Automatic Crop Furrow Detection for Precision Agriculture

Using color histograms in automatic emotion recognition systems faces different issues. One of the important challenges is to determine the appropriate number of bins in the color histogram to achieve the highest recognition performance possible with minimal computations. This research focuses on emotion recognition induced by visual contents of images, or REVC for short, using ARTphoto dataset. Twenty-two different classifiers are used with color histograms in both RGB (red, green, blue) and HSV (hue, saturation, value) color spaces across different numbers of bins, and overall performance of each bin size is compared with that of other bin sizes. The research findings show that the performance of REVC system does not improve in terms of overall sensitivity rate, when the number of bins in color histogram is increased. Moreover, this paper identifies the advantage of using HSV color space over RGB in using color histogram for REVC systems. Furthermore, findings recognize the optimum number of bins in both RGB and HSV color spaces, and ANOVA (analysis of variance) is used to analyze experimental data, which identifies the optimum color histogram bin size used for HSV color space is significantly better than that used for RGB color space in REVC systems.

Crop rows and weeds detection in maize fields applying a computer vision system based on geometry

Machine vision-based navigation in the maize field is significant for intelligent agriculture. Therefore, precision detection of the tasseled crop rows for navigation of agricultural machinery with an accurate and fast method remains an open question. In this article, we propose a new crop rows detection method at the tasseling stage of maize fields for agrarian machinery navigation. The whole work is achieved mainly through image augment and feature point extraction by micro-region of interest (micro-ROI). In the proposed method, we first augment the distinction between the tassels and background by the logarithmic transformation in RGB color space, and then the image is transformed to hue-saturation-value (HSV) space to extract the tassels. Second, the ROI is approximately selected and updated using the bounding box until the multiple-region of interest (multi-ROI) is determined. We further propose a feature points extraction method based on micro-ROI and the feature points are used to calculate the crop rows detection lines. Finally, the bisector of the acute angle formed by the two detection lines is used as the field navigation line. The experimental results show that the algorithm proposed has good robustness and can accurately detect crop rows. Compared with other existing methods, our method's accuracy and real-time performance have improved by about 5 and 62.3%, respectively, which can meet the accuracy and real-time requirements of agricultural vehicles' navigation in maize fields.

Low-light images suffer from severe noise, low brightness, low contrast, etc. In previous researches, many image enhancement methods have been proposed, but few methods can deal with these problems simultaneously. In this paper, to solve these problems simultaneously, we propose a low-light image enhancement method that can be combined with supervised learning and previous HSV (Hue, Saturation, Value) or Retinex model-based image enhancement methods. First, we analyse the relationship between the HSV color space and the Retinex theory, and show that the V channel (V channel in HSV color space, equals the maximum channel in RGB color space) of the enhanced image can well represent the contrast and brightness enhancement process. Then, a data-driven conditional re-enhancement network (denoted as CRENet) is proposed. The network takes low-light images as input and the enhanced V channel (V channel of the enhanced image) as a condition during testing, and then it can re-enhance the contrast and brightness of the low-light image and at the same time reduce noise and color distortion. In addition, it takes 23 ms to process a color image with the resolution 400*600 on a 1080Ti GPU. Finally, some comparative experiments are implemented to prove the effectiveness of the method. The results show that the method proposed in this paper can significantly improve the quality of the enhanced image, and by combining it with other image contrast enhancement methods, the final enhancement result can even be better than the reference image in contrast and brightness when the contrast and brightness of the reference are not good.

Recently, compressive sensing (CS) schemes have been studied as a new compression modality that exploits the sensing matrix in the measurement scheme and the reconstruction scheme to recover the compressed signal. In addition, CS is exploited in medical imaging (MI) to support efficient sampling, compression, transmission, and storage of a large amount of MI. Although CS of MI has been extensively investigated, the effect of color space in CS of MI has not yet been studied in the literature. To fulfill these requirements, this article proposes a novel CS of MI based on hue-saturation value (HSV), using spread spectrum Fourier sampling (SSFS) and sparsity averaging with reweighted analysis (SARA). An HSV loop that performs SSFS is proposed to obtain a compressed signal. Next, HSV–SARA is proposed to reconstruct MI from the compressed signal. A set of color MIs is investigated, such as colonoscopy, magnetic resonance imaging of the brain and eye, and wireless capsule endoscopy images. Experiments were performed to show the superiority of HSV–SARA over benchmark methods in terms of signal-to-noise ratio (SNR), structural similarity (SSIM) index, and measurement rate (MR). The experiments showed that a color MI, with a resolution of pixels, could be compressed by the proposed CS at MR of 0.1, and could be improved in terms of SNR being 15.17% and SSIM being 2.53%. The proposed HSV–SARA can be a solution for color medical image compression and sampling to improve the image acquisition of medical devices.

Nowadays understanding of image processing and pattern recognition is the great focus to develop an application of image segmentation. Edge detection is the fundamental technique that can provide information of the boundaries in different objects in an image. Selecting a suitable edge detection algorithm is important to obtain the best performance of segmentation. This paper presents an analysis of implementation for various edge detection techniques on color spaces. We implement Sobel, Prewitt, Robert, and Laplacian edge detection on Hue, Saturation, Value (HSV) and YUV color space by using threshold = 65. We calculated the mean square error (MSE), root mean square error (RMSE), peak signal to noise ratio (PSNR) and execution time as a comparison. This paper tested images from Berkeley Segmentation Dataset (BSDS). The results showed that Robert edge detection in HSV color space especially hue channel can be utilized to obtain more precise boundaries in different objects and it is also quick to compute.

PurposeTo show feasibility of computerized techniques for ocular redness quantification in clinical studies, and to propose an automatic, objective method.MethodsSoftware for quantification of redness of the bulbar conjunctiva was developed. It provides an interface for manual and automatic sclera segmentation along with automated alignment of region of interest to enable estimation of changes in redness. The software also includes the redness scoring methods: (1) contrast-limited adaptive histogram equalization (CLAHE) in red-green-blue (RGB) color model, (2) product of saturation and hue in hue-saturation-value (HSV), and (3) average of angular sections in HSV. Our validation pipeline compares the scoring outcomes from the perspectives of segmentation reliability, segmentation precision, segmentation automation, and the choice of redness scoring methods.ResultsNinety-two photographs of eyes before and after provoked redness were evaluated. Redness in manually segmented images was significantly different within human observers (interobserver, P = 0.04) and two scoring sessions (intraobserver, P < 0.001). Automated segmentation showed the smallest variability, and can therefore be seen as a robust segmentation method. The RGB-based scoring method was less sensitive in redness assessment.ConclusionsComputation of ocular redness depends heavily on sclera segmentation. Manual segmentation appears to be subjective, resulting in systematic errors in intraobserver and interobserver settings. At the same time, automatic segmentation seems to be consistent. The scoring methods relying on HSV color space appeared to be more consistent.Translational RelevanceComputerized quantification of ocular redness holds great promise to objectify ocular redness in the standard clinical care and, in particular, in clinical trials.

Crop-row detection algorithm based on Random Hough Transformation

Fire and smoke are common sight at mines and could be catastrophic for humans working there. Our paper focuses on an alert mechanism that is built based on detection and validation of smoke from mines. We propose a computer vision based model that is robust enough to detect presence of smoke and the direction of motion of smoke. This research paper proposes a smoke detection model integrated with Unmanned Aerial Vehicle (UAV). The model focuses on smoke detection that leads to early detection of fire. The model works on the basis of image processing. The distinguishing feature of our model is the dynamic and real time processing. Here the system tracks the dynamic environment in the mining areas using UAVs featured with cameras. The real time captured video frames are calibrated to overcome distortion considering the radial and tangential factors. The calibrated frame thus obtained is further processed to get the Region of Interest (ROI) based on the spatial Hue Saturation Value (HSV) colouring model. Each pixel of the frame is then compared with colour range of smoke using opencv functions. The matched ROI is de-noised using Non-local means De-nosing algorithm to avoid unnecessary colours. These filtered frames are then processed concurrently to identify the pattern of change in motion. Based on this pattern the ROI is classified as either smoke or no smoke. With the analysis of turbulence in each pixel the intensity of smoke is detected. The experiment is conducted with-1) Prerecorded smoke video clippings. 2) Real time video captured in the camera. The results turned out to be positive in both the test cases. So, the model works quite efficiently with both recorded and real time video. Key Terms: UAV, ROI, HSV, De-noising algorithm, pattern matching

Objective: To develop a system that will optimize storage, automatically classify and tag images using image content and image context by using classification techniques and construct image archive to enable enhanced reference and inference with a simple query. Methods: The approach uses both image processing and data mining techniques. Multi modal processing of the Human brain has the ability to distinguish the components in an image and understand the content instantly. To automate this processing ability by the Computer, Computer Vision attempts various techniques. Features that describe the objects of interest are extracted by converting the image into Hue, Saturation, Value (HSV) space. Further segmentation, profiling and thresholding techniques are applied. Finally K Nearest Neighbours (KNN) classifier has been used for classifying the images. Hierarchical decision tree has been built by uniquely identifying classes that could be used for tagging. Findings: Two levels of classification have been addressed in this paper. Statistical features are extracted from the H-plane of the HSV color space and with an appropriate threshold obtained from the histogram, images have been classified into indoor and outdoor images at the first level. Further the indoor images are classified using segmentation and profiling, as classes of images with presentation and without presentation. Applications/Improvements: Images can be segmented based on more image content that signify the event by watermarking technique and by extracting more features. These segments could further be used to build the decision trees that will identify distinct classes. The depth of the decision tree will enhance indexing, tagging and improve the efficiency of query based image retrieval.

Automated robust crop-row detection in maize fields based on position clustering algorithm and shortest path method

The arterial blood sample collector produced in large quantities often fails to meet the requirements due to missing components in the packaging bag, and traditional manual detection methods are both inefficient and inaccurate. To solve this problem, a PyCharm-integrated development environment was used to study image processing and recognition algorithms for identifying components inside the packaging bag of the HN-3 arterial blood sample collector. The machine vision system was used to capture images of the packaging bags of the HN-3 Arterial blood sample collector. Template matching was employed to extract the packaging ROI, and the threshold segmentation method in the HSV color model was used to extract material features based on the packaging ROI. Morphological processing algorithms such as dilation or erosion were used to enhance the connectivity of the extracted features. The existence of components was determined by setting thresholds for the connected domain area or length. The results of the recognition experiment show that the false detection rate is 0.2%, the missed detection rate is 0%, and the average image processing time per product is no more than 39 ms. Compared with manual recognition methods, the efficiency and accuracy have been improved by 36.5 times and 2.3%, respectively. The experimental results confirm the effectiveness of the image processing algorithm.

Traffic Sign Recognition (TSR) system is a significant component of Intelligent Transport System (ITS) as traffic signs assist the drivers to drive more safely and efficiently. This paper represents a new approach for TSR system where detection of traffic sign is carried out using fuzzy rules based color segmentation method and recognition is accomplished using Speeded Up Robust Features(SURF) descriptor, trained by artificial neural network (ANN) classifier. In the detection step, the region of interest (sign area) is segmented using a set of fuzzy rules depending on the hue and saturation values of each pixel in the HSV color space, post processed to filter unwanted region. Finally the recognition of the traffic sign is implemented using ANN classifier upon the training of SURF features descriptor. The proposed system simulated on offline road scene images captured under different illumination conditions. The detection algorithm shows a high robustness and the recognition rate is quite satisfactory. The performance of the ANN model is illustrated in terms of cross entropy, confusion matrix and receiver operating characteristic (ROC) curves. Also, performances of some classifier such as Support Vector Machine (SVM), Decision Trees, Ensembles Learners (Adaboost) and K-Nearest Neighbor (KNN) classifier are assessed with ANN approach. The simulation results illustrate that recognition using ANN model is higher than classifiers stated above.

The method of fuzzy color recognition and segmentation of vision scene of robot based on the combination of fuzzy color quantization and edge extraction of HSV (hue saturation value) color space was proposed in the article. In the method the color would be quickly recognized fuzzily as less than ten natural standard colors according to human visual perception and the recognized image will be edge extracted. Integrating with color and edge information, the area segmentation will be conducted to the image, realizing color fuzzy recognition and segmentation and setting up color index map. As the experimental results and the subjective perception of human have reached good consistency, it improves color recognition speed and perception speed of the environment of the robot.

Weed control is a substantial challenge in field management. A better weed control method at an earlier growth stage is important for increasing yields. As a promising weed control technique, intelligent weeding based on machine vision can avoid the harm of chemical weeding. For machine vision, it is critical to extract and segment crops from their background. However, there is still no optimal solution for object tracking with occlusion under a similar color background. In this study, it was found that the gray distribution of a maize canopy follows the gradient law. Therefore, the recognition method based on the HLS-SVM (HLS color space and Support Vector Machine) and on the grayscale gradient was developed. First, the HLS color space was used to detect the maize canopy. Second, the SVM method was used to segment the central region of the maize canopy. Finally, the maize canopy center was identified according to the gradient law. The results showed that the average segmentation time was 0.49 s, the average segmentation quality was 87.25%, and the standard deviation of the segmentation was 3.57%. The average recognition rate of the center position was 93.33%. This study provided a machine vision method for intelligent weeding agricultural equipment as well as a theoretical reference for further agricultural machine vision research.