Search

The research on crop phenotype is one of the important measures in crop breeding. Crop breeding is a technology on selecting the good seeds by the crop phenotype. The farm remote sensing image analysis is a simple and effective method for rapid analysis of crop phenotype. However, the farm remote sensing image acquired by UAV (unmanned aerial vehicle) will be affected by the noise. In order to process and analyze the remote sensing image accurately, the remote sensing image should be first denoised. Typical image denoising algorithm of frequency domain is wavelet threshold algorithm. Wavelet transform can identify the singular signal accurately; however, the traditional wavelet can't effectively deal with multidimensional signal. For example, two-dimensional wavelet obtained from one-dimensional wavelet tensor only has horizontal and vertical directions, and the wavelet filter is isotropic, which is not sensitive to the image with more directions of the texture; while using the traditional wavelet threshold denoising algorithm to handle noise image, it is easy to make texture region blurry compared with the common wavelets. It is well known that the shear transformation has been introduced into the definition of the shearlet, which makes shearlet can represent more directions. In order to capture the directions of local geometric features of image, shearlet must be compactly suppressed. Anisotropic expansion scale transform can be applied in multi-resolution analysis. From coarse scale to fine scale approximation signal, namely in the approximation image in smooth regions using coarse scale, and in the approximation image with directions of texture using fine scale, the optimal approximation of singular curves can be achieved through the application of multi-resolution analysis method. Shearlet filter is anisotropic, and it is very sensitive to the direction of the texture; however, with the increase of noise standard deviation, after denoising, using shearlet algorithm generates artifacts easily. In brief, the denoising algorithm based on the total variation model has the advantage of edge preserving and noise reduction. In the application of shearlet for image denoising, we find that the shearlet with noise in the texture region can detect the texture direction, and can effectively remove the noise; but when the noise standard deviation is large, the application of shearlet algorithm in the image can cause the denoised image to tend to produce some artifacts, which will be identified as noise texture perhaps due to the shear wave in the smooth region. In order to solve the problem of discrete shearlet algorithm, this paper proposes a method based on the combination of shearlet and total variation model to eliminate the artifacts. First, select the generated Symmlet quadrature mirror filter, and ascertain anisotropic scale parameters and shear parameters. Perform discrete shearlet transformation to farmland remote sensing image with multi-resolution analysis. Wavelet coefficients can be obtained using the conventional method by anisotropic wavelet transformation. Wavelet coefficients are projected into wavelet of image. Hard threshold algorithm is used to handle noise coefficients. Wavelet coefficients are adopted to reconstruct farm remote sensing image. After using total variation model to smooth artifacts, the mirror extension is used to preprocess the image. In part of the experiment, the algorithm proposed in this paper is compared with the shearlet algorithm and total variation algorithm, and this algorithm gets a PSNR (peak signal to noise ratio) 1dB more than that from total variation model, and an iteration number of total variation less than that from direct total variation. In addition, not only can this algorithm effectively denoise farm remote sensing image noise and visual effects, but also can remove artifact better than the discrete shearlet transform algorithm. [ABSTRACT FROM AUTHOR]

Image processing and analysis play the key roles in the smart agriculture system. As the agricultural images are often taken in the open air, the heavy atmospheric haze and dust in the air are often included in the images. It’s very difficult to differentiate the noises and the detail texture in the images using existing common methods. As the detail texture is often employed to identify the important feature of different plants, the texture preserving image denoising algorithm plays an important role in the agricultural field. The existed methods can’t identify the detail texture and the noise, and so it is difficult to fulfill the texture preserving image denoising. The shearlet transform proposed in recent years possesses the multi-scale directivity as the shear matrix is introduced into it. So the shearlet transform can be used to describe the detail texture precisely. However, the shortcoming of the shearlet transform is that it often mistakes the noise as the texture in many cases. On the contrary, the variational method views all the objects in the images as the smooth domains, this results in detail texture in the images being often destroyed. To solve the problem, we proposed a coupling technology of the multi-scale variational method based on the interpolation wavelet frame and the shearlet transform, in which the variational method was employed to identify the contour and the shearlet transform to describe the texture precisely. According to this method, the image was firstly decomposed and reconstructed by means of the shearlet transform, which can remove most of the noise in the image. Second, the variational method based on the multi-scale interpolative wavelet frame was employed to smooth the denoised image. This can divide the image into some domains, and they possessed different texture feature which could be identified by means of the correlation value derived from the gray-level co-occurrence matrix of the grayscale image. Compared to the variational method under the total variation (TV) frame, the multi-scale interpolative wavelet frame can identify more detail domains which was helpful to improve the quality of the denoised images. To the image noised by the Gauss noise with the standard derivation 10, the peak signal to noise ratio (PSNR) of the denoised image obtained by the variational method based on the multi-scale interpolative wavelet frame was 1.3131 larger than PSRN obtained by one based on the TV frame, and the structural similarity image measurement (SSIM) increased by 4.5% accordingly. Next, the variational method and the shearlet transform method can be used to remove the noises existed in the cartoon region and the texture region, respectively. This can overcome the shortcomings of only one method and remain all the merits in all the denoising algorithms. For instance, median filtering can be applied to remove the noise in the smooth domain, and the shearlet transform can be employed to remove the noise in the domain with abundant textures. Last, the locust slice images were taken as the examples to illustrate the proposed method. The numerical experiments showed that coupling technology proposed in this paper can improve the image denoising precision effectively compared to each single method. The value of the PSNR was improved by 6.37% compared to the multi-scale variational method, and by 5.90% compared to the shearlet transform method. Compared with another attractive algorithm K-SVD which is an excellent algorithm for designing of overcomplete dictionaries for sparse representation, the coupling technology also had apparent superiority. To the locust slice images noised by the Gauss noise with the standard derivation 40, PSRN obtained by the coupling technology increased by 0.43 compared to the K-SVD algorithm, and SSIM was improved by 2.5% accordingly. In addition, the coupling technology was independent of the images, and the K-SVD algorithm yielded sparse representation for the training signals which limits its application to some extent. [ABSTRACT FROM AUTHOR]

Nowadays, smart agriculture has become a research hotspot in the field of agriculture technology. Meanwhile, the image is one of the important data sources for smart agriculture and related technology. Image processing technology has been widely used in modern agricultural research. In the application of outdoor agriculture, environmental conditions are important factors that degrade the quality of the obtained image. In particular, the haze is a very common factor that decreases image quality seriously. Images acquired in bad weather, such as haze, are seriously degraded by the scatting of the atmosphere particles, which reduces the contrast, color saturation and hue shift and makes the object features difficult to identify. In order to remove the negative effect of the haze in degrading image quality, this study proposes a new image dehazing algorithm that combines the dark channel prior model with the interval interpolating wavelet transform. The dark channel prior is based on the statistics of the haze-free outdoor images. Specifically, it is based on a key observation, i.e. most local patches in haze-free outdoor images contain some pixels which have very low intensities in at least one color channel. Using this prior with the haze imaging model, we can directly estimate the thickness of the haze and recover a high quality haze-free image. The wavelet transform is used to carry out multi-scale refinement through the operation of telescopic translation, which can highlight the characteristics of the details of the image. Interval interpolation wavelet may reduce the error caused by the approximation of the wavelet. Firstly, we estimate the transmission and the atmospheric light value by using the dark channel prior theory, and restore the image. Secondly, the obtained image is decomposed by interval interpolation wavelet transform, and then reconstructed by processing high frequency sub-band wavelet coefficients. An experiment is carried out using this method. The results show that after the image processing by using this method, the whole image looks like comparatively bright, and the image contrast and clarity are improved. Finally, it works to filter out the negative effect caused by the haze. The processed image fits the human observation feeling well. It has good visual effect, obvious layering and rich texture detail. For color images, color saturation can be well kept, and the distortion is correspondingly low. Hence, the processed color images are close to the real objects with true color. Moreover, after the image processing, the contour contrast of the scene is obvious and is not blurred. It also makes distant scenery in the image very clear. We compare our haze removal results with that by the dark channel prior algorithm. On average, the standard deviation values of our algorithm in the R, G, and B channels are respectively improved by 25.44%, 27.90% and 26.24%. In sum, this study presents a new method that combines the dark channel prior model with the interval interpolating wavelet transform, and the image can be well dehazed and achieve good restoration in image visibility using this method, and thereby lays the foundation for acquiring accurate image information. Moreover, it is also useful for the application in modern precision agriculture. [ABSTRACT FROM AUTHOR]

The richness of the marbling in beef, as an important index of beef quality, can be used to characterize the beef fat content. In particular, the area ratio of marbling, big fat density, and small fat density are the main indicators for most existing beef grade determination. Researchers have investigated that computer vision and image processing is applicable to the automatic grading of beef marbling, and thus plays a great role in promoting the development of the beef industry. However, images may be polluted when experiencing acquisition, transmitting and other processing. Consequently, the quality of the images may be reduced, and thereby, more uncertainties emerge. Importantly, the texture of the beef marbling image becomes blurred and texture contour is not clear. It will further affect the subsequent procedures of texture segmentation and extraction. Therefore, it is necessary to use the de-noising method with better edge preserving property to keep the edge and texture information of the image. In this study, we aimed to use the method of multi-scale interval interpolation wavelet to de-noise images, and thereby to smooth the gray values to segment and extract the regions of beef muscle, large and small fat particles from the beef marbling image. Here, we used the method of multi-scale interval interpolation wavelet to solve the partial differential equation, thus to de-noise images. Specifically, from this method, the edge-preserving smoothing for different object area can be realized, so that the texture and edge of beef marble were made more clearly. In addition, in this method, we chose the external collocation points adaptively, thus the computational efficiency can be greatly improved. In particular, extension method based on Center Similarity Transformation can be used to solve the boundary effect effectively. Firstly, on the basis of the objective evaluation index of the image, the PSNR (Peak Signal to Noise Ratio) mean value of the image de-noised by the proposed algorithm was higherthan the mean values obtained by using the mean filtering, median filtering and Wiener filtering of 9.0, 8.2 and 6.6 dB, respectively. In addition, the SSIM (Structural Similarity Image Measurement) value of the image de-noised by the proposed algorithm was also the largest among values obtained by algorithms mentioned above. Secondly, it is known that different objectives have different gray values, which is taken as the principle of segmentation. Hence, the processed image was segmented using different gray thresholds. In detail, the procedures of segmentation included two steps. The first step was to obtain the gray thresholds by prior knowledge, and the next was to segment the image for dividing the background, external fat, adhesive fat, small and big fat from the image by those thresholds. Finally, we compared the results of segmentation derived from our methods with the results of segmentation from Otsu. Here we showed that using the de-noised method of multi-scale interval interpolation wavelet was useful to achieve a local uniform smooth and keep the object contour information of beef images, thus to improve the accuracy of the segmentation and extraction of fat particles. The result of segmentation by gray thresholds was more accurate than the results of Otsu and retained more details about the texture of beef marbling. Furthermore, we also found that our result almost had no omission in segmenting and extracting fat particles, especially for small fat particles. Overall, our results provided a new de-noised method to improve the accuracy of beef grading. [ABSTRACT FROM AUTHOR]

Weeds is one of the main harmful factors to the yield and quality of wheat and other main crops during seedling stage. Image processing technology is often used in weed recognition, but the method mainly cares about the weeds between different rows, which is always inefficient and wasteful for unmanned aerial vehicle (UAV) and machine spraying ways. In order to overcome the limitations above, this paper proposes a regional weed identification method, which takes advantage of properties of shearlets. Shearlets have attracted much attention in the field of image recognition because of its good sensitivity and fast computation in texture recognition. Meanwhile, it is a multi-scale analysis method with the characteristic of direction independence. Through the comparison of the regional images of the wheat and weed, it shows that the texture of the weed leaves is more complex while the wheat leaves are relatively regular. So we first choose 8 images including 4 wheat images and 4 weed images. Then we obtain shearlet transform coefficient (STC) at diverse scales and directions according to the different texture characteristics of wheat and weeds. In the STC images of different scales, the brightness from black to white represents different coefficient value. Moreover, the complexity of bright regional distribution represents the textural complexity, which can be used to distinguish wheat and weeds. Shearlets have self-adaptability because of different directions on these scales, so that obvious textural features in images taken from different angles can be detected. In our research, we take the self-adaptability of shearlets and the differences of STC images into account, and we choose the STC in the second scale of vertical cone to distinguish wheat weeds as experimental object. The result shows that the STC mean of wheat in the second scale is lower than that of weeds. Additionally, the fluctuation of STC mean of wheat is smaller than that of weeds. This study chooses 16 wheat images and 16 weeds images, aiming to distinguish weed and wheat more intuitively; we take a further statistical analysis on the mean and variance of coefficient matrixes of shearlets in the second scale of vertical cone. After normalization treatment, the distinction mean values and mean square error between wheat seedling and weeds are about 0.07 and 0.08 respectively. We randomly select 13 pictures of weeds and wheat seedling, and the recognition accuracy is 69.2%. The experimental results of contrast experiment show that the shearlet-transform method performs better than gray level co-occurrence matrix (GLCM) method to distinguish wheat seedling and weeds. We can get an explanation for the experimental results from the different theory of the shearlet-transform and GLCM. The theory of shearlet-transform shows that it can get different directions information adaptively. On the contrary, GLCM can only get the directions assigned, so the number of directions for image processing can't be changed. In addition, the method of splitting blocks of larger image gathered by UAV is used to realize the effective identification of non-wheat region. From the experimental results, we can see that the difference between wheat and weeds is based on effective shearlet-transform, and we can generalize our method to other image classification based on textural features. Furthermore, this method performs with high flexibility and stability and it has the potential for herbicide spraying in the field. [ABSTRACT FROM AUTHOR]

In order to improve the automation degree in agriculture, reduce the workload of farmers, enhance labor efficiency and product quality and ensure that the fruit can be harvested in real time, we designed a robot system which can imitate human picking behavior and pick fruits in real time. In this paper, we first analyzed the physiological characteristics of human upper limb and built a motion model according to human upper limb movement. In the model we selected the key joints and bones as the acquisition object, while the acquisition information includes its location, angle, speed and some other information. In addition, based on the bionics characteristics, we built a motion model of the robot manipulator. The system can gather the skeleton information of the human body using the depth camera Kinect device when the human picking movement occurs. After that the system calculates the position deviation of the joint and the rotation angle of the skeleton by calculating the space vector. At the same time, the system calculates the robot arm for each joint position and rotation angle information. According to the information of instructions, the system can translate the mathematical information into many instructions and send them to the robot manipulator. The robot manipulator will follow the instruction and fulfill the task of picking in real time when instructions have been received correctly. The action system includes 3 main sub-modules: information acquisition module, instruction transition module and instruction execution module. The information acquisition module uses a depth camera Kinect device and acquires the desired three-dimensional (3D) physiological data from the human behavior. The degree of mechanical freedom of human arm is 7 while five-axis robot limb is enough to arrive anywhere human limb can arrive. So the paper built a model with 5 degrees of mechanical freedom to imitate the human picking behavior. The 3D information includes 3 kinds of physiological parameters: position, velocity and angle information. The position information can be obtained through Kinect SDK (software development kit). Based on the position data the module can calculate the vector data automatically. Additionally, through Kinect SDK the module can get 30 frames of position data per second which can support velocity calculation when the data are denoised and smoothed by a filter. The instruction transition module will compare the motion data with the data already saved in the database to find out the most similar movement instruction. The instruction transition module consists of a reverse calculation sub-module which can calculate all the location, mechanical rotating angle and speed information of the robot arm joint according to the destination position and motion model data. The mechanical data in addition to instruction will be sent to the instruction execution module. The instruction execution module in fact is a robot arm. Its mechanical arm shape is very similar to that of human arm. There are mechanical joints corresponding to human upper limb joints on the mechanical limb. The 3 linkage mechanisms can simulate the movement of human upper limbs, which can help the robot finish the picking task. In this paper, we analyzed the mechanism of human upper limb, built a mathematical model based on the bionics principle of robot, built a model of manipulator with 5 degrees of freedom, and studied the actual coordinate movement of the mathematical model of the mechanical arm in the process of fruit picking. And on the basis, we put forward a mathematical method, which is relatively simple and effective. In order to verify the validity of the model, we performed a simulation test, and the results of the test were in the acceptable range, which verified the validity of the model. [ABSTRACT FROM AUTHOR]

Precise control of EC for fertilizer solution is one of the key technologies to realize smart irrigation in the production of protected vegetable. Serious lag and instability in EC detection will happen during the mixing of fertilizer and water due to differences in mother liquor concentration, fertilizer suction method and irrigation flow etc. To solve this problem, a new EC sensor was designed to measure the EC value of fertilizer solution indirectly by measuring the frequency based on different EC values leading to different equivalent resistances, and the smart irrigation system was developed as microcontroller (MSP430) was the master controller. Meanwhile, a subsection control strategy was introduced to regulate and control the EC value of fertilizer solution, and the opening or closing of electromagnetic valve for the suction of fertilizer solution was controlled by outputting corresponding duty cycle. When the difference between the target EC value and the measured value was large, PI control was adopted to narrow the gap as fast as possible. In this study, the threshold is defined as 0.2 mS/cm according to practical knowledge, therefore, fuzzy control with good robustness and high adaptability was adopted when the difference was smaller than 0.2 mS/cm. In fuzzy control stage, triangle function and centroid method were selected as membership function and defuzzification calculation with 63 rules and the output language variable value was divided into 13 levels. A compound venturi was designed as fertilizer solution suction device, with the diameter of inlet and outlet pipes was both 40 mm, and a closedcontainer (10 cm× 10 cm× 6 cm) was installed at the throat part to connect the inlet and outlet pipes, and 7 suction interface were located at the bottom of the container to link with the electromagnetic valves. The main pipe of the irrigation system was controlled by 24VDC electromagnetic valve and the drippers of the branch pipes were inserted into the substrate near plant roots. The filter was installed between inlet water and the irrigation system to avoid blocking venturi and drippers. An experiment including 4 different target EC values and 4 different suction pipelines was carried out to test the effect of the designed control system. The results showed that the frequency measured by our EC sensor had a significant linear correlation with the measured EC value with R2 is 0.999 5. In the case of subsection control, when the target EC value was 2 mS/cm, the steady response time was 122 s and maximum overshoot is 0.34 mS/ cm when 4 channels were used for fertilizer solution suction, which performed much better than only PI control with steady response time 180 s and maximum overshoot up to 0.62 mS/cm. In addition, the higher the target EC value set, the more accurate the steady EC value was, however, the steady response time became longer and overshoot was more serious. The subsection control strategy used in this study can effectively overcome excessive overshoot, also the time for mixing fertilizer solution and the measured EC value can both meet the requirements for the practical production of protected vegetable. [ABSTRACT FROM AUTHOR]