Laser, as an emerging artificial light source, is one of the greatest inventions of the 20th century, featuring high power density, excellent directionality, and superb monochromaticity. It has been widely applied in the agricultural sector. This article provides an in-depth analysis of the research progress and development trends of laser technology in agriculture. In plant production, it discusses applications in plant mutation breeding, promoting plant growth and development, enhancing yield and quality, plant protection, plant detection, and phenotyping. In animal management, the focus is on the use of laser technology in animal genetic breeding, growth, medical care, and product testing. For fungal research, it summarizes the use of laser technology in fungal breeding, growth, detection, and identification. The article also addresses the challenges and difficulties faced when integrating laser technology with agricultural production, proposing directions for future development, such as improving precision in plant production, optimizing animal management, and advancing scientific fungal production. Although laser technology has already achieved significant results in agriculture, future research should explore more innovative applications and integrate with artificial intelligence and big data technologies to further advance the deep integration of laser technology with the agricultural industry, leading to more efficient and intelligent agricultural production management and offering new opportunities and breakthroughs for modern agriculture.

Nitrogen is one of the important elements for the growth and development of rice, and accurate estimation of nitrogen concentration is crucial for guiding precise fertilization and assisting in the selection of nitrogen efficient varieties in rice. Traditional field sampling methods make it difficult to obtain real-time nitrogen concentration in rice. With the rapid development of information technology, establishing the relationship between unmanned aerial vehicle hyperspectral data and nitrogen concentration through machine learning methods is currently one of the main technical routes for crop nitrogen nutrition diagnosis. This study constructs an inversion model by using the feature bands of unmanned aerial vehicle canopy hyperspectral data selected by the continuous projection algorithm as input and the measured nitrogen concentration data as output. Extreme learning machine (ELM) has the advantages of fast speed and strong generalization ability compared to similar machine learning methods. However, due to its randomly generated connection weights and neuron thresholds, its training stability is insufficient and it is prone to falling into local optima. The beluga whale optimization (BWO) is a competitive algorithm inspired the behavior of beluga whales to solve single modal and multimodal optimization problems. In this study, the BWO-ELM rice nitrogen concentration unmanned aerial vehicle hyperspectral inversion model was constructed to achieve rapid estimation of rice nitrogen concentration by optimizing the connection weight between the input layer and the hidden layer of the ELM, as well as the initial weight of the hidden layer through the BWO. The research results show that the continuous projection algorithm filters out 10 feature bands, which are 673, 703, 727, 823, 850, 877, 895, 952, 961, and 985 nm, respectively. The training set R² and RMSE of the nitrogen concentration inversion model constructed based on BWO-ELM are 0.742 5 and 0.382 6%, respectively, while the testing set R² and RMSE are 0.702 8 and 0.487 7%, respectively. The predictive ability is superior to that of the nitrogen concentration inversion model constructed based on ELM. In summary, the rice nitrogen concentration unmanned aerial vehicle hyperspectral inversion model based on BWO-ELM can quickly and accurately obtain rice nitrogen concentration, providing a new method for rice nutrition monitoring.

Rice seedlings need sufficient light for normal growth. The increasing temperature demand of early spring seedling has promoted the development of greenhouses, greenhouses and factory facilities. However, due to the reflection and absorption of sunlight by glass and plastic, the lack of light in facility seedlings is prominent. Supplementary lighting can better solve the problem of lack of light in facility seedling, LED as the representative of the supplementary light source has been promoted and applied in facility seedling, but it still has high energy consumption problem. Laser is the only artificial light source with parallel light characteristics of sunlight, with good correlation, good monochromism, good directivity, high brightness, large energy, photoelectric conversion efficiency, energy saving and so on. Studies have shown that the special light quality of laser can effectively improve the photosynthesis efficiency of seedlings. The traditional laser light source represented by He-Ne laser is large in volume, high in cost, and difficult to be popularized. Using semiconductor laser combined with uniform light technology to develop a new laser light source, small size, low cost, can achieve a large area of uniform laser irradiation of rice seedlings, the single lamp irradiation area can reach 60-70 square meters. At the same time, the new laser light source retains the traditional laser correlation, monochromism and directivity, and also has the characteristics of high efficiency and energy saving, and the energy consumption can be only 1/30 of the traditional LED light source. The results showed that reasonable irradiation of new laser light source in the process of rice seedling cultivation can improve the quality of seedlings, fast greening after transplanting, more tillers, early heading, and finally achieve stable and increased rice yield. Since 2021, the test results in many places across the country have shown that the use of a new laser light source to irradiate rice seedlings for about 20 days before transplanting can achieve a yield increase of more than 10%. This result not only provided important technical support and guarantee for improving the quality and yield of rice in China, but also provided a new method and means for other plants to fill light at various stages.

The realization of automatic identification and positioning of tea buds is the basis for the development of high-quality tea intelligent picking equipment. Aiming at the problems of tiny tea buds and the picking environment which is greatly affected by light, this research proposes a tea bud recognition method based on a deep learning network model to carry out the light source design of the recognition system, which can provide technical support for the realization of all-weather and high-efficiency intelligent tea bud picking equipment. First, an aluminum alloy frame was constructed to provide a closed and shaded dark environment; then, three combinations of heights and three combinations of light intensities were created by adjusting the height of the crossbar and the brightness of the light source; finally, the image datasets of tea buds in different combinations were collected, and recognition tests were carried out on one bud and one leaf and one bud and two leaves by using the improved YOLOv5 model. The experimental results show that the overall accuracy of YOLOv5s is 77.13%, and the overall average precision mean is 86.14%, the overall accuracy of the improved recognition model YOLOv5s-SPD is 80.30%, and the overall average precision mean is 87.3%, and the average detection time of a single image is 5.7 ms, which meets the requirement of real-time detection, and is better than the original YOLOv5s with an overall accuracy improvement of 3.17% and an overall average precision mean of 1.16%, which effectively improves the recognition performance of tea buds. Under the condition of height 90 cm and luminance L7(0.164~0.328 μmol/m²), the detection accuracy, recall and AP average of one bud and two leaves were 86.70%, 92.45% and 95.00%, respectively. The method proposed in this paper can effectively and quickly detect tea buds, and the light source design scheme supports the development of all-weather high-quality tea intelligent picking equipment.

In recent years, with the continuous progress of semiconductor laser technology, laser technology applied in the field of agriculture has attracted much attention. The laser light source has the advantages of strong monochromaticity, high electro-optical conversion efficiency and low use cost, which provides new technical support for improving crop quality and increasing yield. In order to study the effects of different light quality laser on the growth, physiological and biochemical indexes, polysaccharide and saponin accumulation of ginseng, biennial ginseng carrier seeds were selected for experiments. From the leaf opening stage to the leaf withering stage: from 7：00 to 11：00, and from 14：00 to 18：00. every day, full-wavelength LED lights, red laser light (R3), blue laser light (B3), hybrid laser light R2B1, R3B2 (optical quantum density of 4.48 μmol/(m²·s), 4.61 μmol/(m²·s)) are used to fill light, respectively. According to the influence of different light quality, this study set up no light as a blank control group. It was found that the net photosynthetic capacity and stomatal conductance of leaves of ginseng were improved. Among them, the photosynthesis capacity of the LED light source was more obvious, and the absorption capacity of the blue light source was also stronger. In particular, the net photosynthetic rate of the LED treatment group was the most prominent, while the blue light treatment group achieved the most significant improvement in transpiration rate and intercellular CO₂ concentration. The stomatal conductance of R3B2 treatment group was the highest. In terms of growth, ginseng performed best under mixed red and blue light treatment. Compared with the control group, blue light could significantly increase plant height and leaf length and width. The root weight and leaf weight of LED full wavelength complement light group were higher than other treatments. In terms of the accumulation of nutrients, the polysaccharide content in blue light treatment was the highest, and the saponin content in red light treatment was generally higher than that in blue light treatment group, indicating that red light promoted the accumulation of saponin content. The content of Rg1 and Rb1 saponin in R2B1 treatment group was the highest, which was much higher than that in other treatments; the content of Re saponin in R3B2 treatment was similar to that in red light treatment group, and higher than that in the other four treatments. In future studies, we will select the most suitable conditions for the growth and nutrient accumulation of ginseng, so as to provide a feasible method for shortening the growth cycle, improving the quality and efficiency of ginseng.

As agricultural production continues to increase its requirements for field management, traditional agricultural machinery and equipment have gradually become difficult to meet the production needs of modern smart agriculture. In this context, soil moisture monitoring technology, as a key means of obtaining soil moisture information in modern agricultural management, is playing an important role in promoting the development of agricultural machinery towards intelligence and automation. Therefore, this study thoroughly reviews and analyzes the current research status of soil moisture monitoring technology at home and abroad, focusing on the research progress of three aspects of soil moisture monitoring methods and principles, model construction algorithms, and signal processing methods. Through comparative analysis, the differences and similarities in monitoring methods, principles, model construction algorithms, and signal processing methods at home and abroad, as well as the problems and challenges in practical applications, are summarized. The future development trends of soil moisture monitoring technology in these three aspects are proposed: in terms of soil moisture monitoring methods, a multi-source soil moisture monitoring information platform is constructed to achieve more comprehensive data collection and analysis; In terms of model construction algorithms, machine learning and deep learning algorithms are adopted to customize model algorithm modules for different soil environments and working scenarios, improving the accuracy and applicability of monitoring equipment; In terms of signal processing, the application of multi-source signal fusion technology is strengthened to reduce the impact of the working environment on monitoring equipment.

The cold chain in the production area of fruits and vegetables is the primary link to reduce product loss and improve product quality, but it is also a weak link. With the application of big data technology in cold chain logistics, intelligent devices, and technologies have become important carriers for improving the efficiency of cold chain logistics in fruit and vegetable production areas, extending the shelf life of fruits and vegetables, and reducing fruit and vegetable losses. They have many advantages in fruit and vegetable pre-cooling, sorting and packaging, testing, warehousing, transportation, and other aspects. This article summarizes the rapidly developing and widely used intelligent technologies at home and abroad in recent years, including automated guided vehicle intelligent handling based on electromagnetic or optical technology, intelligent sorting based on sensors, electronic optics, and other technologies, intelligent detection based on computer vision technology, intelligent transportation based on perspective imaging technology, etc. It analyses and studies the innovative research and achievements of various scholars in applying intelligent technology in fruit and vegetable cold chain storage, sorting, detection, transportation, and other links, and improves the efficiency of fruit and vegetable cold chain logistics. However, applying intelligent technology in fruit and vegetable cold chain logistics also faces many problems. The challenges of high cost, difficulty in technological integration, and talent shortages have limited the development of intelligent technology in the field of fruit and vegetable cold chains. To solve the current problems, it is proposed that costs be controlled through independent research and development, technological innovation, and other means to lower the entry threshold for small enterprises. Strengthen integrating intelligent technology and cold chain logistics systems to improve data security and system compatibility. At the same time, the government should introduce relevant policies, provide necessary financial support, and establish talent training mechanisms. Accelerate the development and improvement of intelligent technology standards in the field of cold chain logistics. Through technological innovation, cost control, talent cultivation, and policy guidance, we aim to promote the upgrading of the agricultural industry and provide ideas for improving the quality and efficiency of fruit and vegetable cold chain logistics.