As strawberries are a widely grown cash crop, the development of strawberry fruit-picking robots for an intelligent harvesting system should match the rapid development of strawberry cultivation technology. Ripeness identification is a key step to realizing selective harvesting by strawberry fruit-picking robots. Therefore, this study proposes combining deep learning and image processing for target detection and classification of ripe strawberries. First, the YOLOv8+ model is proposed for identifying ripe and unripe strawberries and extracting ripe strawberry targets in images. The ECA attention mechanism is added to the backbone network of YOLOv8+ to improve the performance of the model, and Focal-EIOU loss is used in loss function to solve the problem of imbalance between easy- and difficult-to-classify samples. Second, the centerline of the ripe strawberries is extracted, and the red pixels in the centerline of the ripe strawberries are counted according to the H-channel of their hue, saturation, and value (HSV). The percentage of red pixels in the centerline is calculated as a new parameter to quantify ripeness, and the ripe strawberries are classified as either fully ripe strawberries or not fully ripe strawberries. The results show that the improved YOLOv8+ model can accurately and comprehensively identify whether the strawberries are ripe or not, and the mAP50 curve steadily increases and converges to a relatively high value, with an accuracy of 97.81%, a recall of 96.36%, and an F1 score of 97.07. The accuracy of the image processing method for classifying ripe strawberries was 91.91%, FPR was 5.03%, and FNR was 14.28%. This study demonstrates the program’s ability to quickly and accurately identify strawberries at different stages of ripeness in a facility environment, which can provide guidance for selective picking by subsequent fruit-picking robots.

The practical problems of existing methods of walnut cracking under compression loading, including incomplete walnut-shell crushing, broken walnut kernels, and so on, are widespread in walnut processing and are constraints that hinder mechanized walnut processing. Therefore, attempts have been made to design and optimize a multi-point extrusion walnut cracking device. For this, walnuts were fed manually into a cracking unit through the hopper. The tangential force of the grading roller graded the walnuts and dropped them into the gap between the rotating cracking roller and extrusion plate, causing them to crack. The developed machine was tested and the parameters were optimized using a central composite design (CCD). The objective functions involving the cracking angle (CA: 0.17, 0.27, 0.52, 0.76, 0.86°) and roller speed (RS: 63, 75, 105, 135, 147 r/min) were calculated. The shell cracking rate (SCR), whole kernel rate (WKR), and specific energy consumption (Es) regression models were established using the quadratic regression orthogonal combination test and the parameters were optimized using MATLAB software. The results showed that the most significant factors for the RS were the linear terms of the SCR and WKR, whereas for the CA the most significant factor was the linear term of the Es. The interaction term of the two factors had a significant effect on the three indicators. The optimal parameter combination was determined to be 0.47° for the CA and 108 r/min for the RS. On this basis, the adaptability test showed that the cracking device had a better cracking effect on walnuts with a gap between the walnut shell and kernel greater than 1.6 mm and a shell thickness less than 1.2 mm. The results have practical significance for the design of walnut cracking devices.

Calibration and experimental verification of discrete element parameters of Panax notoginseng root

This paper introduces a cooperative positioning algorithm for agricultural vehicles, which uses the relative distance of the workshop to improve the performance of the Global Navigation Satellite Systems (GNSS), to improve the positioning accuracy and stability. Firstly, the extended Kalman filter (EKF) fuses the vehicle motion state data with GNSS observation data to improve the independent GNSS positioning accuracy. Subsequently, vehicle state and observation models are formulated using Bayesian theory, incorporating GNSS/UWB data with UWB tag network ranging and with GNSS positioning data among agricultural vehicles and Inter-Vehicular Ranges (IVRs). This integration addresses the significant drift issue in GNSS elevation positioning by employing a high-dimensional decoupling algorithm, standardizing the discrete elevation data, and improving the data’s continuity and predictability. A particle filter is used to refine the vehicle’s position estimation further. Finally, experiments are carried out to verify the robustness of the proposed algorithm under different working conditions.

Integrating modern information technology with traditional agriculture has made agricultural machinery navigation essential in PA (precision agriculture). However, agricultural equipment faces challenges such as low positioning accuracy and poor algorithm adaptability due to the complex farmland environment and various operational requirements. In this research, we proposed a generalized ranging theoretical framework with multi-scenario variable-state fusion to improve the GNSS (Global Navigation Satellite System) observation exchange performance among agricultural vehicles, and accurately measure IVRs (inter-vehicular ranges). We evaluated the effectiveness of three types of GNSS observations, including PPP-SD (precise single point positioning using single difference), PPP-TCAR (precise single point positioning using double difference based on three-carrier ambiguity resolution), and PPP-LAMBDA (precise single point positioning using double difference based on least-squares ambiguity decorrelation adjustment). Moreover, we compared the accuracy of IVRs measurements. Our framework was validated through field experiments in different scenarios. It provides insights into the appropriate use of different positioning algorithms based on the application scenario, application objects, and motion states.

Innovated design,‎‏‎ simulation and ‎evaluation of potato ‎harvester ‎excavation and separation conveyors