The implementation of image recognition technology can significantly enhance the levels of automation and intelligence in smart agriculture. However, most researchers focused on its applications in medical imaging, industry, and transportation, while fewer focused on smart agriculture. Based on this, this study aims to contribute to the comprehensive understanding of the application of image recognition technology in smart agriculture by investigating the scientific literature related to this technology in the last few years. We discussed and analyzed the applications of plant disease and pest detection, crop species identification, crop yield prediction, and quality assessment. Then, we made a brief introduction to its applications in soil testing and nutrient management, as well as in agricultural machinery operation quality assessment and agricultural product grading. At last, the challenges and the emerging trends of image recognition technology were summarized. The results indicated that the models used in image recognition technology face challenges such as limited generalization, real-time processing, and insufficient dataset diversity. Transfer learning and green Artificial Intelligence (AI) offer promising solutions to these issues by reducing the reliance on large datasets and minimizing computational resource consumption. Advanced technologies like transformers further enhance the adaptability and accuracy of image recognition in smart agriculture. This comprehensive review provides valuable information on the current state of image recognition technology in smart agriculture and prospective future opportunities.

To address the insufficient accuracy of traditional single-sensor navigation methods in dense planting environments of pomegranate orchards, this paper proposes a vision and LiDAR fusion-based navigation line extraction method for orchard environments. The proposed method integrates a YOLOv8-ResCBAM trunk detection model, a reverse ray projection fusion algorithm, and geometric constraint-based navigation line fitting techniques. The object detection model enables high-precision real-time detection of pomegranate tree trunks. A reverse ray projection algorithm is proposed to convert pixel coordinates from visual detection into three-dimensional rays and compute their intersections with LiDAR scanning planes, achieving effective association between visual and LiDAR data. Finally, geometric constraints are introduced to improve the RANSAC algorithm for navigation line fitting, combined with Kalman filtering techniques to reduce navigation line fluctuations. Field experiments demonstrate that the proposed fusion-based navigation method improves navigation accuracy over single-sensor methods and semantic-segmentation methods, reducing the average lateral error to 5.2 cm, yielding an average lateral error RMS of 6.6 cm, and achieving a navigation success rate of 95.4%. These results validate the effectiveness of the vision and 2D LiDAR fusion-based approach in complex orchard environments and provide a viable route toward autonomous navigation for orchard robots.

Electric multirotor plant protection unmanned aerial vehicles (UAVs) are widely used in China for efficient and precise plant protection at low altitude for low volumes. Unstructured farmland in China has various types of obstacles, and UAVs usually use a detour path to avoid obstacles due to flight altitude limitations. However, existing UAV spray systems do not spray when in obstacle neighborhoods during obstacle avoidance, resulting in insufficient droplet coverage and reduced plant protection quality in the area. To improve the droplet coverage in obstacle neighborhoods, this article carries out a study of side spray technology with an electric quadrotor UAV, and proposes the design and development of a side spray device. The relationship between the obstacle avoidance path of the UAV and the spray pattern of the side spray device and their effect on droplet coverage in obstacle neighborhoods was explored. An accurate measurement method of the relative position between the UAV and obstacles was proposed. Spray angle calculations and nozzle selection for the side spray device were carried out in conjunction with the relative position. A rotor wind field simulation model was designed based on the lattice Boltzmann method (LBM), and the spatial layout of the side spray device on the UAV was designed based on the simulation results. To explore suitable spray patterns for the side spray device, comparative experiments of droplet coverage in obstacle neighborhoods were carried out under different environments, spray patterns, and flight parameter combinations. The relationship between the flight parameter combinations and the distribution uniformity of droplets and the effective swath width of the side spray device was explored. The experimental results were analyzed by an analysis of variance (ANOVA) and a relationship model was obtained. The results showed that the side spray device can effectively improve droplet coverage in obstacle neighborhoods compared to a device without side spray using the same flight parameter combinations. The effective swath width in obstacle neighborhoods can be increased by a minimum of 6.35%, maximum of 35.32%, and average of 15.25% using the side spray device. The error between the predicted values of the relational model and the field experiment results was less than 15%. The results verify the effectiveness and rationality of the method proposed in this article. This study can provide technical and theoretical references for improving the plant protection quality of UAVs in obstacle environments.

During variable-rate spraying in orchards, the atomization characteristics and distribution of droplets in and out of the target area can be affected by the sprayer pressure. In this study, a variable-rate spraying control system test bench was designed, and a hollow cone nozzle QY82.317.22 was selected. The droplet atomization characteristics, including volume median diameter (D_v0.5), the relative span of the droplet spectrum, and droplet velocity at different spray pressures, were studied at distances ranging from 0.4 to 2.4 m from the nozzle orifice with an air velocity of 10 m/s at the nozzle orifice position. The effects of longitudinal distance, transverse distance, and spray pressure on D_v0.5, relative span, and droplet velocity were analysed by multiple linear regression analysis, and the regression model was established. The experimental results show that at a longitudinal distance of 1.8 m, D_v0.5 ranges from 120 to 150 μm, meeting the requirements for optimal droplet size for controlling crawling pests and plant diseases on crop leaves; and the relative span is 1.2, indicating a wide droplet spectrum. At different pressure conditions, D_v0.5 decreases as pressure increases. Through multiple linear regression analysis, the longitudinal distance, the transverse distance, and the spray pressure have high significance for D_v0.5 and the droplet velocity. The longitudinal distance and the transverse distance have a highly significant effect on the relative span. In this study, the mathematical relational model of droplet characteristics at different spatial positions and different pressures was established, providing an agricultural reference for predicting the droplet characteristics at different spatial positions to achieve the best application effect. This model is conducive to the effective use of pesticides and reduces environmental pollution.

Silver nanoparticles were biosynthesized from Conyzacanadensis leaf extract with the help of a microwave oven. The UV-vis spectrum showed the maximum absorption at 441 nm, corresponding to the surface plasmon resonance of silver nanoparticles. Transmission electron microscope and scanning electron microscope images showed that the synthesized silver nanoparticles were spherical or near-spherical with an average diameter of 43.9 nm. X-ray diffraction demonstrated nanoparticles with a single-phase cubic structure. As-synthesized silver nanoparticles displayed prominent antifungal activity against Bipolaris maydis. The colony inhibition rate reached 88.6% when the concentration of nanosilver colloid was 100 μL·mL⁻¹ (v/v). At such a concentration, no colony formation was observed on the solid plate. The diameter of the inhibition zone was 13.20 ± 1.12 mm. These results lay the foundation for the comprehensive control of plant pathogens using an environmentally friendly approach.

Aiming to address the problems of low working efficiency and high damage rate of high and low roller peeling equipment in the process of fresh corn harvesting in China, this paper theoretically analyzes the mechanical motion process between the peeling device and the corn ear, and a high–low roll peeling structure is proposed. This structure incorporates elastomeric rubber material, a roller segmentation design, and an adjustable spiral frame, and the selection of relevant parameters is given. To determine the optimal operating parameters for the fresh-corn-peeling device, a three-factor, three-level orthogonal test was conducted using the Box–Behnken central grouping method in Design-Expert 12 software. The test factors were peeling roller speed, peeling roller tilt angle, and vibrating plate frequency. The evaluation indices considered were the bract peeling rate (BPR) and the grain breaking rate (GBR). Based on the theoretical analysis results, a test bench for the fresh-corn-ear-peeling device was established and the parameter combination for optimal peeling quality was determined according to the actual work situation. The results show that the impact on the BPR and GBR, from large to small, is in the following order: peeling roller speed, peeling roller tilt angle, and frequency vibration plate. The optimization module was used to optimize the operating parameters and used the following integers to obtain the optimal operating parameter combination: the peeling roller speed was 480 r·min⁻¹; the peeling roller tilt angle was 8°; the vibrating plate frequency was 260 times·min⁻¹; the corresponding BPR was 91.75%, which was 0.66% points lower than the optimal value; and the GBR was 1.55%, which was 0.08% points higher than the optimal value. Notably, this fresh-corn-peeling equipment exhibited superior performance in terms of peeling fracture results compared with standard peeling equipment. Therefore, this study provides valuable technical support for the optimal design and selection of fresh-corn-peeling equipment.