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Droplets deposition characteristics estimation is helpful to know the pesticides deposition distribution on crops, which is related to the crop spray quality directly, and especially the fast detection method will provide the basis for improving the pesticides spraying technology. Droplets image processing based on the water sensitive paper is one of the most common methods to detect the droplets deposition characteristics. The droplets coverage percentage and coverage density were selected to evaluate deposition distribution in this paper. With the development of science and technology, a smart phone was selected as image acquiring tool to replace the special image acquisition system, and the improved image processing algorithm of the water sensitive paper was developed. There are 3 steps in the image processing algorithm to obtain each droplet stain, which are image preprocessing, droplets area segmentation, and overlapped droplets segmentation. Firstly, the image was enhanced and calibrated by a calibration board, and then the image of water sensitive paper was segmented from the R channel and B channel image and transferred to a gray scale image. Secondly, the blue droplets stain area was segmented from yellow paper background, and the dynamic threshold method based on the pixel position was used to solve the problem of the influence of uneven brightness in this step, in which the gray mean value of an area of 25×25 pixels was calculated as the segmentation threshold for the middle pixel, and the droplets coverage percentage was calculated by the stains pixel number divided by the pixel number of water sensitive paper area. Thirdly, the circulatory segmentation method based on region circularity was designed to segment the multiple-droplet overlapped regions. Based on the statistical analysis, 0.6 was selected as the circularity threshold, less than 0.6 was considered to be overlapped droplets, and first erosion and then dilation based on a circle with the radius r were performed; next the segmented area circularity was recalculated, the area with the circularity of greater than 0.6 was selected again and segmented by the circle with the radius (r+1), and the erosion-dilation operation would be repeated over and over until the circularity of all stains was greater than 0.6. Finally, the identified droplets were marked as circle, and the droplets coverage density was calculated by droplets number divided by the water sensitive paper area. Experiments were conducted to test and verify the detection advancement of the proposed image processing algorithm. Experimental results showed that the dynamic threshold segmentation method is not affected by the uneven brightness and can extract 92.64% of droplets, and the droplet coverage percentage detection result is 12.57% and 8.74% greater than constant threshold and partitioned threshold method respectively. Moreover, the proposed overlapped droplets segmentation algorithm can segment successfully more than 2 droplets and the long and thin droplets, the accuracy of droplets identification is 97.2%, and the coverage density detection results showed that the relative error between the algorithm in this paper and manual counting is only 3.31%. The results indicated that the proposed image detection algorithm of droplets deposition characteristics is efficient and convenient, and can completely fulfill the demand of droplets deposition characteristics detection in the field, and the corresponding smartphone applications are in development. [ABSTRACT FROM AUTHOR]

Ochotona curzoniae is an endemic species and key species in the alpine meadow of the Tibetan Plateau and also it is a main kind of organism that destroys the grassland ecology of the plateau. In order to prevent the dangers of the ochotona curzoniae, we need to study the living habits of ochotona curzoniae and investigate the degree of harm of ochotona curzoniae, and then we can control the number of ochotona curzoniae through the effective preventive measures. With the development of sensing technology and image processing, we can provide an objective basis through intelligent monitoring system to control the damage of ochotona curzoniae. The object detection of ochotona curzoniae is a key technology in the intelligent monitoring equipment because it can provide the object contour feature for behavior analysis of ochotona curzoniae. The object detection of ochotona curzoniae is very difficult, because the ochotona curzoniae video possesses the characteristics of complex background, low contrast, the object color with intensity inhomogeneity, diversity and mutability. The traditional object detection method cannot extract the object contours accurately. This paper presents a fast object detection method based on space-time domain. Firstly, the centroid position of the object in the current frame image is determined by the background subtraction, and then the rough segmented image and the initial contour are obtained based on the centroid position. The rough segmented image is segmented by the improved Chan-Vese model, and then we can obtain the object contours. In view of the fact that the level set function needs to be initialized in the process of improved Chan-Vese model, and the initialized computation is enhanced with the increase of the image scale, the centroid of the object is taken as the center to intercept the image containing the object as the roughly segmented image. Then, the improved Chan-Vese model is used to segment the roughly segmented image, so as to reduce the time consumption of Chan-Vese model segmentation. In addition, as Chan-Vese model can’t fully segment the image of object whose color is diverse and mutable, we use the improved Chan-Vese model to segment the roughly segmented image. The internal pixels of image evolution contours were processed by K-means clustering, and the clustering center point values were obtained. The internal fitting values of Chan-Vese model were constructed by the clustering center point values and the image mean filtered intensity information, thereby improving the adaptability of Chan-Vese model for complex object image segmentation．In addition, rectangular Dirac function was used to replace regularized Dirac function in the energy function of Chan-Vese model, and the calculation of level set evolution equation could be limited to the zero level set so as to avoid the influence of the image background disturbance on the segmentation result. In this paper, the video processing with 50 frames of images shows that the time consumption of this method is only 15.25 s, the average value of Dice similarity coefficient is 0.852 929, and the average value of Jaccard index is 0.744 57. In summary, the object detection method proposed in this paper can accurately extract the object contour and has a high real-time performance. [ABSTRACT FROM AUTHOR]

The breeding environment plays an important role in healthy growth and development of the pigs, and it is also a guarantee of the excellent traits. The growth of pig is the integrated result of their own traits and external environmental factors. For different environments, pigs will show different behaviors. It is found that the appropriate growth environment can be reflected by their degree of dispersion in the pens. When the environment is suitable, the pig group will lie down together. When the temperature is low, they will huddle together; and when the environmental temperature is high, the pigs will scatter around. Image segmentation is the important link to analyze pig behavior status based on computer vision technology, and pig image segmentation is an image representation that provides easy-to-understand and analysis for the behavior feature extraction, parameter measurement, image analysis, pattern recognition of individual pig. The accurate and effective image segmentation algorithm is for pig behavioral intelligent analysis, understanding and environmental intelligent decision. The traditional Graph Cut algorithm uses the energy function to compute all the pixels in the image, it takes a lot of time. It has the low segmentation efficiency and it cannot accurately segment specific pig. Aiming at the limitation of traditional Graph Cut algorithm and the characteristic of image itself, on the basis of the interactive watershed algorithm, in this paper, we proposed an interactive image segmentation method based on improved Graph Cut algorithm aiming at better use of interaction information provided by the user and effectively control of the number of super pixels. To some extent, the algorithm could also avoid the problems of over segmentation or under segmentation. The image was divided into regions based on the interactive watershed algorithm, and the foreground and background were specified. Each region block was regarded as the super pixel, the gray scale of super pixel was used as the vertex. Establish edges between adjacent vertices and the new network map was constructed instead of the traditional weighted graph. In order to reconstruct the energy function to complete the effective segmentation of the foreground and background, the maximum flow, and minimization cut algorithm were used to cut the image. Results of experiments showed that the method can effectively segment pig target, it had high segmentation quality and high efficiency. In this paper, the peak signal to noise ratio (PSNR) and structural similarity (SSIM) were used as objective evaluation standard. The range of the PSNR was [30, 40] and the range of the SSIM was [0.9, 1] in this method, which was the most accurate segmentation algorithm in the shown segmentation algorithm. It indicated that two evaluation criterions were consistent with the subjective evaluation, and the stability and the reliability of this method were improved. The average time consumed in this algorithm was only 33.7% of in the traditional Graph Cut algorithm. It greatly improved the efficiency of segmentation, and it had highly robust for the segmentation of specific target pigs. The results of this paper can provide technical support for subsequent target identification, detection, tracking and monitoring, evaluation of external environments, such as alarms. [ABSTRACT FROM AUTHOR]

The amount of wheat ears per unit area is an important parameter for assessing wheat yield and wheat planting density. Generally, phenotype parameters should be obtained by manual count technique which is time-consuming and needs great effort. Aiming at the traditional image segmentation method based on color feature, texture feature and Haar feature, in this paper, we proposed an improved K-means algorithm for estimating the number of wheat ears. This method established a direct mapping relationship from the low-level features of the image to the number of wheat ears in the image through the color feature so that the target did not need to be segmented or detected. This type of method was more suitable for complex lighting and dense wheat ears, and had higher computational efficiency than counting methods based on image segmentation. This method made full use of the color feature of wheat ear image, took the area feature of local region extracted from local region as the basis of wheat ear judgment, and used the number of sub-regions in clustering region as the estimation value of wheat ear number, thus avoiding the task of target detection and location, and greatly improving the accuracy of Wheat ear counting. According to classification results of K-means method, the wheat ear image was divided into three regions. The green area pixel value representing the wheat eat region was set to 255, the pixel value of other areas was set to 0, so the binary image of wheat ears was obtained. Most of the binarized areas in the image were wheat ears, and a small part of the binarized area that was too small or too large was caused by the leaves. Following the steps were used to count wheat ears: 1) extracting the connected regions of the binary image and labeling them; 2) calculating the area of each connected region, the area was represented by the number of pixels in the region; 3) using the following method to filter out the connected region where the area was too small or too large; 4) finally, the number of wheat ears was obtained by counting the retained binary regions. This method established a direct classification relation from the image color feature of the lower layer to the image containing wheat ears so that the image did not need to be segmented or detected. Based on color feature clustering, this method could estimate the number of wheat ears in local area of space, and it was more scalable without training. The average prediction precision of the total wheatear number in a wheatear image for 12 wheatear images was 94.69%. And the Statistical error of the total wheatear number was between 2.17% and 7.41%, the average statistical error was 5.31%. The statistical experiment results showed that the accuracy of this algorithm to wheat ear counting was better than the traditional method based on image color feature and texture feature. [ABSTRACT FROM AUTHOR]

The phenotypic traits of corn ear are important quantitative data in maize breeding and variety identification. In tradition, breeding workers are employed to deal with lots of corn ears by means of manual measurement and visual count, however this process is seriously labor-consuming and time-costing, and the measured traits are prone to be subjective and incomplete. In recent years, some semi-automatic systems based on machine vision and image analysis have been developed and applied to the maize variety test, however fully automated test system is still a challenge task owing to the strict high-throughput and high-precision requirements in large-scale maize breeding. To balance efficiency and accuracy of variety test for corn ears, in this paper, a high-throughput phenotypic measurement method and system based on panoramic surface image is proposed. Firstly, a novel mechanic system is proposed, which automatically conveys corn ears above a chain-roller structure, while the rolling corn ears are continuously imaged by a fixed industrial camera that is perpendicular to the moving plane of corn ear. In only several seconds, dozens of side images in which corn ears are in different positions can be collected to generate the image dataset of single corn ear. By analyzing the movement state of corn ear, a transformation model which describes the relationship among ear roll, camera imaging and surface position is then built to bridge the image sequence and the panoramic surface image of corn ear. Corn ears in the image sequence are respectively segmented and the center axes are dynamically determined by figuring out the shape and bounding box. This model always extracts the most appropriate sub regions of corn ear from image sequence, and then stitches them to the calculated positions on the panoramic surface image. As a result, the panoramic image of corn ear demonstrates the three-dimensional surface information in a two-dimensional image, and thus provides more intuitive and complete way for phenotyping calculation of corn ear. The valid surface region of corn ear in the panoramic image is further determined by the boundary detection technique that is performed by evaluating the perimeters of corn ear in the image sequence. Robust kernel segmentation based on hierarchical threshold method is also utilized to extract all candidate kernels which satisfy area and shape constraint, and some more restrictive filters based on machine learning methods, such as SVM (support vector machine), can also be taken to evaluate the validation of kernels. The segmented kernels in the panoramic image are used to calculate the total kernels, number of ear rows and kernels per row. The experimental results show that the proposed method and system can achieve optimized efficiency and accuracy balance. High-throughput convey mechanism improves the efficiency of image acquisition to 15 ears per minute. Compared with the methods based on single and multiple images, the variety test method based on panoramic surface image can make full use of the entire surface information of corn ear and reveal its individual phenotypic traits. The computation accuracies of ear length, ear diameter, number of ear rows, kernels per row and total kernels are up to 99%, 91.84%, 97.15%, 98.89% and 95.37% respectively. [ABSTRACT FROM AUTHOR]

Plant phenotype is a key link in understanding gene function and environmental effects, and with the demand of further research of plant function genomics, the traditional phenotypic observation has become the main bottleneck. High-throughput plant phenome analysis technology such as image processing is an effective way to solve this problem. In this paper, a maize plant drought stress phenotype testing method was proposed based on the time-series images. Firstly, an acquisition system of time-series images was developed. System hardware included the mvc3000 high speed industrial camera (the maximum resolution 1600×1200 pixels, 24 FPS (frames per second)), the Pentax len (8.5 mm, f/1.5), the storage card (32G), the holder and the background. Software development environment included Win7 operating system, Microsoft Visual Studio 2010 Professional, and OpenCV2.4.3. Maize plant time-series images were acquired from 08:00 to 17:00 every 10 mins, and various natural light conditions strongly affected the profile of the crop images taken outdoors. This often made it rather difficult for vegetation parts segmented from the background parts in an image. We adopted the K-means and SVM (support vector machine) algorithm to create a classifier used in rough segmentation, and the classifier was composed of 24 dimension feature vectors including color features, texture features, and gradient features. The result refinement was implemented with a non-local mean (NLM) filter, and actually the anisotropic structural-aware filter was adopted to implement the NLM function, in which the pixels were reinforced with each other in the same structure and even if they were not the first-order neighboring pixels. Secondly, the plant organs such as stem and leaf were segmented in binary image with a scan line method, which used the plant morphology characteristics such as alternate phyllotaxis, curved blade, and leaf margin in folds as prior knowledge. The method denoted the pixels according to the number of neighborhoods. Specifically, each row of pixels in the scan line was marked as a candidate region, and the candidate regions were labeled as the original organ or new organ according to the number of neighborhoods. When all of the candidate regions were labeled, a cluster of regions which presented rectangular shape were defined as plant stem. A labeled cluster of leaf would be merged to the other cluster if they shared neighborhood regions up to 70%. Finally, the ratio SOP1/SOP2 was used to characterize the drought stress response. SOP is the intersection point of stem and leaf, SOP is a point in stem line, LEN is the length of leaf tip to stem line, SOP1 is a point in leaf curve with length 1/4LEN, SOP2 is a point in leaf curve with length 2/3 LEN. SOP1/SOP2 represented the degree of leaf bending. It was comparable even between the different leaf orders, and had strong robustness to environmental factor disturbance. The field tests showed that the SOP1/SOP2 of drought-resistant genetically-modified maize presented an ascending trend before midday and a descending trend after then, which could be caused by transpiration leading to leaf water loss. The change trend of environmental factors such as air relative humidity against SOP1/SOP2 showed that under drought stress condition, genetically-modified maize was more sensitive to moisture content of the atmosphere, and the test on conventional maize plant showed that the SOP1/SOP2 lacked the regularity with the time. [ABSTRACT FROM AUTHOR]

The Kinect sensor scanning images for agricultural plants are vulnerable to field light conditions and background noise, etc. In addition, the amount of data of the color image and depth image affect the efficiency and accuracy of the plant area, which leads to the difficulty of meeting the requirement for the Kinect sensor in agricultural plant detection. For the above problems, considering the influence of the light conditions and complex background information in agricultural environment on the quality of the plant detection and the depth data acquisition, in this paper, we proposed a plant depth detection image restoration method based on K-means and K-nearest neighbor. We also developed a novel method of image restoration to reduce the impact of background information to improve the accuracy of the color image segmentation, and to enhance the accuracy of depth data. Firstly, a RGB threshold segmentation algorithm was applied to original RGB-formatted plant color images to extract plant target areas from backgrounds. Three components R, G, and B were respectively separated from RGB color space, and the difference between G and R or B was primary extract of the plant area information. Meanwhile, for the color characteristic of the environment, a K-means clustering segmentation algorithm was performed on the extracted plant target areas to remove background noise and enhance target contours. Secondly, to fix the errors of the depth data and meet the requirements of the agricultural plant detection operations, the color image and depth image were registered to restore the suspicious pixels depth data based on K-nearest neighbor algorithm. Then, a K-Nearest Neighbor algorithm was presented to recovery the black hole pixels for depth images. Finally, we acquired the depth data of target plant from the detected images. Compared with conventional RGB threshold segmentation method and K-means algorithm method, the proposed method can be used to solve the problem of the color image noise. The experiment results showed that, the segmentation error can be reduced by 12.12% with RGB threshold segmentation method, and 41.48% with K-means algorithm method. The average segmentation error can be up to 12.33% by using RGB threshold segmentation first and then the K-means algorithm. Furthermore, the proposed method can be used to restore the depth data, and can significantly reduce the effect of the backgrounds. Thus it had a good improvement to the edge sharpness of the depth data, and the accuracy of the empty point depth data of single frame. The result of this study can be a reference for agricultural plant detection and 3D reconstruction, precision of target spraying. [ABSTRACT FROM AUTHOR]