. Traditional forecasting methods for rabbit house environmental parameters perform poorly because the coupling relationship between sequences is ignored. Using the STL algorithm, the proposed model first decomposes the non-stationary time series into trend, seasonal, and residual components and then predicts separately based on the characteristics of each component. LSTM and Informer are used to predict the trend and residual components, respectively. The aforementioned two predicted values are added together with the seasonal component to obtain the final predicted value. The most important environmental variables in a rabbit house are temperature, humidity, and carbon dioxide concentration. The experimental results show that the encoder and decoder input sequence lengths in the Informer model have a significant impact on the model’s performance. The rabbit house environment’s multivariate correlation time series can be effectively predicted in a multi-input and single-output mode. The temperature and humidity prediction improved significantly, but the carbon dioxide concentration did not. Because of the effective extraction of the coupling relationship among the correlated time series, the proposed model can perfectly perform multivariate multi-step prediction of non-stationary time series.

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Ronghua Ji

Papers

Crop-row detection algorithm based on Random Hough Transformation

Classification and identification of foreign fibers in cotton on the basis of a support vector machine

Method for detecting quality of cotton

Mark-Spectra: A convolutional neural network for quantitative spectral analysis overcoming spatial relationships

Decomposition-Based Multi-Step Forecasting Model for the Environmental Variables of Rabbit Houses