Buckwheat is an important minor grain crop with medicinal and edible functions. The accurate judgment of buckwheat maturity is beneficial to reduce harvest losses and improve yield. With the rapid development of unmanned aerial vehicle (UAV) technology, it has been widely used to predict the maturity of agricultural products. This paper proposed a method using recursive feature elimination cross-validation (RFECV) combined with multiple regression models to predict the maturity of buckwheat in UAV-RGB images. The images were captured in the buckwheat experimental field of Shanxi Agricultural University in Jinzhong, Northern China, from September to October in 2021. The variety was sweet buckwheat of “Jinqiao No. 1”. In order to deeply mine the feature vectors that highly correlated with the prediction of buckwheat maturity, 22 dimensional features with 5 vegetation indexes, 9 color features, and 8 texture features of buckwheat were selected initially. The RFECV method was adopted to obtain the optimal feature vector dimensions and combinations with six regression models of decision tree regression, linear regression, random forest regression, AdaBoost regression, gradient lifting regression, and extreme random tree regression. The coefficient of determination ($R^2$) and root mean square error (RMSE) were used to analyze the different combinations of the six regression models with different feature spaces. The experimental results show that the single vegetation index performed poorly in the prediction of buckwheat maturity; the prediction result of feature space “5” combined with the gradient lifting regression model performed the best; and the $R^2$ and RMSE were 0.981 and 1.70 respectively. The research results can provide an important theoretical basis for the prediction of the regional maturity of crops.

Buckwheat is a globally recognized, nutritionally rich crop with robust adaptability, serving as a multi-purpose plant for its health benefits. Achieving precise and mechanized plot seed harvesting is a critical step in obtaining accurate results in breeding experiments. However, plot breeding requires no seed retention, no mixing, and ensures no accumulation of seed in the threshing unit. A self-cleaning technology was developed to prevent seed retention, mixing, and accumulation in the multistage tangential cylinder threshing unit. The newly designed cleaning system has five air inlets and a centrifugal fan for pneumatic cleaning. CFD simulations were conducted for each inlet position, coupled with four varying inlet velocities and the rotation speed of the main threshing cylinder. During the post-processing stage of the CFD modeling, a line consisting of fifty points was drawn beneath the threshing drums, and the air velocity at these points was recorded. The optimal configuration of inlet position, inlet air velocity, and main threshing drum rotation speed for efficient cleaning was identified based on the ratio of points beneath the drums where the airflow speed surpassed the suspension speed of buckwheat to the points where the airflow speed was lower than the suspension speed of buckwheat. The optimal configuration for "inlet_1" was identified based on the suspension velocity of buckwheat grain, with an inlet velocity of 4 m/s and a main threshing drum speed of 450 rpm.

Introduction: To achieve fast detection of pear fruits in natural pear orchards and optimize path planning for harvesting robots, this study proposes the AHG-YOLO model for multi-category detection of pear fruit occlusion in complex orchard environments.Methods: Using the Red Delicious pear as the research object, the pears are classified into three categories based on different occlusion statuses: non-occluded fruits (NO), fruits occluded by leaves/branches (OBL), and fruits in close contact with other fruits but not obstructed by leaves/branches (FCC). The YOLOv11n model is used as the base model for a lightweight design. First, the sampling method in the backbone and neck networks is replaced with ADown downsampling to capture higher-level image features, reducing floating-point operations and computational complexity. Next, shared weight parameters are introduced in the head network, and group convolution is applied to achieve a lightweight detection head. Finally, the boundary box loss function is changed to Generalized Intersection over Union (GIoU), improving the model’s convergence speed and further enhancing detection performance.Results: Experimental results show that the AHG-YOLO model achieves 93.5% (FCC), 95.3% (NO), and 93.4% (OBL) in AP, with an mAP@0.5 of 94.1% across all categories. Compared to the base YOLOv11n network, precision, recall, mAP@0.5, and mAP@0.5:0.95 are improved by 2.5%, 3.6%, 2.3%, and 2.6%, respectively. The model size is only 5.1MB, with a 16.9% reduction in the number of parameters.Discussion: The improved model demonstrates enhanced suitability for deployment on pear-harvesting embedded devices, providing technical support for the path planning of fruit-picking robotic arms.

This paper illustrates a simple yet effective spectroscopic technique for the prediction of soil organic matter (SOM) from moist soil through the synchronous 2D correlation spectroscopy (2D-COS) analysis. In the moist soil system, the strong overlap between the water absorption peaks and the SOM characteristic features in the visible-near infrared (Vis-NIR) spectral region have long been recognised as one of the main factors that causes significant errors in the prediction of the SOM content. The aim of the paper is to illustrate how the tangling effects due to the moisture and the SOM can be unveiled under 2D-COS through a sequential correlogram analysis of the two perturbation variables (i.e., the moisture and the SOM) independently. The main outcome from the 2D-COS analysis is the discovery of SOM-related bands at the 597 nm, 1646 nm and 2138 nm, together with the predominant water absorbance feature at the 1934 nm and the relatively less important ones at 1447 nm and 2210 nm. This information is then utilised to build partial least square regression (PLSR) models for the prediction of the SOM content. The experiment has shown that by discarding noisy bands adjacent to the SOM features, and the removal of the water absorption bands, the determination coefficient of prediction (R_p²) and the ratio of prediction to deviation (RPD) for the prediction of SOM from moist soil have achieved R_p² = 0.92 and the RPD = 3.19, both of which are about 5% better than that of using all bands for building the PLSR model. The very high RPD (=3.19) obtained in this study may suggest that the 2D-COS technique is effective for the analysis of complex system like the prediction of SOM from moist soil.

Introduction: Tomato leaf pests and diseases pose a significant threat to the yield and quality of Q6 tomatoes, highlighting the necessity for comprehensive studies on effective control methods.Methods: Current control measures predominantly rely on experience and manual observation, hindering the integration of multi-source data. To address this, we integrated information resources related to tomato leaf pests and diseases from agricultural standards documents, knowledge websites, and relevant literature. Guided by domain experts, we preprocessed this data to construct a sample set.Results: We utilized the Named Entity Recognition (NER) model ALBERT-BiLSTM-CRF to conduct end-to-end knowledge extraction experiments, which outperformed traditional models such as 1DCNN-CRF and BiLSTM-CRF, achieving a recall rate of 95.03%. The extracted knowledge was then stored in the Neo4j graph database, effectively visualizing the internal structure of the knowledge graph.Discussion: We developed a digital diagnostic system for tomato leaf pests and diseases based on the knowledge graph, enabling graphical management and visualization of pest and disease knowledge. The constructed knowledge graph offers insights for controlling tomato leaf pests and diseases and provides new research directions for pest control in other crops.