Rice blast is a serious threat to rice yield. Breeding disease-resistant varieties is one of the most economical and effective ways to prevent damage from rice blast. The traditional identification of resistant rice seeds has some shortcoming, such as long possession time, high cost and complex operation. The purpose of this study was to develop an optimal prediction model for determining resistant rice seeds using Ranman spectroscopy. First, the support vector machine (SVM), BP neural network (BP) and probabilistic neural network (PNN) models were initially established on the original spectral data. Second, due to the recognition accuracy of the Raw-SVM model, the running time was fast. The support vector machine model was selected for optimization, and four improved support vector machine models (ABC-SVM (artificial bee colony algorithm, ABC), IABC-SVM (improving the artificial bee colony algorithm, IABC), GSA-SVM (gravity search algorithm, GSA) and GWO-SVM (gray wolf algorithm, GWO)) were used to identify resistant rice seeds. The difference in modeling accuracy and running time between the improved support vector machine model established in feature wavelengths and full wavelengths (200–3202 cm⁻¹) was compared. Finally, five spectral preproccessing algorithms, Savitzky–Golay 1-Der (SGD), Savitzky–Golay Smoothing (SGS), baseline (Base), multivariate scatter correction (MSC) and standard normal variable (SNV), were used to preprocess the original spectra. The random forest algorithm (RF) was used to extract the characteristic wavelengths. After different spectral preproccessing algorithms and the RF feature extraction, the improved support vector machine models were established. The results show that the recognition accuracy of the optimal IABC-SVM model based on the original data was 71%. Among the five spectral preproccessing algorithms, the SNV algorithm’s accuracy was the best. The accuracy of the test set in the IABC-SVM model was 100%, and the running time was 13 s. After SNV algorithms and the RF feature extraction, the classification accuracy of the IABC-SVM model did not decrease, and the running time was shortened to 9 s. This demonstrates the feasibility and effectiveness of IABC in SVM parameter optimization, with higher prediction accuracy and better stability. Therefore, the improved support vector machine model based on Ranman spectroscopy can be applied to the fast and non-destructive identification of resistant rice seeds.

Changes in soybean flower and pod numbers are important factors affecting soybean yields. Obtaining the number of flowers and pods, as well as fallen flowers and pods, quickly and accurately is crucial for soybean variety breeding and high-quality and high-yielding production. This is especially challenging in the natural field environment. Therefore, this study proposed a field soybean flower- and pod-detection method based on an improved network model (YOLOv8-VEW). VanillaNet is used as the backbone feature-extraction network for YOLOv8, and the EMA attention mechanism module is added to C2f, replacing the CioU function with the WIoU position loss function. The results showed that the F1, mAP, and FPS (frames per second) of the YOLOv8-VEW model were 0.95, 96.9%, and 90 FPS, respectively, which were 0.05, 2.4%, and 24 FPS better than those of the YOLOv8 model. The model was used to compare soybean flower and pod counts with manual counts, and its R² for flowers and pods was 0.98311 and 0.98926, respectively, achieving rapid detection of soybean flower pods in the field. This study can provide reliable technical support for detecting soybean flowers and pod numbers in the field and selecting high-yielding varieties.

During hybrid soybean seed production, the parents’ phenotypic consistency is assessed by breeders to ensure the purity of soybean seeds. Detection traits encompass the hypocotyl, leaf, pubescence, and flower. To achieve the detection of hybrid soybean parents’ phenotypic consistency in the field, a self-propelled image acquisition platform was used to obtain soybean plant image datasets. In this study, the Large Selective Kernel Network (LSKNet) attention mechanism module, the detection layer Small Network (SNet), dedicated to detecting small objects, and the Wise Intersection over Union v3 (WIoU v3) loss function were added into the YOLOv5s network to establish the hybrid soybean parent phenotypic consistency detection model SLW-YOLO. The SLW-YOLO achieved the following: F1 score: 92.3%; mAP: 94.8%; detection speed: 88.3 FPS; and model size: 45.1 MB. Compared to the YOLOv5s model, the SLW-YOLO model exhibited an improvement in F1 score by 6.1% and in mAP by 5.4%. There was a decrease in detection speed by 42.1 FPS, and an increase in model size by 31.4 MB. The parent phenotypic consistency detected by the SLW-YOLO model was 98.9%, consistent with manual evaluation. Therefore, this study demonstrates the potential of using deep learning technology to identify phenotypic consistency in the seed production of large-scale hybrid soybean varieties.

The extraction of navigation lines is a crucial aspect in the field autopilot system for intelligent agricultural equipment. Given that soybean seedlings are small, and straw can be found in certain Northeast China soybean fields, accurately obtaining feature points and extracting navigation lines during the soybean seedling stage poses numerous challenges. To solve the above problems, this paper proposes a method of extracting navigation lines based on the average coordinate feature points of pixel points in the bean seedling belt according to the calculation of the average coordinate. In this study, the soybean seedling was chosen as the research subject, and the Hue, Saturation, Value (HSV) colour model was employed in conjunction with the maximum interclass variance (OTSU) method for RGB image segmentation. To extract soybean seedling bands, a novel approach of framing binarised image contours by drawing external rectangles and calculating average coordinates of white pixel points as feature points was proposed. The feature points were normalised, and then the improved adaptive DBSCAN clustering method was used to cluster the feature points. The least squares method was used to fit the centre line of the crops and the navigation line, and the results showed that the average distance deviation and the average angle deviation of the proposed algorithm were 7.38 and 0.32. The fitted navigation line achieved an accuracy of 96.77%, meeting the requirements for extracting navigation lines in intelligent agricultural machinery equipment for soybean inter-row cultivation. This provides a theoretical foundation for realising automatic driving of intelligent agricultural machinery in the field.

The identification of soybean growth periods is the key to timely take field management measures, which plays an important role in improving yield. In order to realize the discrimination of soybean growth periods under complex environments in the field quickly and accurately, a model for identifying soybean growth periods based on multi-source sensors and improved convolutional neural network was proposed. The AlexNet structure was improved by adjusting the number of fully connected layer 1 and fully connected layer 2 neurons to 1024 and 256. The model was optimized through the hyperparameters combination experiment and the classification experiment of different types of image datasets. The discrimination of soybean emergence (VE), cotyledon (VC), and first node (V1) stages was achieved. The experimental results showed that after improving the fully connected layers, the average classification accuracy of the model was 99.58%, the average loss was 0.0132, and the running time was 0.41 s/step under the optimal combination of hyperparameters. At around 20 iterations, the performances began to converge and were all superior to the baseline model. Field validation trials were conducted applying the model, and the classification accuracy was 90.81% in VE, 91.82% in VC, and 92.56% in V1, with an average classification accuracy of 91.73%, and single image recognition time was about 21.9 ms. It can meet the demand for the identification of soybean growth periods based on smart phone and unmanned aerial vehicle (UAV) remote sensing, and provide technical support for the identification of soybean growth periods with different resolutions from different sensors.

To address the issue of improving the accuracy and efficiency of variable-rate application under large-scale field conditions, an RBFNN-SMC variable-rate application control system was designed and experimentally verified. A first-order inertial pure lag model of the spray flow rate was identified through step tests. Based on this, three controllers, namely traditional SMC, fuzzy SMC, and RBFNN-SMC, were designed for comparison. The simulation results indicated that RBFNN-SMC had the shortest adjustment time and the smallest steady-state error under conditions of model uncertainty and time delay. Its maximum overshoot was reduced by 11.2% compared to traditional SMC, and the steady-state error was controlled within ±0.51%. The control strategy was implemented on a microcontroller and integrated with a prescription-based spray system. Field trials showed that the average absolute error of the proposed system in tracking the target flow rate was 3.2%, and it achieved a weed control effect comparable to that of traditional uniform spraying. These results suggest that the RBFNN-SMC-based variable spray rate system can enhance flow rate control performance and support more precise and potentially more sustainable herbicide application, providing a reference and ideas for the research on variable-rate application flow rate control based on prescription maps.