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.

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.

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.

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 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.