Estimation of rice plant height distribution plays a significant role in keeping the feed rate of rice combine harvesters stable. This is an effective way to guarantee the working stability of the whole machine, as a consequence, improving threshing and cleaning efficiency and reducing loss and damage rates. However, dense growth and leafy and bent branches of mature rice make it difficult to detect the lowest point of aggregated growing plants in three dimensional (3D) point cloud data. Therefore, the objective of this study was to put forward a method to estimate plant height distribution on the basis of a moving surface and 3D point cloud elevation. The statistical outlier removal (SOR) algorithm was used to reduce noise points far away from target point cloud body, and then moving surface fitting elevation was applied to achieve accurate classification of ground and crop point cloud data for plant height estimation. Experiments showed that, compared with the actual value, the average square root error (RMSE) of the estimation results was 8.29, the average absolute percentage error (MAPE) was 9.28%, and the average accuracy was 90%. The proposed method could accurately estimate the height of mature rice and is beneficial to calculating the feed rate in advance, which can provide a reference for further investigation in automatic and intelligent harvesting.

In this paper, UAV (unmanned aerial vehicle, DJI Phantom4RTK) and YOLOv4 (You Only Look Once) target detection deep neural network methods were employed to collected mature rice images and detect rice ears to produce a rice density prescription map. The YOLOv4 model was used for rice ear quick detection of rice images captured by a UAV. The Kriging interpolation algorithm was used in ArcGIS to make rice density prescription maps. Mature rice images collected by a UAV were marked manually and used to build the training and testing datasets. The resolution of the images was 300 × 300 pixels. The batch size was 2, and the initial learning rate was 0.01, and the mean average precision (mAP) of the best trained model was 98.84%. Exceptionally, the network ability to detect rice in different health states was also studied with a mAP of 95.42% in the no infection rice images set, 98.84% in the mild infection rice images set, 94.35% in the moderate infection rice images set, and 93.36% in the severe infection rice images set. According to the severity of rice sheath blight, which can cause rice leaves to wither and turn yellow, the blighted grain percentage increased and the thousand-grain weight decreased, the rice images were divided into these four infection levels. The ability of the network model (R² = 0.844) was compared with traditional image processing segmentation methods (R² = 0.396) based on color and morphology features and machine learning image segmentation method (Support Vector Machine, SVM R² = 0.0817, and K-means R² = 0.1949) for rice ear counting. The results highlight that the CNN has excellent robustness, and can generate a wide range of rice density prescription maps.

For the inconsistent lodging of wheat with dense growth and overlapped organs, it is difficult to detect lodging direction accurately and quickly using vehicle vision for harvesters. Therefore, in this paper, the k-means algorithm is improved by designing a validity evaluation function, selecting initial clustering centers by distance, constructing a multidimensional feature vector, and simplifying calculations using triangle inequality. An adaptive image grid division method based on perspective mapping and inverse perspective mapping with a corrected basic equation is proposed for constructing a dataset of wheat lodging directions. The improved k-means algorithm and direction dataset are used to construct a bag of visual words. Based on scale-invariant feature transform, pyramid word frequency, histogram intersection kernel, and support vector machine, the wheat lodging directions were detected in the grid. The proposed method was verified through experiments with images acquired on an intelligent combine harvester. Compared with single-level word frequencies with existing and improved k-means, the mean accuracy of wheat lodging direction detection by pyramid word frequencies with improved k-means increased by 6.71% and 1.11%, respectively. The average time of detection using the proposed method was 1.16 s. The proposed method can accurately and rapidly detect wheat lodging direction for combine harvesters and further realize closed-loop control of intelligent harvesting operations.

In complex field environments, wheat grows densely with overlapping organs and different plant weights. It is difficult to accurately predict feed quantity for wheat combine harvester using the existing YOLOv5s and uniform weight of a single wheat plant in a whole field. This paper proposes a feed quantity prediction method based on the improved YOLOv5s and weight of a single wheat plant without stubble. The improved YOLOv5s optimizes Backbone with compact bases to enhance wheat spike detection and reduce computational redundancy. The Neck incorporates a hierarchical residual module to enhance YOLOv5s’ representation of multi-scale features. The Head enhances the detection accuracy of small, dense wheat spikes in a large field of view. In addition, the height of a single wheat plant without stubble is estimated by the depth distribution of the wheat spike region and stubble height. The relationship model between the height and weight of a single wheat plant without stubble is fitted by experiments. Then, feed quantity can be predicted using the weight of a single wheat plant without stubble estimated by the relationship model and the number of wheat plants detected by the improved YOLOv5s. The proposed method was verified through experiments with the 4LZ-6A combine harvester. Compared with the existing YOLOv5s, YOLOv7, SSD, Faster R-CNN, and other enhancements in this paper, the mAP₅₀ of wheat spikes detection by the improved YOLOv5s increased by over 6.8%. It achieved an average relative error of 4.19% with a prediction time of 1.34 s. The proposed method can accurately and rapidly predict feed quantity for wheat combine harvesters and further realize closed-loop control of intelligent harvesting operations.

In addressing the challenges of low path tracking accuracy and poor robustness during tractor autonomous operation, this paper proposes a path tracking control method for tractors that integrates prescribed performance with sliding mode control (SMC). A key feature of this control method is its inherent immunity to system parameter perturbations and external disturbances, while ensuring path tracking errors are constrained within a predefined range. First, the tractor is simplified into a two-wheeled vehicle model, and a path tracking error model is established based on the reference operation trajectory. By defining a prescribed performance function, the constrained tracking control problem is transformed into an unconstrained stability control problem, guaranteeing the boundedness of tracking errors. Then, by incorporating SMC theory, a prescribed performance sliding mode path tracking controller is designed to achieve robust path tracking and error constraint for the tractor. Finally, both simulation and field experiments are conducted to validate the method. The results demonstrate that compared with the traditional SMC method, the proposed method effectively mitigates the impact of complex farmland conditions, reducing path tracking errors while enforcing strict error constraints. Field experiment data shows the proposed method achieves an average absolute error of 0.02435 m and a standard deviation of 0.02795 m, confirming its effectiveness and superiority. This research lays a foundation for the intelligent development of agricultural machinery.

It is difficult to extract small and dense objects with random state, such as grain and impurity, in image of vehicle-mounted dynamic rice grain flow on combine harvester. Therefore, this paper improves Deeplabv3+ by constructing MobileNetv2 in coding layer and adding ECA(Efficient Channel Attention) to Encoder and Decoder to improve extraction accuracy of high-dimensional features in images with a large number of objects with random state. In addition, the YOLOv4 is improved by using Mixup in preprocessing, constructing Mish in Neck and Head, adding ECA to Neck and Prediction of BackBone to improve training precision of small and dense objects and reducing effect of gradient disappearance. And the impurity/breakage rates are assessed based on relationship model between pixel area and quality, improved Deeplabv3+ and YOLOv4. The proposed method was verified by experiments with images acquired on intelligent combine harvester. Compared with existing Deeplabv3+, YOLOv4, U-NET, BP, the extraction accuracy by improved method increased by more than 4.01%. The average relative error and time of impurity/breakage assessment by proposed method were 7.69% and 1.56s. The proposed method can accurately and rapidly assess impurity/breakage rates for dynamic rice grain flow on combine harvester, and further realize closed-loop control of intelligent harvesting operation.