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The mechanization of red jujube industry is a high-potential agricultural research field in China. In this study, a vibration harvesting device has been developed for jujube trees that features adjustable vibration frequency and amplitude. The device is designed to make jujube trees vibrate with varying diameters by utilizing different vibration frequencies and amplitudes according to the tree’s size such that jujubes can be harvested efficiently. The study completed the structural design of excitation and fruit collection mechanisms based on the working principle of vibration harvesting. The red jujube excitation mechanism was dynamically simulated using ADAMS, and it was found that the acceleration at the end of the vibration rod of the mechanism had a tendency to sharply increase and decrease, which was conducive to the vibration shedding of red jujube. A collision model between the red jujubes and the fruit collection umbrella was constructed, and the fruit collection mechanism was structurally simulated using ANSYS. The tests showed that the device effectively harvested red jujubes from nine types of jujube trees with diameter ranges of 29.15–31.26 mm, 49.56–52.34 mm, and 65.23–73.25 mm. The average net harvesting rates were 93.98%, 94.71%, and 94.33%, and the average fruit collection efficiencies were 95.78%, 89.43%, and 85.04%, respectively. These results demonstrate the effectiveness of the excitation and collection mechanisms and provide a theoretical basis for the development of vibratory harvesting devices for red jujubes.

Due to complex environmental factors such as illumination, shading between leaves and fruits, shading between fruits, and so on, it is a challenging task to quickly identify red jujubes and count red jujubes in orchards. A counting method of red jujube based on improved YOLOv5s was proposed, which realized the fast and accurate detection of red jujubes and reduced the model scale and estimation error. ShuffleNet V2 was used as the backbone of the model to improve model detection ability and light the weight. In addition, the Stem, a novel data loading module, was proposed to prevent the loss of information due to the change in feature map size. PANet was replaced by BiFPN to enhance the model feature fusion capability and improve the model accuracy. Finally, the improved YOLOv5s detection model was used to count red jujubes. The experimental results showed that the overall performance of the improved model was better than that of YOLOv5s. Compared with the YOLOv5s, the improved model was 6.25% and 8.33% of the original network in terms of the number of model parameters and model size, and the Precision, Recall, F1-score, AP, and Fps were improved by 4.3%, 2.0%, 3.1%, 0.6%, and 3.6%, respectively. In addition, RMSE and MAPE decreased by 20.87% and 5.18%, respectively. Therefore, the improved model has advantages in memory occupation and recognition accuracy, and the method provides a basis for the estimation of red jujube yield by vision.

Rapid and accurate crop chlorophyll content estimation is crucial for guiding field management and improving crop yields. This study explored the potential for potato chlorophyll content estimation based on unmanned aerial vehicle (UAV) multispectral imagery. To search the optimal estimation method, three parts of research were conducted as following. First, a combination of support vector machines (SVM) and a gaussian mixture model (GMM) thresholding method was proposed to estimate fractional vegetation cover (FVC) during the potato growing period, and the proposed method produced efficient estimates of FVC; among all the selected vegetation indices (VIs), the soil adjusted vegetation index (SAVI) had the highest accuracy. Second, the recursive feature elimination (RFE) algorithm was utilized to screen the VIs and texture features derived from multispectral images: three Vis, including modified simple ratio (MSR), ratio vegetation index (RVI) and normalized difference vegetation index (NDVI); three texture features, including correlation in the NIR band (corr-NIR), correlation in the red-edge band (corr-Red-edge) and homogeneity in the NIR band (hom-NIR), showed higher contribution to chlorophyll content estimation. Finally, a stacking model was constructed with K-Nearest Neighbor (KNN), a light gradient boosting machine (light-GBM), SVM algorithm as the base model and linear fitting as the metamodel, and four machine learning algorithms (SVM, KNN, light-GBM and stacking) were used to build the chlorophyll content estimation model suitable for different growing seasons. The results were: (1) The performance of the estimation model could be improved based on both VIs and texture features over using single-type features, and the stacking algorithm yielded the highest estimation accuracy with an R2 value of 0.694 and an RMSE value of 0.553; (2) When FVC was added, the estimation model accuracy was further improved, and the stacking algorithm also produced the highest estimation accuracy with R2 value of 0.739, RMSE value of 0.511 (3) When comparing modeling algorithms, stacking algorithms had greater advantages in the estimation chlorophyll content with potato plants than using single machine learning algorithms. This study indicates that taking into account the combination of VIs reflecting spectral characteristics, texture features reflecting spatial information and the FVC reflecting canopy structure properties can accomplish higher chlorophyll content estimation accuracy, and the stacking algorithm can integrate the advantages of a single machine learning model, with great potential for estimation of potato chlorophyll content.

Precise nitrogen (N) application ensures the best N status of potato plants to improve crop growth and food quality and to achieve the best N use efficiency. Four N fertilization levels (0, 2, 4 and 6 g N pot−1) were used to establish a critical N dilution curve (CNDC) of potato plants cultivated in substrates with a greenhouse environment. RGB images of potato plants were obtained, and a red–green fit index (RGFI) was calculated based on the linear relationship between R and G channels and the principle of the excess green index (EXG). The N in the substrate can meet the nutritional requirements of potato plants during the first 35 days after emergence. In order to solve the complex sampling problem of maintaining a sufficient N strip for aboveground dry biomass (DM) and crop nitrogen concentration, a reference curve method for detecting N status was proposed. RGFI and SPAD values from the economically optimum 4 g N pot−1 treatment were used to derive the reference curve. The RGFI and SPAD values from the 4 g N pot−1 treatment had high correlations and were fitted with a second-order polynomial function with an R2 value of 0.860 and an RMSE value of 2.10. The validation results show that the N concentration dilution curve constructed by RGFI and SPAD values can effectively distinguish N-limiting from non-N-limiting treatments, CNDCs constructed based on RGFI and SPAD values could be used as an effective N status monitoring tool for greenhouse potato production.

Achieving rapid and accurate localization of winter jujubes in trees is an indispensable step for the development of automated harvesting equipment. Unlike larger fruits such as apples, winter jujube is smaller with a higher density and serious occlusion, which obliges higher requirements for the identification and positioning. To address the issues, an accurate winter jujube localization method using improved YOLOX-Nano network was proposed. First, a winter jujube dataset containing a variety of complex scenes, such as backlit, occluded, and different fields of view, was established to train our model. Then, to improve its feature learning ability, an attention feature enhancement module was designed to strengthen useful features and weaken irrelevant features. Moreover, DIoU loss was used to optimize training and obtain a more robust model. A 3D positioning error experiment and a comparative experiment were conducted to validate the effectiveness of our method. The comparative experiment results showed that our method outperforms the state-of-the-art object detection networks and the lightweight networks. Specifically, the precision, recall, and AP of our method reached 93.08%, 87.83%, and 95.56%, respectively. The positioning error experiment results showed that the average positioning errors of the X, Y, Z coordinate axis were 5.8 mm, 5.4 mm, and 3.8 mm, respectively. The model size is only 4.47 MB and can meet the requirements of winter jujube picking for detection accuracy, positioning errors, and the deployment of embedded systems.

A trunk segmentation and a diameter measurement of red jujubes are important steps in harvesting red jujubes using vibration harvesting robots as the results directly affect the effectiveness of the harvesting. A trunk segmentation algorithm of red jujubes, based on improved Pyramid Scene Parsing Network (PSPNet), and a diameter measurement algorithm to realize the segmentation and diameter measurement of the trunk are proposed in this research. To this end, MobilenetV2 was selected as the backbone of PSPNet so that it could be adapted to embedded mobile applications. Meanwhile, the Convolutional Block Attention Module (CBAM) was embedded in the MobilenetV2 to enhance the feature extraction capability of the model. Furthermore, the Refinement Residual Blocks (RRBs) were introduced into the main branch and side branch of PSPNet to enhance the segmentation result. An algorithm to measure trunk diameter was proposed, which used the segmentation results to determine the trunk outline and the normal of the centerline. The Euclidean distance of the intersection point of the normal with the trunk profile was obtained and its average value was regarded as the final trunk diameter. Compared with the original PSPNet, the Intersection-over-Union (IoU) value, PA value and Fps of the improved model increased by 0.67%, 1.95% and 1.13, respectively, and the number of parameters was 5.00% of that of the original model. Compared with other segmentation networks, the improved model had fewer parameters and better segmentation results. Compared with the original network, the trunk diameter measurement algorithm proposed in this research reduced the average absolute error and the average relative error by 3.75 mm and 9.92%, respectively, and improved the average measurement accuracy by 9.92%. To sum up, the improved PSPNet jujube trunk segmentation algorithm and trunk diameter measurement algorithm can accurately segment and measure the diameter in the natural environment, which provides a theoretical basis and technical support for the clamping of jujube harvesting robots.

A significant progress in modern power grids is witnessed by the tendency of becoming complex cyber-physical systems. As a fundamental physical infrastructure, smart meter in the demand side provides real-time energy consumption information to the utility. However, ensuring information security and privacy in the meter data aggregation process is a non-trivial task. This study proposes a distributed, privacy-preserving, and secure meter data aggregation framework, backed up by Blockchain and homomorphic encryption (HE) technologies. Meter data are aggregated and verified by a hierarchical Blockchain system, in which the consensus mechanism is supported by the practical Byzantine fault tolerance algorithm. On the top of the Blockchain system, HE technology is used to protect the privacy of individual meter data items during the aggregation process. Performance analysis is conducted to validate the proposed method.