Leaf area index (LAI) is one of the indicators measuring the growth of rice in the field. LAI monitoring plays an important role in ensuring the stable increase of grain yield. In this study, the canopy reflectance spectrum of rice was obtained by ASD at the elongation, booting, heading and post-flowering stages of rice, and the correlations between the original reflectance (OR), first-derivative transformation (FD), reciprocal transformation (1/R), and logarithmic transformation (LOG) with LAI were analyzed. Characteristic bands of spectral data were then selected based on the successive projections algorithm (SPA) and Pearson correlation. Moreover, ridge regression (RR), partial least squares (PLS), and multivariate stepwise regression (MSR) were conducted to establish estimation models based on characteristic bands and vegetation indices. The research results showed that the correlation between canopy spectrum and LAI was significantly improved after FD transformation. Modeling using SPA to select FD characteristic bands performed better than using Pearson correlation. The optimal modeling combination was FD-SPA-VI-RR, with the coefficient of determination (R²) of 0.807 and the root-mean-square error (RMSE) of 0.794 for the training set, R² of 0.878 and RMSE of 0.773 for the validation set 1, and R² of 0.705 and RMSE of 1.026 for the validation set 2. The results indicated that the present model may predict the rice LAI accurately, meeting the requirements of large-scale statistical monitoring of rice growth indicators in the field.

This study focuses on a wheeled mobile robot used for detection, weeding and information monitoring in agriculture. However, it is difficult to reach satisfactory motion mode switching (MMS) performance. This paper aimed at exploring the optimal control parameters guaranteeing smooth MMS of four-wheel steering. Single factor tests were first conducted using a test-bench. A binary quadratic general rotation combination test was designed to obtain the optimal parameters. An entropy weight method was introduced to construct the four indexes as a comprehensive index. The optimal combination of the parameters was obtained, based on the regression equation. The results showed that the two factors and their interaction had a significant impact on the comprehensive index (p < 0.05). The best combinations of the speed of the stepper motor and locking voltage were 56 r·min⁻¹ and 3.96 V for 15° steering, 72 r·min⁻¹ and 4.35 V for 30°, and 107 r·min⁻¹ and 5.50 V for 45°, respectively. A verification test was performed using the prototype of the robot chassis. The results demonstrated that the MMS process was smooth and stable, and the proposed method was effective. This study is a beneficial exploration of the experimental method concerning wheeled robots.

Seed tape sowing technology is a kind of crop cultivation technology based on a carrier. This technology first wraps crop seeds in a kind of carrier materials and makes them into seed tape. The seed tape is then laid down in farmland with special equipment. Seed tape planting has the advantages of accurate control of hill spacing, simplification of field sowing process and helps to implement order agriculture. Seed tape manufacturing and laying equipment are the core equipment of the technology and their working reliability directly affects the advantages of seed tape planting technology. Based on the research status of seed tape planting technology and equipment, this article made comparisons between the key technologies which include the method for seed tape manufacturing, seed tape sowing, furrowing, seed tape guiding technology, etc. In this paper, the basic problems of seed tape technology that still need further study are put forward. The future development of tape sowing technology and equipment are predicted as follows: the intelligent high precision and high-speed seed tape manufacturing equipment, the large intelligent integrated seed tape planter of “land preparation-seeding tape making-sowing”, the small and medium-sized mobile walk-behind planters, the application on the agricultural landscape and the crop transplanting. This study will be helpful to promote the further development of seed tape planting technology and provide a reference for the research of tape planting technology and equipment.

Appropriate agronomic management is vital for the soil fertility and crop output of coastal salt-affected farmlands. Cattle manure incorporation and straw mulching are targeted as effective methods that can improve soil structure and stimulate crop growth, respectively. However, the combined application of manure and straw into salt-affected soils is less documented, especially with limited water supplement. In this study, a 3-year field experiment (2016–2018) was conducted in Binhai district, Tianjin, China to evaluate the effects of traditional tillage without manure and straw mulching application (TT), cattle manure incorporation (CM), straw mulching (SM), and CM combined with SM (CM + SM) on soil physiochemical properties, maize (Zea mays L.) growth, and water use efficiency. TT represented traditional cultivation in the study area without manure and straw application, as a control. All four treatments were carried out in a randomized block design with three replicates. The results demonstrate that CM treatment relieved salinity, decrease bulk density, and thereby stimulated root development. SM also has the advantage of improving salinity via 3-year implementation. Throughout the 3-year cultivation, CM + SM crop yields increased by >14.3% and grain water use index (GWUI) improved by >14.7% in comparison to TT treatment due to the improvement in soil properties. These benefits in soil properties, crop yield, and water use are important for minimizing salt constraints and realizing regional agro-ecological values.

This study addresses the challenges of tomato maturity recognition in natural environments, such as occlusion caused by branches and leaves, and the difficulty in detecting stacked fruits. To overcome these issues, we propose a novel YOLOv8n-CA method for tomato maturity recognition, which defines four maturity stages: unripe, turning color, turning ripe, and fully ripe. The model is based on the YOLOv8n architecture, incorporating the coordinate attention (CA) mechanism into the backbone network to enhance the model’s ability to capture and express features of the tomato fruits. Additionally, the C2f-FN structure was utilized in both the backbone and neck networks to strengthen the model’s capacity to extract maturity-related features. The CARAFE up-sampling operator was integrated to expand the receptive field for improved feature fusion. Finally, the SIoU loss function was used to solve the problem of insufficient CIoU of the original loss function. Experimental results showed that the YOLOv8n-CA model had a parameter count of only 2.45 × 10⁶, computational complexity of 6.9 GFLOPs, and a weight file size of just 4.90 MB. The model achieved a mean average precision (mAP) of 97.3%. Compared to the YOLOv8n model, it reduced the model size slightly while improving accuracy by 1.3 percentage points. When compared to seven other models—Faster R-CNN, YOLOv3s, YOLOv5s, YOLOv5m, YOLOv7, YOLOv8n, YOLOv10s, and YOLOv11n—the YOLOv8n-CA model was the smallest in size and demonstrated superior detection performance.

Prior to dispatch from manufacturing facilities, seeders require rigorous performance evaluations for their seeding capabilities. Conventional manual inspection methods are notably less efficient. This study introduces a wheat seeding detection approach anchored in an enhanced YOLOv5s image-processing technique. Building upon the YOLOv5s framework, we integrated four CBAM attention mechanism modules into its model. Furthermore, the traditional upsampling technique in the neck layer was superseded by the CARAFE upsampling method. The augmented model achieved an mAP of 97.14%, illustrating its ability to elevate both the recognition precision and processing speed for wheat seeds while ensuring that the model remains lightweight. Leveraging this advanced model, we can effectively count and locate seed images, enabling the precise calculation and assessment of sowing uniformity, accuracy, and dispersion. We established a sowing test bench and conducted experiments to validate our model. The results showed that after the model was improved, the average accuracy of wheat recognition was above 97.55% under different sowing rates and travel speeds. This indicates that this method has high precision for the total number of seed particles. The sowing rate and sowing travel speed were consistent with manual measurements and did not significantly affect uniformity, accuracy, or dispersion.