In order to classify the appearance quality level of Fritillaria thunbergii, the F. thunbergii dataset was constructed with the DigiEye system followed by an image annotation tool. Several statistical learning and object detection algorithms were selected to train and test the F. thunbergii dataset. The results showed that the model trained by the YOLO-X of YOLO (you only look once) series had relatively better performance. In addition, to optimize YOLO-X, according to the unique features of F. thunbergii dataset, a dilated convolution structure was embedded into the end of the backbone feature extraction network of YOLO-X as it could improve the model sensitivity to the dimension feature. The mean average precision (mAP) of the improved model was raised to 99.01%; the average precision (AP) for superfine, level one, level two, moth-eaten, mildewed, and broken F. thunbergii were raised to 99.97%, 98.33%, 98.47%, 98.71%, 99.73%, and 98.85%, respectively; and the weighted harmonic mean of precision and recall (F₁) were raised to 0.99, 0.92, 0.94, 0.97, 0.99, and 0.97, respectively. The tune-up in this study enhanced the detection performance of the model without increasing the number of parameters, computational complexity, or major changes to the original model. This study provides a scientific basis for the subsequent construction of F. thunbergii detection platform.

In order to solve the precision and real-time problems of parental discrimination in the processes of hybrid rice breeding and pollination, an improved DeepLabV3+ hybrid rice breeding parental discrimination semantic segmentation model based on a fully convolution neural network was proposed. The lightweight MobileNetV2 structure of the backbone network was used to replace the Xception structure of the original DeepLabV3+ backbone network, which is more suitable for the application on mobile devices. An extraction method of low-level features with close connection was proposed. The lower-level information and higher-level information were preliminarily concated as the input of the original lower-level information, which enabled the network to obtain more intensive information, thus enhancing the ability of the network to extract details. The results showed that the improved DeepLabV3+ network model had higher segmentation precision for parents of hybrid rice seed production than the original DeepLabV3+ network model, and reduced the model training time and image predictive time. Compared with other mainstream network models and advanced network models, it is found that the accuracy of different parameters of improved DeepLabV3+ network model is improved. This study provides a reference for the development of deep learning in the field of agricultural visual robots.

This study aims to design and optimize the main structure of a stable and lightweight unmanned vehicle-based field crop phenotyping platform. In order to meet the requirement of high safety, high stability, and lightweight, Pro/Engineer Wildfire 5.0 software was used to design the main structure model of the platform, and HyperWorks 2020 software was employed to perform the finite element analysis and optimize the structure model. Meanwhile, the statics and dynamics analysis of the structure was implemented during the design process. Taking the main structural mass as the objective function, with the material yield limit and the first-order mode as the constraints, the design of experiment (DOE) method was applied to extract the structural parameters of parts with the high sensitivity to the first-order mode and stress under multi-working conditions as design variables, which greatly reduced the variable number. Then, the adaptive response surface method (ARSM) was applied for iterative calculation to obtain the optimal variables. Compared with the corresponding output response of the actual finite element model, the ARSM approximate model produced a low error of 3.79% and 4.32% in the main structure mass and the first-order modal frequency, respectively, which also obtained the maximum stress error of 4.24%, 4.14%, and 1.26% under the static and uniform speed conditions, starting conditions, and emergency shutdown conditions, respectively. These results show that the ARSM approximate model has a high accuracy and the error is less than 5%. Compared with the original structure, the final overall mass was reduced by 63.61% at maintaining the safety factor of each working condition above 5.0. As a result, the main structure of field crop phenotyping platform is obtained with high safety factor and meeting usage requirements.

As a perennial foliage plant, the changes of physiological and biochemical indexes of tea plant under lead aerosol stress and the lead accumulation effect need to be studied urgently. In the present study, the lead aerosol was used to simulate atmospheric pollution, and the lead accumulation in roots, stems, and leaves as well as the changes of photosynthetic pigments and antioxidants in leaves of ‘Wuniuzao’ and ‘Yingshuang’ tea plants were evaluated. Then the model for the rapid detection of each index was established based on Fourier transform infrared (FTIR) spectroscopy. The results showed that the lead content of tea plant leaves in the normal environment was very low, which met the national food safety standards. The lead content of roots was much higher than that of leaves, which proved that the soil-root pathway was the main way for tea plants to accumulate lead in the normal environment. With the increase of stress time, the lead content in the leaves of high concentration lead stress group was significantly higher than that in the stems and roots, which proved that there was an air-leaf absorption pathway, and high concentration lead stress group was up to 14 times that of no lead treatment group. In addition, the photosynthetic pigment and ascorbic acid contents increased initially and then decreased, whereas glutathione content basically increased during the entire 42 days. Support vector machine (SVM) and artificial neural network (ANN) were used to establish quantitative prediction models for monitoring the physiological and biochemical indexes based on the characteristic wave-band of the mid-infrared spectrum, proving that the mid-infrared spectrum could be a potential approach for the rapid detection of physiological and biochemical indexes in tea plants under the lead aerosol stress, and the ANN model showed better effects than the SVM model. The ANN quantitative model of chlorophyll a obtained the best prediction effect, of which the best correlation coefficient of prediction set (r_p) could reach 0.810, and the root-mean-square error of prediction set (RMSE_p) was 0.032 mg/g. The above results indicate that lead aerosol stress could cause the accumulation of lead and result in the significant changes of physiological and biochemical indexes in tea plants, and the FTIR spectroscopy is a reliable method for the rapid detection of physiological and biochemical indexes in tea plants under the lead aerosol stress.

Traditional rice lodging measurements are time-consuming and destructive to rice plants. This study thus developed an easy-to-implement and nondestructive mechanical platform for phenotyping analysis of rice lodging, which can monitor the lodging-resistant characteristics of rice in different growth periods. The lodging measurements were conducted at the jointing stage, booting stage and heading stage from August 15th to September 21st, 2019. The force and displacement were measured from two different directions using the lodging measuring platform with a force sensor, which were used to calculate the dynamic bending stiffness coefficient (K_EI) of rice. Meanwhile, RGB images were collected from the mechanical platform, which were applied to calculate the projected area and the center of force (CoF). The results showed that the K_EI values of lodging-resistant cultivars (Beidao 1 and Shennong 9816) were different from those of lodging cultivars (Yueguang and Qiuguang), which can reflect rice’s lodging resistance in the growth period. In addition, we found that the average distances between CoF and the root of lodging cultivars within the RGB images were larger than those of lodging-resistant cultivars and easily led to rice instability. This study can provide valuable information for rice lodging monitoring and precision breeding.

In view of the low efficiency of picking high-level apples from fruit trees by fruit farmers, a pipeline transportation device for assisting manual picking was designed. In order to optimize the transportation parameters of the device, a test bench for impact force was established. Taking 'Fuji' apples as the research object, the impact force and mechanical damage of 'Fuji' apples with a fruit diameter of 80-90 mm from a height of 3 m along the pipeline to the fruit box were analyzed. Taking the type of pipeline lining, the lining thickness, and the crash pad thickness as the test factors, and the impact force and damage volume of apples when they fell into the fruit box as the indexes, the response surface test was carried out on the basis of the single factor test. The results of single factor test showed that the pearl cotton material had a relatively good protective effect on apples. The impact force and damage volume gradually decreased with the increase of the lining thickness, and gradually decreased with the increase of the crash pad thickness. The results of the response surface test showed that the optimal combination of transportation parameters was as follows: the lining type was pearl cotton, and the lining thickness was 10 mm, and the crash pad thickness was 8 mm. At the optimal combination conditions, the impact force when the apple fell into the fruit box was 4.99-5.47 N, and the damage volume was 275.02-300.52 mm³. The results of verification test showed that the errors of the impact force and the damage volume of apple were both less than 5%, indicating that the optimization results of pipeline transportation parameters are reliable.

The shape information of leaves from 39 tobacco varieties was extracted by using landmark method. The differences in leaf shapes were compared and analyzed among different varieties and different leaf positions at different growth stages. Principal component analysis was used to reduce the dimensionality of the data. The sources of differences were visualized among different leaf shapes. Decision tree, random forest and support vector machine were used to perform discriminant analysis on tobacco leaf shapes. The results of the principal component analysis showed that the first three principal components accounted for 42.7%, 21.3% and 10.7% of the total differences in tobacco leaves at the flowering stage, which were characterized by leaf width and the maximum width position, leaf torsion, and petiole size, respectively. The discriminant results of tobacco leaf shape based on machine learning showed that the discriminant accuracy based on landmark data was 52%-62%, while the value was 51%-54% for common leaf shape indicators. The discriminant accuracy on superior or medial leaves was about 10% higher than that of inferior leaves, representing more obvious characteristics of variety. Due to the growth of the leaves, the discriminant accuracy of the leaves at rosette stage was nearly 10% lower than flowering stage. The discriminant accuracy of landmark method increased to 77% after removing 12 atypical varieties. The effect of the landmark method on leaf shape information extraction is better than the common leaf shape indicators, which provides a new idea for the automated extraction of leaf shape information.

To develop an efficient method for quantifying tobacco plant types in the field, the three-dimensional (3D) point clouds of individual plant of five tobacco cultivars were reconstructed based on multi-view image sequences using the structure from motion method. According to the plant type characteristic indexes commonly used, ten phenotypic parameters such as plant height, top width, bottom width, and maximum width of leaf layer were automatically extracted based on the 3D point cloud of tobacco plant, and the calculation accuracy was evaluated based on the plant height and maximum width of leaf layer measured manually in situ in the field. The results indicated the coefficients of determination (R²) of the plant height and maximum width of leaf layer extracted from the 3D point cloud were all greater than 0.97, and the root mean square errors were 3.0, 3.1 cm, respectively. Meanwhile, the extracted phenotypic parameters of tobacco plants were analyzed by different methods. The results of intergroup correlation analysis showed that 16 pairs of traits were extremely significant positive correlations, while one pair of traits was extremely significant negative correlation. The results of one-way multivariate analysis of variance showed that there were highly significant differences among the plant types. The first three principal components were extracted by principal component analysis, and their cumulative contribution rate to the overall variance was 81.6%. The accuracy of plant type discrimination was 93.7% using Stacking ensemble learning method, which was significantly higher than those using random forest, support vector machine and naive Bayesian. This study can provide a method basis for phenotypic characteristics and plant type recognition of field-grown tobacco plants.

Light stress can restrict the normal growth and development of rapeseed seedlings. In order to realize the early diagnosis of light stress in rapeseed seedlings, a 21 d experiment was conducted on rapeseed seedlings of two leaves and one heart stage using hyperspectral imaging technology. After preprocessing the collected canopy leaf spectra, the light-stress-sensitive bands were extracted through spectral reflectance and continuous wavelet transform. Then successive projection algorithm was used to extract characteristic wavelengths, and the continuous wavelet transform-stepwise discriminant analysis method was used to extract wavelet features. To further improve the accuracy of the stress detection model, a total of four features including the area under curvein the 939-978 nm band, the tangent value of the characteristic angle (tan θ), the reflectances at 984 and 1 408 nm were selected by analyzing the characteristics of the spectral band and the evolution of the spectral characteristics at the seedling stage of rapeseed to establish a multi-feature fusion Fisher discriminant model. The results showed that the average classification accuracy of the model was 86.88%, which achieved the best classification effect in the d₂₀ family, with an accuracy of 95.00%. The research provides a powerful reference for the rapid diagnosis of light stress in rapeseed based on hyperspectral imaging technology.

In order to study the change rule of the internal flow field of the self-cleaning mesh filter with different inlet flow rates during the filtration and sewage processes, we used Fluent software to carry out the numerical simulation of the clear-water flow field for the filtration and sewage processes of the self-cleaning mesh filter, and obtained the pressure field and velocity field at the different inlet flow rates. Comparing the numerical simulation results with the physical test results, the relative error was within 10%, which confirmed the reliability of the numerical simulation. Meanwhile, the results of the numerical simulation showed that the higher the water flow rate was, the more violent the mixing of water was in the tank and the sewage system, which could reduce the sedimentation and help to improve the filtration and sewage effects. Moreover, with the increase of the inlet flow rate, the pressure difference between the inlet and outlet of the tank and the sewage system increased, and the head loss also increased, and the requirement for the stability of the filter structure was higher. The above research results can provide a reference for the optimization of the horizontal self-cleaning mesh filter structure.

To investigate the impact of soil physical properties and driving velocity on the driving performance of a tracked tractor and obtain better operating conditions on paddy soils in the plastic state, a paddy soil mechanical model and a tracked tractor physical model were established using the RecurDyn, a multi-body dynamics software. The method of the quadratic orthogonal rotating combination design of four factors and five levels was applied to determine the impacts of clay content, moisture content, density of soils and driving velocity on the driving resistance and subsidence depth. The modeling results indicated that the driving resistance was positively associated with clay content and moisture content of soils, but negatively associated with soil density and driving velocity. The contribution rates of the factors to the driving resistance from high to low followed the order as soil moisture content, driving velocity, soil density, and soil density combined with soil clay content. The subsidence depth increased with greater soil moisture content but decreased with higher soil clay content, soil density, and driving velocity. The contribution rates of the variables to subsidence depth were ordered as soil moisture content, soil density, soil clay content, clay content combined with moisture content, clay content combined with driving velocity, and moisture content combined with driving velocity. Overall, this study quantifies the relationships among soil clay content, soil moisture content, soil density, driving velocity and driving resistance, subsidence depth of a tracked tractor using the modeling approach; and according to the soil clay content and density, the model can be used to optimize the soil moisture content and driving velocity when the driving resistance is minimum.

Taking the west lakeside oasis of Bosten Lake as the study area, using the measured soil organic carbon content and hyperspectral data, the successive projection algorithm (SPA) was used to filter the characteristic variables from the full-band spectral data, and then the full-band and characteristic bands were used to construct partial least square regression (PLSR) and support vector machine (SVM) models to estimate soil organic carbon content. The results showed that: 1) The soil organic carbon content varied from 0.75 to 48.13 g/kg, with an average value of 13.31 g/kg, showed moderate variability, with a coefficient of variation of 63.19%. 2) The soil organic carbon content and the original spectral reflectance showed a negative correlation, with －0.62＜correlation coefficient (r)＜－0.07. After the bands were preprocessed by Savitzky-Golay-standard normal variate-first derivative (SG-SNV-1st Der), the number of bands that passed the extremely significant test (P＜0.01) were 414, mainly concentrated in 487-575, 725-998 and 1 464-1 514 nm. The correlation between 788, 800 and 1 768 nm was the highest, with the correlation coefficients of more than 0.80. 3) After the spectra were preprocessed by SG-SNV-1st Der, the coefficient of determination (R²) of validation set of PLSR model constructed by SPA was 0.79; root mean square error (RMSE) was 3.58 g/kg; residual prediction deviation (RPD) was 1.99; and ratio of performance to interquartile distance (RPIQ) was 2.23. However, the validation set constructed by SPA combined with SVM was R²=0.81, RMSE=3.16 g/kg, RPD=2.25, RPIQ=2.53. It shows that the model constructed by SPA combined with SVM can better estimate soil organic carbon content in the study area.

In view of actual situations such as light change, soil, branch and leaf background and fruit overlap in the natural environment, which causing the problem of red tomato recognition during the robotic picking process was not accurate, a tomato image recognition method based on circle fitting algorithm was proposed. We collected the images of tomato by camera, used the red, green, blue (RGB) color space based Matlab as simulation experiment, and preprocessed the tomato images with red-green (R-G) color component. Then, edge detection algorithm, threshold segmentation and watershed segmentation methods were adopted to segment tomato target and the background, respectively. The Otsu segmentation method of threshold segmentation was adopted, which was best to segment target. We used the back propagation-artificial neural network (BP-ANN) and circle fitting algorithm to recognize the tomato fruit. Finally, the contour, centroid and radius of the red tomato were obtained. The results of red tomato images were statistically analyzed, and the recognition rate of circle fitting algorithm was as high as 90.07%. This algorithm not only has a high recognition rate for single fruit, but also solves the problem of multiple fruit overlapping in a complex environment, which lays a good foundation for the following robotic picking work.

Rice species identification and the retrieval of key phenological stages were processed in the area around Jinhu County, Huai’an City, Jiangsu Province, based on multi-temporal full-polarization synthetic aperture radar (SAR) data. By extracting and analyzing the change characteristics of the time-series curve of the polarization characteristic parameters of rice, the polarization characteristic parameters that are sensitive to the changes of rice phenology were screened out, and a radar phenology index (RPI) that can reflect the key phenological changes of rice growth was constructed, and then was reconstructed by Savitzky-Golay (S-G) filter. The key phenological stages of rice were retrieved by the RPI. The results showed that the response of the polarization parameter Shannon entropy was quite different between japonica rice and indica rice, indicating that Shannon entropy could be used to identify indica rice and japonica rice with precisions of 92.38% and 95.10%, respectively; the curve derivative method was used to extract the characteristic points of the time-series RPI curve of rice, and three key phenological stages of rice were identified. The dates of the identified key phenological stages of rice were all within ±16 d from the date obtained in the field survey. The above results show that the use of RPI can more accurately retrieve the key phenological stages of rice.

In order to investigate the effects of structural and working parameters on the working resistance and subsidence depth of boat-type tractor in paddy field, simplified prototypes of boat-type tractors with testing system were developed. Ground ratio pressure, working speed, curvature radius of board, ground contact angle and depth of surface water of paddy soil were taken as factors, and working resistance and subsidence depth were taken as evaluation indexes. The experiments were conducted through the paddy soil platform which was filled with filtered silt loam. Results showed that all the factors had significant effects on the two evaluation indexes. The working resistance and subsidence depth increased monotonously with the increase of ground ratio pressure within 250-350 kg/m². However, when the ground ratio pressure was beyond 300 kg/m², the increase of two evaluation indexes was much slower. When working speed, curvature radius of board and ground contact angle were within 0.2-1.0 m/s, 300-500 mm and 25.0°-35.0°, respectively, the working resistance and subsidence depth increased after a slight decrease on the whole. The working resistance and subsidence depth reached the minimum values at the working speed of 0.6 and 0.8 m/s, respectively; and subsidence depth and working resistance reached the minimum values with ground contact angle of 27.5° and 30.0°, respectively. When the depth of surface water of paddy soil increased within 10-50 mm, the subsidence depth decreased monotonously, while the working resistance increased after a slight decrease on the whole. The working resistance reached the minimum value with the depth of surface water of 20 mm. In conclusion, the optimum working and structural parameters for boat-type tractors in paddy field are 0.6-0.8 m/s for working speed, 350-450 mm for curvature radius of board, 27.5°-30.0° for ground contact angle, and 20-40 mm for depth of surface water. The result can provide references for the design and operation of boat-type tractor in paddy field.

In order to realize the mechanization of the process of the orchard seedling leaving the nursery, the structure of seedling lifting and soil cleaning machine was designed, and the parametric design was carried out for key components such as seedling lifting device and soil cleaning device, and then finite element analysis and optimization were carried out for the seedling lifting shovel. The results showed that the maximum equivalent stress decreased from 5.026 MPa to 0.238 MPa, and the maximum equivalent strain decreased from 2.141 to 0.663. The overall deformation was far less than 5 mm, which met the design requirements. Finally, the reliability of the prototype was verified by field test. The results showed that the main root length of the seedlings was between 120 mm and 150 mm, and the injury rate of the seedlings was low, which met the requirements of agronomy. The size parameters of the machine are expected to provide a reference for the design and research of the seedling lifting and soil cleaning device.

In order to improve the uniformity of the flow when fertilizer apparatus is working, a kind of fertilizer apparatus owned circular arc gears was designed. Taking the circular arc gear discharging fertilizer apparatus as the research object, this study used discrete element method (DEM) simulation to analyze the influence of two key components including the arc radius of concave-groove of circular arc gear discharging fertilizer (r₁) and the minimal length between two mutual meshing arc gears (l). The results indicated that the arc radius of concave-groove of circular arc gear discharging fertilizer had significant effect on the coefficient of determination of discharging fertilizer amount in unit time, and the minimal length between two mutual meshing arc gears had significant effect on the coefficient of variation of the stability of fertilizer sowing amount. The optimum structural parameters were 8.54 mm as the arc radius of concave-groove and 5.22 mm as the minimal length between two mutual meshing arc gears. Upon this circumstance, the coefficient of variation was 0.28, and the coefficient of determination was 0.997 2. The optimum structure was selected to do the bench test. The results indicated that the coefficient of variation of the quality changes of discharging fertilizer was 0.27, and the coefficient of determination of fertilizer discharging amount in unit time was 0.998 0. The results of simulation experiment were basically consistent with the real result.

Aiming at the poor collection of the Hermetia illucens insect sand, combining with mechanical and physical properties of insect sand, the bidirectional spiral collecting device was designed, and the working principle and structural parameters of the device were analyzed and calculated. In order to determine the working performance of the bidirectional spiral collecting device for H. illucens insect sand and get the optimum parameters, the bidirectional spiral speed, discharging spiral speed, insect sand thickness were taken as the testing factors, and single lap discharge and homework time were taken as the evaluating indicators, with insect sand (moisture content of 43.6%) as experimental object. The single- and multi-factor orthogonal test analysis were carried out. The results showed that the factors affecting the single lap discharge were as follows: discharging spiral speed＞insect sand thickness＞bidirectional spiral speed. The factors affecting the homework time were as follows: insect sand thickness＞discharging spiral speed＞bidirectional spiral speed. The multi-factor orthogonal test had the same results with variance significance test, and discharging spiral speed and insect sand thickness had significant effects on single lap discharge (P＜0.05); for the homework time, insect sand thickness had significant effect (P＜0.05). The two evaluating indicators were analyzed based on the comprehensive scoring method, and the optimal operating parameters of the device were obtained with the bidirectional spiral speed of 78 r/min, discharging spiral speed of 68 r/min, insect sand thickness of 4 cm, mean single lap discharge of 142 g, and mean homework time of 48 s, which meet the requirements of H. illucens insect sand collection. This research provides a reference for the improvement and optimization of spiral collecting equipment for H. illucens insect sand.

We assessed the effects of ambient temperature and relative humidity on the infrared thermography (IRT) temperature of dairy cows, and evaluated the IRT body surface temperatures as effective surrogates of cow’s rectal temperature. The rectal temperature and the IRT temperatures (eyes, nose, skin) of 171 cows were measured and obtained at the ambient temperatures of －1 to 36 ℃. The standard deviations of IRT temperatures decreased with the rising of ambient temperatures. Both ambient temperature and relative humidity had significant impact on the IRT temperatures (P＜0.000 1). The effect of relative humidity on the IRT temperatures was less than that of ambient temperature. Regression analysis showed that significant correlation existed between the rectal temperature and the IRT temperatures of eyes, nose and skin (P＜0.000 1), with R² being 0.494 0, 0.328 0, and 0.273 1 and the standard errors being 0.17, 0.19, and 0.20 ℃, respectively. The correlation was improved notably by breaking down the ambient temperature (T) into three segments, i.e., T≤10 ℃, 10 ℃＜T≤26 ℃, T＞26 ℃. Linear regression based on the segmented ambient temperature outperformed the original univariate linear correlation. Therefore, the influence of ambient temperature on the IRT temperatures cannot be ignored, and the IRT temperatures can accurately predict the body temperature using the segmented ambient temperature. Although all IRT temperatures (eyes, nose, and skin) could indicate the cow’s body (rectal) temperature, the IRT temperature of eyes is preferred because it is simple to measure, least affected by the ambient temperature, and has the highest accuracy.

In order to solve the problem of crop loss caused by the internal wheel difference when the front wheels of the field management machine were turned, this study proposed a path tracking algorithm based on the four-wheel steering field management machine. Taking the 3WPZ-750 field management machine as the research object, a set of path tracking control system was designed. First, the mechanical structure design of the research subject’s steering outriggers and the principle of the automatic steering hydraulic system were explained. Second, the technical route of the path tracking host computer control system based on the original control system was outlined. Then, based on the four-wheel steering model, a pure tracking algorithm was used for wheel steering angle control research. Finally, the path tracking control interface was written based on Microsoft integrated development environment (visual studio). Through its experimental platform, the straight path tracking and curved path tracking experiments were carried out on the cement pavement and the southern paddy field environment, respectively. The results showed that at the speed of 0.5 m/s, the field management machine had a path tracking error of 6.92 cm on the cement pavement, and its standard deviation was 4.84 cm; while the mean value of the path tracking error of the fixed curvature curve was 12.49 cm, and its standard deviation was 9.16 cm. At the speed of 0.2 m/s, on the southern paddy field, the mean value of the path tracking error was 2.25 cm, and its standard deviation was 1.35 cm; while the mean tracking error of the fixed curvature curve was 8.72 cm, and its standard deviation was 5.59 cm. The above results indicate that the path tracking algorithm proposed in this study can meet the needs of field management machines and field operations, and provide a basis for automatic driving of agricultural machinery.

In order to investigate the potential of near-infrared spectroscopy for accurately predicting the capsaicin content in fresh chili peppers, taking Hangzhou chili pepper as a material, the near-infrared spectroscopy was employed to acquire spectral information of chili peppers, and high-performance liquid chromatography was conducted to obtain the reference values of capsaicin content. Three different variable selection methods with successive projection algorithm (SPA), competitive adaptive reweighted sampling (CARS) and uninformation variable elimination (UVE) were performed to select the optimal wavelengths. Partial least square (PLS) models based on full spectra and optimal wavelengths were then developed to predict the capsaicin content, and the prediction performances and operation efficiency were compared. The results showed that the CARS-PLS model yielded the best prediction performances, with the correlation coefficient of 0.838 6 and root-mean-square error of prediction set of 0.014 8 mg/g. In addition, compared with the full spectra of 200 wavelengths, the number of the optimal wavelengths selected by CARS was reduced by 96%, which indicated that optimal wavelengths can be used to simplify the models and improve the operation efficiency. The above results demonstrate that the near-infrared spectroscopy based on optimal wavelengths is feasible for the detection of capsaicin content.

The applicability of ISO 8178 C1 eight steady-state emission test cycle to small-scale agricultural machinery diesel engines with huge reserves remains to be discussed, so a diesel engine working condition recorder based on a single chip microcomputer was designed to collect and record the actual working condition information of small-scale agricultural machinery diesel engines. Through data analysis and extraction, a six-condition steady-state emission test cycle suitable for domestic small-scale agricultural machinery diesel engine was proposed and verified by bench test. The results of data analysis showed that the typical working conditions of domestic small-scale agricultural machinery diesel engine were quite different from those of the ISO 8178 C1 eight operating cycle. The results of bench emission test showed that the comprehensive emission level of pollutants of small-scale agricultural machinery diesel engine under the six working conditions was obviously different from that under the ISO 8178 C1 eight working conditions, and in most cases, the emission level under the six working conditions was worse than that under the eight working conditions. The above results can provide some references for the formulation and revision of relevant emission standards and the research on emission control technology of small-scale agricultural machinery diesel engine.

According to the agricultural characteristics of sugarcane planting in central Zhejiang Province, i.e. intertilling, fertilizing, ridging and earthing-up, the V-shaped deep trench sugarcane cultivator is equipped with good working parts, such as soil digging, breaking, casting and trench clearing. It can form a good V-shaped groove cross section. This paper analyzed the agronomic requirements of sugarcane planting in central Zhejiang Province and narrow row spacing, designing the key parts of the whole machine, such as V-shaped deep trench ridging cutter plate, trench clearing, etc. Field experiment results showed that the efficiency of the management machine could reach 0.19 hm²/h. The trench depth and the thickness of soil could reach 230 mm on average and 70 mm, respectively. The trench depth stability coefficient reached about 85%. The soil breaking rate reached about 95%. The fertilizer distributor had a good effect on granular fertilizer. No obvious damage and seedling emergence were found in sugarcane seedlings. The design of the machine meets the agronomic requirements of sugarcane planting in central Zhejiang Province.

In order to reduce the impact of mosaic disease on soybean production and explore a theoretical basis for rapid detection of early soybean mosaic disease, a novel hyperspectral detection method for early soybean mosaic disease based on convolutional neural network (CNN) model was proposed. First, soybean samples inoculated separately with SC3, SC7 viruses and normal soybean samples (Nannong 1138-2) were collected through a hyperspectral system. A region of 40 pixel×40 pixel was selected as the region of interest (ROI) and the average spectral information of ROI was extracted. Then, the CNN model was established based the hyperspectral image. Finally, the recognition rate of the training set in the CNN model reached 94.79%, and the recognition rate of the prediction set reached 92.08%. The recognition rate of the mosaic leaf inoculated with SC3 virus was 88.75%, and the recognition rate of the mosaic leaf inoculated with SC7 virus was 93.13%, and the recognition rate of the normal leaf was 94.38%. Compared with the least square-support vector machine (LS-SVM) and extreme learning machine (ELM) models, the CNN model can more fully extract the deep features of the spectrum, and the extracting effect was significantly improved. Thus, this research shows that the CNN model can achieve the detection of early soybean mosaic disease more accurately, and combining the CNN model with hyperspectral methods also provides a new idea for plant disease detection.

In order to realize the rapid and nondestructive recognition of slight bruises of apples, a hyperspectral imaging technique (400-1 000 nm) was used. Hyperspectral images of sound and different damage time of Fuji apples were collected, and the average spectral reflectance and entropy were extracted from the region of interest (ROI) of the image. All the samples were divided into training set and test set (2∶1). The characteristic wave- bands extracted based on the spectral average reflectance and entropy using RELIEF algorithm were 17, 30, 35, 51, 61, 66, 94 and 120, respectively. Then, based on full wavebands and characteristic wavebands, an extreme learning machine (ELM) model was built, as comparison with support vector machine (SVM) and K-mean algorithm. The results showed that the recognition accuracy of ELM model for the test set based on the full wave- bands was 94.44%, and the accuracy of the Re-ELM model based on the characteristic wavebands was 96.67%, and the accuracy of the Re-SVM and Re-K mean models for the characteristic wavebands were 92.22% and 91.67%, respectively, which demonstrated that the Re-ELM was a more effective method for the bruise apple classification. Subsequently, an apple damage detection algorithm based on the characteristic wavebands and image processing was proposed, which performed an independent component algorithm (ICA) transformation of the characteristic wavebands, and selected the third component image of the ICA transformation, and used adaptive threshold segmentation to obtain the bruise area on apples. The final detection accuracy of apple damage detection algorithm based on the image processing technology was over 94%, which indicates that the algorithm is efficient for identifying slight bruises of apples.

To realize pollination mechanization for hybrid rice seed production, a collision-pneumatic hybrid rice pollination machine was designed and manufactured. The airflow velocity in the air hole of pollination tubes with diameter of 50, 55, 60, 65 and 70 mm was simulated by computational fluid dynamics (CFD). The results showed that the average airflow velocity simulated in the air hole of pollination tube with the diameter of 60 mm was similar to the measured value. The field pollination experiments at the average airflow velocity of 9.35, 11.82 and 15.07 m/s were conducted with the spacing between the air hole and collision rope of 50, 100 and 150 mm respectively. The pollen amount received by four line female parents beside male parents was super to that of the artificial pollination and distributed uniformly. The pollen amount distributed on the four line female parents increased with the distance between the air hole and collision rope. However, the pollen amount was not obviously increased when the distance between the air hole and collision rope increased to 100-150 mm, and the average airflow velocity of the air hole increased to 11.82-15.07 m/s. In conclusion, the optimal pollination parameters for the collision-pneumatic hybrid rice pollination machine are obtained when the airflow velocity is 11.82 m/s, and the distance between the air hole and collision rope is 100-150 mm.

Parameters of wheat crop growth model WOFOST (world food studies) were discriminated, and the sensitivity of crop and soil parameters in WOFOST model was analyzed using OAT (one-at-a-time) analysis method. Meanwhile, the simulation results of wheat growth were explained. Results showed that the relative sensitivities of seven parameters of WOFOST model, including TSUM1, FRTB, CVO, CVS, DVSI, SPAN, SLATB, were all greater than 0.5, which presented strong sensitivity and had greater influence on the model simulation results. The parameters which had greater influence on the WOFOST model based on different levels of yield and production were almost the same, and the relative sensitivity had no significant difference. Simulation results of leaf area index (LAI) showed that the days of wheat from sowing to flowering were 137, and the days of wheat from flowering to maturity were 66, which was in accordance with the actual days. This study provides a basis for the application and parameter adjustment of WOFOST model.

Focusing on the defects in fiber structure of corn stalk feed, we proposed a novel production method of ruminant animal fodder by co-treatment with ultrasound and alkalization. By using central composite design with four factors at five levels and response surface analysis, we investigated the effects of ultrasonic power, ultrasonic time, liquid-to-solid ratio and ultrasonic power density in the container on extraction of cellulose and removal of hemicellulose and lignin in corn stalk. In addition, the two-phase regression model was established by taking the contents of lignin, cellulose, and hemicellulose as response values, respectively. The model explained the factor contribution rate of ultrasonic factors to response value and the significant influence on response surface model by the interaction between ultrasonic power, ultrasonic time, liquid-to-solid and ultrasonic power density in the container. The results showed that the determination coefficients of lignin, cellulose and hemicellulose models were 0.69, 0.79 and 0.73, respectively. The process parameters were optimized by the response surface model, and the optimal working parameters of each ultrasonic factor were obtained as ultrasonic power 99 W, ultrasonic time 20 min, liquid-to-solid ratio 7.8:1, and ultrasonic power density in the container 2.05 W/mL. The relative errors of actual values and predicted values were less than 13%, 12% and 14%, respectively. It is indicated that reasonable matching of ultrasonic parameters is helpful to reduce the content of lignin and cellulose of corn stalk feed and increase livestock digestibility, and provides scientific basis for the design of ultrasonic alkalization device and selection of working parameters.

Percent ground cover of vegetation is an important parameter which received attention of both agronomists and ecologists. Not only does it reflect dynamic growth of plants in a long time, but also it is associated with abstraction of photosynthesis available radiation (APAR) of plants. So far as the maize crop cover is concerned, current researches mainly focused on calculating percent ground cover of maize on bare ground. It is a fact that plastic film mulching has been widely adopted for maize planting due to its effect on reducing water loss, regulating soil temperature, improving the infiltration of rainwater into the soil, enhancing soil water retention, accelerating crop growth, and significantly increasing crop yield. In addition, the recent advances in image analysis software offered potential for analyzing the digital camera images of habitat to objectively quantify ground cover of vegetation in a repeatable and timely manner too. Here we evaluated use of Matlab software for analyzing the digital photographs of plastic-film maize to quantify the percent ground cover.
In this study, the images of plastic-film maize were firstly taken by smart phone under weak light condition, which were JPEG (joint photographic expert group) format here and were in 1 358×1 314 resolution. Then the method combined the K-mean clustering analysis of hue (H) and saturation (S) color components with performing a corresponding mathematical operation was proposed to discriminate the maize and background. The proposed method was comprised of three main steps. First, color images yielding red (R), green (G), and blue (B) subimages were mathematically transformed to hue (H), saturation (S), and intensity (I) ones. And then, the images were respectively segmented using the methods of excess green and excess green minus excess red. Second, the K-mean clustering analysis of H and S color components was carried out. Finally, the color difference operation between the K-mean clustering analysis of H and S color components was performed for segmentation of target object.
Results of images processing indicated that the images, which were segmented respectively by excess green, excess red, excess green minus excess red, and Otsu thresholding of excess green, excess red and excess green minus excess red, showed incomplete construct of maize and plastic film, but relatively satisfactory results were achieved by clustering analysis of H and S color components. Specifically, the K-mean clustering analysis of H color component clearly delineated leaf edge of maize, and the K-mean clustering analysis of S color component produced complete plastic film construct. The maize plant was successfully separated from plastic film, soil and other backgrounds by application of the color difference operation between the K-mean clustering analysis of H and S color components. Root mean square error (RMSE) and error rate were calculated to verify the reliability of the method proposed in this paper for segmentation of maize plant. The results showed that the RMSE and error rate of segmentation were 0.004 2 and 3.37%, respectively. The low RMSE and error rate further confirmed the rationality of the method used in this paper.
In conclusion, the method presented in this paper for image segmentation of plastic-film corn canopy is reliable under the weak light condition.

The area of facility agriculture in China ranks forefront in the world. In recent years, as an emerging technology of environmental management inside the greenhouse, ground source heat pump (GSHP) is sufficiently emphasized by many scholars. While most of them only focus on heating effect of greenhouse rather than its cooling effect, due to some severe crop problems induced by relatively high temperature in South Jiangsu during summer. In this study, some related indexes were analyzed from the perspective of cooling effect, and the effectiveness and practicability of the cooling system were evaluated in the experimental greenhouse.
The experiment was conducted in two plastic greenhouses in Zhangjiagang City from May 5 to July 17, 2016. The greenhouses were located from north to south, with 88 m long, 7 m wide, and 3 m high, which were assembled with steel tubes and ethylene vinyl acetate (EVA) films. Two greenhouses with similar external environmental conditions and specifications were selected for treatment and control sets, respectively. The air inlet was set at the ridge and the air outlet was on the west side of greenhouse with 20 cm vertical distance from the ground. The buried depth of pipe processing heat exchange in the treatment greenhouse was 60 cm, including 14 groups of polyvinyl chloride (PVC) pipes with the diameter of 110 mm. With 160 W of the input power, 0.2 m³/s of the measured air quantity, and 40 r/s of the rotation, the fan was turned on at 6:00 a.m. and turned off at 6:00 p.m. in the same day. The bottom ventilation was set at 80 cm from east and west side along the greenhouses. The greenhouses were planted with watermelon and ventilated normally from 6:00 a.m. to 6:00 p.m. every day without using sunshade net. The ambient temperature and humidity, and the soil temperature at 60 cm, 40 cm and 15 cm were tested, and the processing humiture at inlet and outlet of heat exchange pipe of greenhouse was calculated at a 10-minute frequency. Then enthalpy difference, heat accumulation capacity, average heat flux, average density of heat flow, and energy efficiency and consumption were analyzed.
The result indicated that from May 5 to July 17, the average temperature reduction in the treatment greenhouse with heat exchange pipe was 3.4 ℃ in sunny days, 1.5 ℃ in cloudy days and 0.8 ℃ in rainy days, compared with the control without heat exchange pipe. The change in humidity was comparatively insignificant between the treatment and control, which were both 40%-90% in sunny days, 60%-100% in cloudy days, and 80%-100% in rainy days. In three continuous sunny days, the ground temperature at the depth of 60 cm was basically at the constant temperature of 21.3 ℃ in the control greenhouse, and it fluctuated at 21.8 ℃±0.4 ℃ in the treatment greenhouse; the average daily temperature at the depth of 40 cm was basically at 22.7 ℃, rising at 0.1 ℃/d for the control, and it was 23.9 ℃, rising at 0.4 ℃/d in a wave mode for the treatment; the average daily temperature at the depth of 15 cm was basically at 24.8 ℃ for the control, and it was 25.7 ℃, rising at 0.5 ℃/d in a wave mode for the treatment. In heat exchange, enthalpy difference was 0.166-9.560 kJ/kg; the heat accumulation capacity was 3.94×10⁵ kJ; the daily electricity consumption was 0.8×10⁵ kJ, and the coefficient of performance (COP) was 4.21 in the treatment greenhouse. Compared with the investment cost of air condition (AC) with the highest energy efficiency standard, the investment cost of the shallow geothermal exchange system was only 15.5% and the energy consumption ratio was 1.4 times higher than that of AC.
Therefore, we conclude that the coolingeffect using the heat exchange system at superficial layer is significant and promising in south Jiangsu in summer.