Aimed at solving the problems of poor stability and easily falling into the local optimal solution in the grey wolf optimizer (GWO) algorithm, an improved GWO algorithm based on the differential evolution (DE) algorithm and the OTSU algorithm is proposed (DE-OTSU-GWO). The multithreshold OTSU, Tsallis entropy, and DE algorithm are combined with the GWO algorithm. The multithreshold OTSU algorithm is used to calculate the fitness of the initial population. The population is updated using the GWO algorithm and the DE algorithm through the Tsallis entropy algorithm for crossover steps. Multithreshold OTSU calculates the fitness in the initial population and makes the initial stage basically stable. Tsallis entropy calculates the fitness quickly. The DE algorithm can solve the local optimal solution of GWO. The performance of the DE-OTSU-GWO algorithm was tested using a CEC2005 benchmark function (23 test functions). Compared with existing particle swarm optimizer (PSO) and GWO algorithms, the experimental results showed that the DE-OTSU-GWO algorithm is more stable and accurate in solving functions. In addition, compared with other algorithms, a convergence behavior analysis proved the high quality of the DE-OTSU-GWO algorithm. In the results of classical agricultural image recognition problems, compared with GWO, PSO, DE-GWO, and 2D-OTSU-FA, the DE-OTSU-GWO algorithm had accuracy in straw image recognition and is applicable to practical problems. The OTSU algorithm improves the accuracy of the overall algorithm while increasing the running time. After adding the DE algorithm, the time complexity will increase, but the solution time can be shortened. Compared with GWO, DE-GWO, PSO, and 2D-OTSU-FA, the DE-OTSU-GWO algorithm has better results in segmentation assessment.

Straw return is one of the main methods for protecting black soil. Efficient and accurate straw return detection is important for the sustainability of conservation tillage. In this study, a rapid straw return detection method is proposed for large areas. An optimized Sh-DeepLabv3+ model based on the aforementioned detection method and the characteristics of straw return in Jilin Province was then used to classify plots into different straw return cover types. The model used Mobilenetv2 as the backbone network to reduce the number of model parameters, and the channel-wise feature pyramid module based on channel attention (CA-CFP) and a low-level feature fusion module (LLFF) were used to enhance the segmentation of the plot details. In addition, a composite loss function was used to solve the problem of class imbalance in the dataset. The results show that the extraction accuracy is optimal when a 2048 × 2048-pixel scale image is used as the model input. The total parameters of the improved model are 3.79 M, and the mean intersection over union (MIoU) is 96.22%, which is better than other comparative models. After conducting a calculation of the form–grade mapping relationship, the error value of the area prediction was found to be less than 8%. The results show that the proposed rapid straw return detection method based on Sh-DeepLabv3+ can provide greater support for straw return detection.

Intelligent straw coverage detection plays an important role in agricultural production and the ecological environment. Traditional pattern recognition has some problems, such as low precision and a long processing time, when segmenting complex farmland, which cannot meet the conditions of embedded equipment deployment. Based on these problems, we proposed a novel deep learning model with high accuracy, small model size and fast running speed named Residual Unet with Attention mechanism using depthwise convolution (RADw–UNet). This algorithm is based on the UNet symmetric codec model. All the feature extraction modules of the network adopt the residual structure, and the whole network only adopts 8 times the downsampling rate to reduce the redundant parameters. To better extract the semantic information of the spatial and channel dimensions, the depthwise convolutional residual block is designed to be used in feature maps with larger depths to reduce the number of parameters while improving the model accuracy. Meanwhile, the multi–level attention mechanism is introduced in the skip connection to effectively integrate the information of the low–level and high–level feature maps. The experimental results showed that the segmentation performance of RADw–UNet outperformed traditional methods and the UNet algorithm. The algorithm achieved an mIoU of 94.9%, the number of trainable parameters was only approximately 0.26 M, and the running time for a single picture was less than 0.03 s.

To meet the need of rapid identification of straw coverage types in conservation tillage fields, we investigated the use of unmanned aerial vehicle (UAV) low-altitude remote sensing images for accurate detection. UAVs were used to capture images of conservation tillage farmlands. An improved GCF-DeepLabv3+ model was utilized for detecting straw coverage types. The model incorporates StarNet as its backbone, reducing parameter count and computational complexity. Furthermore, it integrates a Multi-Kernel Convolution Feedforward Network with Fast Fourier Transform Convolutional Block Attention Module (MKC-FFN-FTCM) and a Gated Conv-Former Block (Gated-CFB) to improve the segmentation of fine plot details. Experimental results demonstrate that GCF-DeepLabv3+ outperforms other methods in segmentation accuracy, computational efficiency, and model robustness. The model achieves a parameter count of 3.19M and its FLOPs (Floating Point Operations) is 41.19G, with a mean Intersection over Union (MIoU) of 93.97%. These findings indicate that the proposed GCF-DeepLabv3+-based rapid detection method offers robust support for straw return detection.

Straw row grouping is vital in conservation tillage for precision seeding, and accurate centerline extraction of the seedling bands enhances agricultural spraying efficiency. However, the traditional single-threshold Otsu segmentation struggles with adaptability and accuracy under complex field conditions. To overcome these issues, this study proposes an adaptive multi-threshold Otsu algorithm optimized by a Simulated Annealing-Enhanced Differential Evolution–Whale Optimization Algorithm (SADE-WOA). The method avoids premature convergence and improves population diversity by embedding the crossover mechanism of Differential Evolution (DE) into the Whale Optimization Algorithm (WOA) and introducing a vector disturbance strategy. It adaptively selects thresholds based on straw-covered image features. Combined with least-squares fitting, it suppresses noise and improves centerline continuity. The experimental results show that SADE-WOA accurately separates soil regions while preserving straw texture, achieving higher between-class variance and significantly faster convergence than the other tested algorithms. It runs at just one-tenth of the time of the Grey Wolf Optimizer and one-ninth of that of DE and requires only one-sixth to one-seventh of the time needed by DE-GWO. During centerline fitting, the mean yaw angle error (MEA) ranged from 0.34° to 0.67°, remaining well within the 5° tolerance required for agricultural navigation. The root-mean-square error (RMSE) fell between 0.37° and 0.73°, while the mean relative error (MRE) stayed below 0.2%, effectively reducing the influence of noise and improving both accuracy and robustness.

Straw mulch quantity is an important indicator in the detection of straw returned to the field in conservation tillage, but there is a lack of large-scale automated measurement methods. In this study, we estimated global straw mulch quantity and completed the detection of straw returned to the field. We used an unmanned aerial vehicle (UAV) carrying a multispectral camera to acquire remote sensing images of straw in the field. First, the spectral index was selected using the Elastic-net (ENET) algorithm. Then, we used the Genetic Algorithm Hybrid Particle Swarm Optimization (GA-HPSO) algorithm, which embeds crossover and mutation operators from the Genetic Algorithm (GA) into the improved Particle Swarm Optimization (PSO) algorithm to solve the problem of machine learning model prediction performance being greatly affected by parameters. Finally, we used the Monte Carlo method to achieve a global estimation of straw mulch quantity and complete the rapid detection of field plots. The results indicate that the inversion model optimized using the GA-HPSO algorithm performed the best, with the coefficient of determination (R²) reaching 0.75 and the root mean square error (RMSE) only being 0.044. At the same time, the Monte Carlo estimation method achieved an average accuracy of 88.69% for the estimation of global straw mulch quantity, which was effective and applicable in the detection of global mulch quantity. This study provides a scientific reference for the detection of straw mulch quantity in conservation tillage and also provides a reliable model inversion estimation method for the estimation of straw mulch quantity in other crops.