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Copyright of Transactions of the Chinese Society of Agricultural Engineering is the property of Chinese Society of Agricultural Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Transactions of the Chinese Society of Agricultural Engineering is the property of Chinese Society of Agricultural Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Transactions of the Chinese Society of Agricultural Engineering is the property of Chinese Society of Agricultural Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Transactions of the Chinese Society of Agricultural Engineering is the property of Chinese Society of Agricultural Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Transactions of the Chinese Society of Agricultural Engineering is the property of Chinese Society of Agricultural Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Transactions of the Chinese Society of Agricultural Engineering is the property of Chinese Society of Agricultural Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Transactions of the Chinese Society of Agricultural Engineering is the property of Chinese Society of Agricultural Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Transactions of the Chinese Society of Agricultural Engineering is the property of Chinese Society of Agricultural Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Manually classifying citrus based on its surface defects is tedious and time-consuming and a new real-time method is proposed in this paper based on the improved SSD deep learning model. In the testing bench of the waxing machine, 2 500 images of a variety of citrus species were taken, of which 2 000 were randomly selected as training set and 500 as testing set. Among them, the method classified 19 507 as normal, 9 097 skin defects and 4 327 mechanically damaged. Considering that traditional methods using near-infrared spectra, support vector machines, HSV and RGB color space model are inefficient to detect surface defects of citrus and can only identify one, we proposed an improved method to calculate the image using the one-stage detection model - SSD-ResNet18. The method gets the feature maps through backbone first, and then predicts the number of boundary boxes from the feature maps before determining the location and category of citrus using confidence and non-maximum suppression. This can detect a batch of citrus. In the proposed method, we used the mAP (mean average precision) as the precision index and the mean detection time as the speed index. Optimization in the proposed method was solved using the SGD (stochastic gradient descent) algorithm. The learning scheduler was based on cosine decay, enabling the learning rate to drop to 0 at the end of the training period. This ensures the lost value during the training period to continuously decline. As the model was stable at the end of the training period, it can be saved at the end of the training for further use. While the VGG16 was used as the original SSD backbone, it needs a multitude of parameters and is hence computationally inefficient. We replaced it with the ResNet18, which is approximately 100 times more efficient than the VGG16. An improved feature map was obtained from the analysis of the effective sensory field of different feature maps and the size of citrus in the map, the anchor in which was obtained using the K-means clustering algorithm from the manual label box. The suitable image resolution for the proposed model was obtained by comparing images taken at five resolutions: 512×512 pixels, 640×640 pixels, 768×768 pixels, 896×896 pixels and 1024×1024 pixels. The results showed that the accuracy of the mAP of SSD-ResNet18 was 87.89%, improving 0.34 percentage points higher than the original SSD. The average detecting time of the SSD-ResNet18 was 20.72 ms, reduced by 436.90% compared to the original SSD's 108.83 ms. The accuracy of the AP of SSD-ResNet18 was 94.72%, 85.79% and 83.17%, respectively, for detecting normal, skin lesion and mechanical damage. We compared MobileNetV3, ESPNetV2, VoVNet39 and ResNet18 as backbones and did not find significant difference between their accuracy, but ResNet18 was 10.52 ms, 16.78 ms and 36.76 ms less than MobileNetV3, ESPNetV2 and VoVNet39 in detection time, respectively. The method proposed in the paper meets the requirement on detecting speed in real-time citrus production line and can effectively classify and detect a multitude of citrus simultaneously. [ABSTRACT FROM AUTHOR]

Copyright of Transactions of the Chinese Society of Agricultural Engineering is the property of Chinese Society of Agricultural Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Transactions of the Chinese Society of Agricultural Engineering is the property of Chinese Society of Agricultural Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Transactions of the Chinese Society of Agricultural Engineering is the property of Chinese Society of Agricultural Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Transactions of the Chinese Society of Agricultural Engineering is the property of Chinese Society of Agricultural Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Transactions of the Chinese Society of Agricultural Engineering is the property of Chinese Society of Agricultural Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Honey pomelo, one of the most important fruits in China, always suffers a variety of diseases during the whole process of planting, such as maculopathy, anthracnose, scab and dark mildew, which seriously affects the fruit quality and yield. The accurate recognition of honey pomelo leaf diseases is the premise of the treatment of honey pomelo diseases, and the precision directly affects the efficiency in controlling diseases. However, most of the current researches on disease recognition aimed at the global information of the study objects, but ignored the disease's local feature extraction in multi-scale and multi-direction; in addition, the present researches generally used the method of "one to one" or "one to many" when building many types of support vector machine (SVM) in the disease classification model, few researches used the method about SVM based on directed acyclic decision tree. So, leaf diseases recognition of honey pomelo based on SVM of directed acyclic decision tree was put forward in this paper. At first, statistical analysis on components of color characteristics of collected honey pomelo leaf diseases was carried on, and the conclusion was drawn according to the statistics of component B, component 2G-R-B, component (G+R+B)/3 and component Q in YIQ color model, which were easily distinguished among the 4 diseases, and so the 4 color components were used as disease color features. Secondly, honey pomelo leaf disease images were converted into 4 grayscale images of component B, component 2G-R-B, component (G+R+B)/3 and component Q in YIQ color model. Gabor wavelet with 5 dimensions and 8 directions was used for convolution calculation with 4 grayscale component images, and 16-dimension energy sub-band was got, the mean value of which was used as eigenvector. Disease recognition model of three-level directed acyclic decision tree SVM was constructed by 6 SVM classifiers, in order to recognize 4 honey pomelo diseases, i.e. maculopathy, anthracnose, scab and dark mildew. According to the test results of cross validation method, the recognition accuracies of maculopathy, anthracnose, scab and dark mildew respectively reached 90%, 96.66%, 93.33% and 96.66%, and the average recognition rate of the 4 diseases was 94.16%, showing that the method could effectively recognize the 4 honey pomelo leaf diseases. Optimal binary tree SVM proposed in this paper was compared with BP neural network, one-to-one SVM and one-to-many SVM in different characteristic dimensions, and the results showed that the training time of the proposed method in this paper and other 3 methods was respectively 740 ms, 420 ms, 450 ms and 370 ms, and the disease recognition accuracy of the 4 methods was respectively 86%, 91.5%, 90% and 94.16%. The method proposed in this paper is superior to the other 3 algorithms in training time and recognition precision. So the proposed method can provide technical support for the accurate recognition of honey pomelo leaf diseases, in favor of the prevention and treatment of pomelo diseases, and also provide references for the prevention and cure of other plant's leaf diseases. [ABSTRACT FROM AUTHOR]

Rice variety identification is important in seed industry to assure rice seed purity and quality. The objective of this study was to assess the feasibility of a rapid and nondestructive determination of varieties of rice seeds using multispectral imaging system. A total of 250 seeds (five varieties with 50 seeds each) were provided by Institute of Rice Research, Anhui Academy of Agricultural Sciences, Hefei, China. The seeds were divided into two groups such as calibration set (40 seeds of each variety) and validation set (10 seeds of each variety). The multispectral imaging analysis was performed using the VideometerLab equipment (Videometer A/S, Hørsholm, Denmark) which acquired the multispectral images at 19 different wavelengths from the visual to the lower wavelengths of the NIR region (in the range of 405-970 nm). Image segmentation was performed using the VideometerLab software version 2.12.23. Background of the image was removed by a Canonical Discriminant Analysis (CDA) and rice seed images were segmented using a simple threshold. Having been segmented into a region of interest (ROI), the images were then used to get reflectance spectral of ROI. Image features data, including area (mm2), width/length, roundness, hunter L*, A*, B* values of rice seeds were extracted from the image analysis. Principal component analysis (PCA) with reflectance spectral and image features data was performed to examine the qualitative difference of these five rice varieties using the first two score vectors. Least squares support vector machine (LS-SVM) was used to obtain the discrimination model of varieties of rice seeds, particle swarm optimization (PSO) was designed to search the optimal values of SVM parameters to improve the search efficiency. The method presented in this paper with reflectance spectral and images feature data (Model 1) was compared with other different 3 models. For Model 2, the method used was the same as Model 1 with the spectral features data only, Model 3 was traditional LS-SVM from which optimal values of SVM parameters were obtained by cross validation with spectral and image features data, and Model 4 was principal component analysis-back propagation neural network (PCA-BPNN) with first four score vectors. The results showed that the discrimination accuracy of rice seeds of each variety from the Model 1 that combined with spectral and image features was up to 90% in validation set and 100% in calibration set, respectively, higher than other methods used in the experiments. It can be concluded that the spectral data or the image features data were both the key factors for the discrimination of the varieties of rice seeds, and differences among different varieties of rice seeds did exist and groups were apparent. The model with LS-SVM based on PSO can get better parameters to improve the classification ability than traditional LS-SVM based on cross validation. The model proposed in this paper using the combined spectral and image features data had the best prediction abilities, with the mean accuracy of the total five varieties seeds up to 94% in validation set. Multispectral imaging combined with the proposed algorithm has been proved to be a very powerful and attractive tool for classifying varieties of rice seeds because it is nondestructive, simple, rapid, and no pretreatments required. [ABSTRACT FROM AUTHOR]

Agricultural machinery is one of the important components of modern agriculture. In recent years, the number of agricultural machinery has continually increased, as well as the fuel consumption caused by agricultural machinery. The fuel consumption of agricultural machinery is directly related to agricultural production costs and the vital interests of farmers. Estimating the fuel consumption of agricultural machinery is of great significance in environmental governance, agricultural machinery operator evaluation, and agricultural cost input. Different from road vehicles, the factors affecting agricultural machinery fuel consumption seem to be more complex. Taking driving conditions for the only consideration cannot accurately predict the fuel consumption of agricultural machineries. Random forest, as a typical representative of ensemble learning, has many applications in various fields and has strong fitting ability for nonlinear data. It is widely used in the research of vehicle fuel consumption prediction. The purpose of this article is to realize the fuel consumption prediction of the grain harvester, WORLD 4LB-150AA, during working in the farmland. Based on the engine operating condition data and driving condition data collected by the harvester CAN(Controller Area Network) bus and GPS (Global Positioning System) terminal, seven indicators are constructed, including engine mean torque, engine mean speed, average speed, mean acceleration, mean deceleration, acceleration standard deviation and deceleration standard deviation. The acquisition frequency of the average data is 1.3 s, and the total number of the records is 130 788. Agricultural machineries that provided the data worked in six provinces including Liaoning, Jilin Province, Shandong, Jiangsu, Zhejiang, and Hubei. At the same time, by calculating the Spearman correlation coefficient, the correlations between seven indicators and fuel consumption were explored. According to China's agricultural divisions, the six provinces are divided into three regions: northeast region, plain region, and hilly region. Then, the fuel consumptions of the same grain harvesters in different regions were analyzed. Above the analysis, the fuel consumption prediction model of the harvester based on Random Forest was established, and compared with the one based on support vector machine. The results showed that the fuel consumption is correlated with all indicators. Among them, the fuel consumption is highly correlated to engine mean torque, engine mean speed and average speed, all with the correlation coefficient above 0.6, followed by mean acceleration, mean deceleration, acceleration standard deviation and deceleration standard deviation, whose correlation coefficients are above 0.4. There are significant differences in the fuel consumption of harvesters working in different regions. Among them, areas with high output per unit area are also relatively high in fuel consumption. Moreover, the Random Forest based model can realize the higher accurate prediction of fuel consumption during harvester operation. The root mean square error RMSE is 0.14, the average absolute error MAE is 0.24, and the coefficient of determination R2 is 0.84. The proposed method in this paper can provide a reference for the optimization of working conditions of agricultural machinery and precise fuel consumption monitoring. [ABSTRACT FROM AUTHOR]

Monitoring behavior of pigs in a pen is possible both in group and at individual level. Data analysis at individual level, however, has more advantages. Identification of pigs is a necessary step towards analyzing the different behaviors of pigs individually. Some current computer vision systems that are used for video surveillance of group-housed pigs require that the pigs be marked. In this paper, using a top-view video sequence of group-housed pigs, a machine-vision technology method for recognizing individual pig is proposed. First, to recognize each individual pig, foreground detection and target extraction are conducted on a top-view video sequence of the group-housed pigs. Second, the training samples are established, and the color, texture and shape of the individual pig are extracted; through the combination of these features, a feature vector representing an individual pig is then built. Third, the combined features are fused using the Isomap algorithm, which reduces the feature dimension on the basis of the maximum retention of the effective recognition information. Finally, the features are trained and recognized using a support vector machine (SVM) classifier with an optimal kernel function. The videos used in the present study are collected from pig farms of the Danyang Rongxin Nongmu Development Company, which is the experimental base for the key discipline of Jiangsu University, i.e. agricultural electrification and automation. The pigs are monitored in a reconstructed experimental pigsty. The pigsty is 1 m high, 3.5 m long and 3 m wide. A camera is located above the pigsty with the height of 3 m over the ground. The camera is the FL3-U3-88S2C-C with an image resolution of 1760 × 1840 pixels from the Grey Point Company. The videos are captured from 8 AM to 5 PM. Over 5 days randomly chosen, we collect 6 sections of videos every day at random time, so there are a total of 30 videos randomly chosen in audio video interleaved (AVI) format. The frame frequency of each video is 25 fps with the duration of approximately 120 s. Among the 90 000 frames (5 days × 6 videos × 120 s × 25 fps), 900 frames satisfying the requirement of experimental conditions are selected. The software MATLAB 2012b is adopted. The experimental results show that the proposed method is effective and the highest recognition rate of pigs is 92.88%. In this paper, a method for recognizing group-housed pigs individually from a top-view video sequence is explored based on the machine vision, which differs from traditional radio frequency identification (RFID) of individual pig. This study provides a new idea for the recognition of individual pig without stressing the animals, and lays a foundation for further analysis of the behavior of individual pig. [ABSTRACT FROM AUTHOR]

In order to extract the features of fault signals of dynamic system polluted and modulated by background noise, and solve anomaly detection problem of slight fault based on the generated normal patterns, this paper considers the one-class classification problem of insufficient fault samples and class imbalanced in intelligent monitoring and diagnosis for dynamic systems. Besides, it is well known that the conventional feature selection methods always depend on specific fault types. If some features are chosen to identify one fault, their performance may be poor for other fault cases. Compared with other classifiers, support vector data description (SVDD) is only based on samples at normal state, and its optimization hyperplane is independent of specific fault types. Chaos and fractal are the nonlinear phenomena which widely exist in nature and socio-economic system. Chaos and fractal can help to find out the internal regularity of system, and it is a new branch and the important fields of nonlinear science, whose subjects include unsmoothed, nondifferentiable geometry and long-range correlated structure in nonlinear system, method to judge if one system has chaos and fractal phenomena that mainly uses the largest Lyapunov index and fractal dimension to judge. Study has showed that the chaos and fractal theory can reflect the essence of the dynamic systems as complex system. Hence, a new abnormal identification and feature selection method of dynamic systems that combines chaos fractal feature and SVDD is presented. In this method, the receiver operating characteristic (ROC) curve for 2 classifying errors i.e. misdiagnosis and missed diagnosis in classification field based on SVDD is synthesized in the proposed method to make the selected chaos fractal and features more effective. The best combination of the largest Lyapunov exponent and correlation dimension is selected through analyzing chaos and fractal features, which include: the largest Lyapunov exponent, box dimension, correlation dimension and the mean of generalized dimension. To ensure good performance in the environment of complex dynamic systems, anti-noise robustness analysis is conducted. The data samples of different normal states are trained to obtain the optimal enclosing spheres respectively. And the relative distances between diagnostic samples and the distributed spheres are introduced to decide which class they belongs to. In this way SVDD classifier of normal condition samples has been proposed based on combined features as the input of SVDD classifier. In addition, SVDD is a robust data domain description method; its performance, however, is strongly influenced by kernel parameter. To improve the low fraction of target class that is accepted by the SVDD in the case of atypical target training data, experimental studies for seeking optimum parameters of kernel functions which are used to improve classification precision have been discussed. The penalty coefficient B is fixed to the value of 0.4, and in this case, the optimal kernel parameter value is suggested as a value of 0.6, which can prevent effectively the phenomenon of over-fitting and has good fault sensitivity and generalization. Abnormal identification model is set up based on the normal history data of the system to detect the abnormal condition by comparing the projection on the chaos fractal feature space to which the real measurement data and normal data were projected. Using SVDD-ROC as feature extraction method, the problem that traditional feature extraction methods depend on specific fault samples is avoided. The experiments with single-point fault data simulated on bearing vibration test-bed have been implemented to evaluate the efficiency and performance of the clustering, and results of test show that the proposed method only requires the samples at normal state to identify condition between normal states and fault states, and it is able to well distinguish unknown fault types. The study can provide theoretical and design basis for the modeling and detecting of dynamic systems with fault conditions, which is helpful to prevent incident, save maintenance cost, improve the utilization rate of the system, protect the running of production, and thus reduce costs. [ABSTRACT FROM AUTHOR]

The aim of this paper is to monitor the nitrogen nutrition status of winter wheat under stripe rust stress by hyperspectral remote sensing. The experiment was carried out at Beijing Xiaotangshan Precision Agriculture Experimental Base, China (40°10.6'N, 116°16.3'E). The cultivar of winter wheat was Jingdong 8 which was very susceptible to stripe rust. Canopy spectral reflectance data of winter wheat was collected by an ASD Fieldspec FR spectroradiometer and the disease index (DI) was measured through counting the number of wheat leaf under stripe rust stress artificially in the field. Leaf total nitrogen (LTN) content of winter wheat used to calculate DI was measured in the laboratory. The relationship between DI of stripe rust and LTN content of winter wheat was analyzed. The canopy spectra were processed by the method of continuous wavelet transform (CWT) on 10 scales, therefore, a series of wavelet coefficients were obtained in this way. The correlation coefficients between wavelet coefficients and LTN content were calculated, and then, the wavelet coefficients, which had strong correlation with LTN content, were chosen. Several hyperspectral indices were also selected according to previous research results, namely SR (simple ratio index), PRI (photochemical reflectance index), NDVI (normalized difference vegetation index), OSAVI (optimized soil-adjusted vegetation index), SIPI (Structure insensitive pigment index), LIC1 (lichtenthaler index 1), LIC2 (lichtenthaler index 2), LIC3 (lichtenthaler index 3), TVI (triangular vegetation index) and MTVI2 (modified triangular vegetation index 2), which had high correlations with LTN content. Both wavelet coefficients and hyperspectral indices were used as independent variables of models to retrieve LTN content of winter wheat, and support vector machine (SVM) regression method was used to establish the estimation models. The above estimation models of different types of variables were made a comparison. Cross-validation method was used to examine estimation model accuracies of winter wheat canopy LTN content. The test results showed that LTN content of winter wheat decreased gradually when DI of stripe rust increased and the correlation coefficient between LTN content and DI was -0.784 (n=33). The research results indicated that the model prediction accuracies of SR, PRI, LIC2, LIC3, TVI and MTVI2 were very low, the values of R2 were less than 0.700, and root mean square error (RMSE) and relative error (RE) were both high. The estimated models of NDVI, OSAVI, SIPI and LIC1 had higher accuracies than aforementioned hyperspectral indices. However, the accuracies of LTN content estimation models which were built by wavelet coefficients obtained by CWT were generally higher than those of hyperspectral indices. The optimal model was established by wavelet coefficient 423(4) which was acquired by Mexican hat wavelet function, and RMSE and RE were 0.315 and 7.62%, respectively. The wavelet coefficient 423(4) was located in 423 nm where was near to 430 nm which is the nitrogen absorption waveband. The estimation model of wavelet coefficient 663(5) was secondary, which had the prediction RMSE of 0.345 and RE of 8.28%. The variable of 663(5) obtained by Daubechises (Db5) wavelet function was also close to the nitrogen absorption waveband which lies in 660 nm. Therefore, the method of jointing CWT and SVM regression is feasible to retrieve LTN content of winter wheat under stripe rust stress and the estimation models have high accuracies. There is an important practical meaning for preventing diseases of wheat and instructing crop fertilization. [ABSTRACT FROM AUTHOR]

The color between stalks and leaves of tea in summer and autumn is similar, which means the traditional color sorter is difficult to sort based on optical characteristics. To realize the rapid modeling of tea selection algorithm and improve the sorting accuracy, a method for sorting the fine and bad products of tea by multi-feature vectors based on the morphological characteristics was introduced in this paper. First, Wuyishan Dahongpao tea was selected as a test sample to collect images during the dynamic drop process. The blue element image was extracted, and single sample’s binary image and edge were obtained by analysis of whole image connection area. Then, feature extraction program was developed based on image processing algorithm to extract morphological feature parameters of the tea samples automatically. Four simple shape descriptors-the sample perimeter, area, the length and width of minimum bounding rectangle were extracted. On this basis, eight complex shape descriptors-circularity, rectangularity, linearity, slightness, diameter, diagonal of minimum bounding rectangle, compactness and centroid were calculated. In addition, the random forest algorithm was used to determine the above features weight, the feature was selected according to weight threshold. Finally, logistic regression (LR), decision tree (DT) and support vector machine (SVM) that three different classification algorithms were established to classify the samples, verify the validity of the features and analyze the effects of different classification algorithms on the classification of tea. The original data were normalized and randomly segmented 80% used for training, 20% for testing. 10-fold cross-validation was used to select the optimal parameters of the classification model, and the training dataset was randomly divided into 10 parts, of which 9 parts were used for training, and the remaining 1 part was used for verification. According to the above machine learning system parameter optimization process to obtain the logical regression, decision tree and support vector machine optimal model, and statistical the final evaluation results on test dataset. The test results showed that: 1) The circularity weight was the highest, at 0.467, and five eigenvectors of circularity, rectangularity, linearity, perimeter and compactness were finally selected with the weight threshold value which was 0.05; 2) In the test dataset, the average accuracy of the three classification algorithms was 0.924, suggesting that the established tea morphological feature descriptors has certain separability and better effect; 3)When testing test-dataset, the accuracy score was 91.7% and F1 score of logistic regression (LR) was 92.9, the accuracy score was 91.7% and F1 score of support vector machine (SVM) was 94.7%.Support vector machine (SVM) algorithm was the best recognition effect in three classification algorithms; 4) From three different classification algorithms assessment score deviation, we can see that the generalization ability of the logic regression algorithm was stronger, the decision tree algorithm has a greater risk of over fitting. We get the lowest accuracy and F1 score of the logistic regression algorithm, while the support vector machine accuracy and F1 score were the highest, so in the evaluation of eigenvector comparability, multiple algorithms can be selected to evaluate the results of the average as the final basis for evaluation. In the experiment, we acquired dynamic image, which stay in line with the actual working conditions of the tea selection process, and can be extended to the actual processing of tea production. [ABSTRACT FROM AUTHOR]

During the detecting and locating process of green tomatoes based on obtained visible images, problems such as shadow caused by uneven illumination source, occlusion of stems and leaves, and occlusion between fruits, need to be solved. In this study, a machine vision algorithm was put forward, which aimed to determine fruit location and size of green tomatoes. Normalized cross-correlation function (NCC) is a feature detection method using template matching. The algorithm firstly detected the potential location of green tomatoes through fast normalized cross-correlation function (FNCC). Then a gray histogram based classifier was used to classify if the location corresponded to green fruit. The histogram based classifier was built based on fruit areas and non-fruit areas of a certain size (30×30 pixels) extracted from the obtained image. Seven texture features, including mean, standard deviation, smoothness, third moment, uniformity, entropy, and gray level range were calculated for those fruit areas and non-fruit areas. Three classifiers including k-nearest neighbor (KNN), SVM (support vector machine), and Naive Bayes, were used to classify fruit and non-fruit areas using those 7 texture features as input vectors. SVM was chosen based on its performance. The non-fruit location was filtered out, and the number of fruit locations was estimated. Meanwhile, the image was segmented based on color analysis. Red and Blue component from RGB (red, green, blue) image, and Hue component from HSV (hue, saturation, value) image transformed from RGB images, were used as the basis for color analysis. Using the fruit potential location number estimated using FNCC as an input parameter of circular Hough transform (CHT), CHT was then applied to the edge image of the segmented result. The center coordinates and radius value of each circle were calculated. Finally, the detection results were merged based on the analysis of the distances of 2 centers of fruit circles detected using CHT. If the distance between 2 circles is smaller than the minimum fruit radius, the circle with a larger radius will be kept, while the other circle will be flagged as repeatedly detected one. Thus, the recognition and positioning of the green tomato were realized. When green fruits and red fruits appear on the same image, a red fruit detection algorithm based on the local maximum value method and random circle round transform detection, which was proposed by the author in another paper, would be carried out on the obtained image. Then the red fruit detection result was combined with the green fruit detection result. The proposed method combined the texture, color, and shape information of the image, and eliminated the disturbance of the color similarity between the green tomatoes and green leaves and stems. A total of 70 images were used in this study, in which 35 images were used as training images, and the other 35 images were used as validation images. The correct detection ratio for 72 fruits in the training dataset was 89.2%, and the correct detection ratio for 105 fruits in the validation dataset was 86.7%. The proposed method has provided a reference for the development of tomato harvesting robots for both red and green mature tomato fruits. [ABSTRACT FROM AUTHOR]

Copyright of Transactions of the Chinese Society of Agricultural Engineering is the property of Chinese Society of Agricultural Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Soft and hard change detection method(SHCD) is a newly proposed approach and previous studies have showed that this method is useful for accurately identifying crop. In this paper, SHCD was used for classifying winter wheat in both simple, homogeneous, low fragmented regions and complicated, heterogeneous, discontinuous regions, and the impact of agricultural landscape pattern and image resolution on the accuracy of winter wheat identification was quantified. Experimental process included simulation image creation, winter wheat mapping by SHCD and result analysis. Simulated images were obtained by the crop change detection model, the winter wheat phenology and the effects of parcel fragmentation. Winter wheat mapping was obtained by the processing of image differencing, sample selection, and ESVM division. Three statistical methods were used to estimate the precision of winter mapping for different window sizes. And we further analyzed the effects of image resolution, window size and spatial characteristics on the identification of winter wheat distribution accuracy. The results showed that: 1) The optimum resolution was 10 and 40 m for hard change detection(HCD) and soft change detection(SCD), respectively. Different from HCD and SCD methods, SHCD was not sensitive to pixel resolution and always yielded accurate classification results. 2)From the view of efficiency, the calculating process including hyperplane segmentation and labelling was same for SHCD, SCD and HCD methods. During the labeling stage, SCD directly assigned the membership probability to each test pixel, while HCD segmented the feature space using hyperplane and adopted thresholds to determine the label for test pixels. By combining the advantages of HCD and SCD, SHCD firstly segmented the feature space into wheat, mixed wheat and non-wheat area using marginal hyperplanes, and then assigned the membership probability for mixed wheat. Accuracy assessment results showed that: 1)In highly fragmented regions, the root mean square errors(RMSEs) of SHCD were lower than 0.15 and not sensitive to image resolution. The bias values were low; the R2 values were higher than 98% and increased with window size increasing. 2)In lowly fragmented regions, SHCD was also not influenced by image resolution. Compared with SCD and HCD, wheat distribution derived from SHCD also had the lowest RMSE and bias and the highest R2 value. Combining the advantages of HCD and SCD method, SHCD method effectively eliminated the classification errors caused by spectral variability in hard change areas by SCD method and exclusive result in soft change areas by HCD method. Therefore, SHCD method provided the highest accuracy of crop acreage. So this simulation experiment provides experimental basis and ideas for winter wheat identification in real situations. However, SHCD is limited to classification errors of ESVM method, which is difficult to identify crop from other land cover types between land parcels. Besides, the impact of the different land cover types with similar spectral characteristics is inevitable for this method. Finally, even though SHCD shows good results for soft change area, the errors of commission are still large, which needs to be solved in the future. At the same time, in order to reduce the impact of other factors, simulation experiment simplified real situations, which needs to be tested in real research areas furthermore. [ABSTRACT FROM AUTHOR]

Copyright of Transactions of the Chinese Society of Agricultural Engineering is the property of Chinese Society of Agricultural Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Transactions of the Chinese Society of Agricultural Engineering is the property of Chinese Society of Agricultural Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Transactions of the Chinese Society of Agricultural Engineering is the property of Chinese Society of Agricultural Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Transactions of the Chinese Society of Agricultural Engineering is the property of Chinese Society of Agricultural Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Transactions of the Chinese Society of Agricultural Engineering is the property of Chinese Society of Agricultural Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Transactions of the Chinese Society of Agricultural Engineering is the property of Chinese Society of Agricultural Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)