The detection, quantification, diagnosis, and identification of plant diseases is particularly crucial for precision agriculture. Recently, traditional visual assessment technology has not been able to meet the needs of precision agricultural informatization development, and hyperspectral technology, as a typical type of non-invasive technology, has received increasing attention. On the basis of simply describing the types of pathogens and host–pathogen interaction processes, this review expounds the great advantages of hyperspectral technologies in plant disease detection. Then, in the process of describing the hyperspectral disease analysis steps, the articles, algorithms, and methods from disease detection to qualitative and quantitative evaluation are mainly summarizing. Additionally, according to the discussion of the current major problems in plant disease detection with hyperspectral technologies, we propose that different pathogens’ identification, biotic and abiotic stresses discrimination, plant disease early warning, and satellite-based hyperspectral technology are the primary challenges and pave the way for a targeted response.

Plant protection unmanned aerial vehicles (UAVs) play a crucial role in agricultural aviation services. In recent years, plant protection UAVs, which improve the accuracy and eco-friendliness of agricultural techniques, have been used to overcome the shortcomings of traditional agricultural operations. First, this paper introduces the development scale, main types, and operation scenarios of China’s plant protection UAVs. Subsequently, the key technologies of plant protection UAVs, such as precision autonomous flight control, pesticide spraying, drift control, and spraying quality measurement technologies, are reviewed. Next, the emergent technologies of plant protection UAVs are studied and analyzed with a focus on better spray effects, calculation models of droplet drift, controllable droplet size atomization technology, droplet drift detection technology, and droplet deposition quality detection technology in the application of plant protection UAVs. Moreover, the technologies of plant protection UAV application are summarized and future research prospects are presented, offering ideas for follow-up research on the key technologies of plant protection UAVs and encouraging agricultural production management to move toward better efficiency, eco-friendliness, and accuracy.

Drought stress is a major environmental constraint for plant growth. Climate-change-driven increases in ambient temperatures resulted in reduced or unevenly distributed rainfalls, leading to increased soil drought. Carex duriuscula C. A. Mey is a typical drought-tolerant sedge, but few reports have examined the mechanisms conferring its tolerant traits. In the present study, the drought responses of C. duriuscula were assessed by quantifying activity of antioxidant enzymes in its leaf and root tissues and evaluating the relative contribution of organic and inorganic osmolyte in plant osmotic adjustment, linking it with the patterns of the ion acquisition by roots. Two levels of stress—mild (MD) and severe (SD) drought treatments—were used, followed by re-watering. Drought stress caused reduction in a relative water content and chlorophyll content of leaves; this was accompanied by an increase in the hydrogen peroxide (H₂O₂) and superoxide (O_2⁻) contents in leaves and roots. Under MD stress, the activities of catalase (CAT), peroxidase (POD), and glutathione peroxidase (GPX) increased in leaves, whereas, in roots, only CAT and POD activities increased. SD stress led to an increase in the activities of CAT, POD, superoxide dismutase (SOD), and GPX in both tissues. The levels of proline, soluble sugars, and soluble proteins in the leaves also increased. Under both MD and SD stress conditions, C. duriuscula increased K⁺, Na⁺, and Cl⁻ uptake by plant roots, which resulted in an increased K⁺, Na⁺, and Cl⁻ concentrations in leaves and roots. This reliance on inorganic osmolytes enables a cost-efficient osmotic adjustment in C. duriuscula. Overall, this study revealed that C. duriuscula was able to survive arid environments due to an efficient operation of its ROS-scavenging systems and osmotic adjustment mechanisms.

Pine wilt disease (PWD) is a serious threat to pine forests. Combining unmanned aerial vehicle (UAV) images and deep learning (DL) techniques to identify infected pines is the most efficient method to determine the potential spread of PWD over a large area. In particular, image segmentation using DL obtains the detailed shape and size of infected pines to assess the disease’s degree of damage. However, the performance of such segmentation models has not been thoroughly studied. We used a fixed-wing UAV to collect images from a pine forest in Laoshan, Qingdao, China, and conducted a ground survey to collect samples of infected pines and construct prior knowledge to interpret the images. Then, training and test sets were annotated on selected images, and we obtained 2352 samples of infected pines annotated over different backgrounds. Finally, high-performance DL models (e.g., fully convolutional networks for semantic segmentation, DeepLabv3+, and PSPNet) were trained and evaluated. The results demonstrated that focal loss provided a higher accuracy and a finer boundary than Dice loss, with the average intersection over union (IoU) for all models increasing from 0.656 to 0.701. From the evaluated models, DeepLLabv3+ achieved the highest IoU and an F1 score of 0.720 and 0.832, respectively. Also, an atrous spatial pyramid pooling module encoded multiscale context information, and the encoder–decoder architecture recovered location/spatial information, being the best architecture for segmenting trees infected by the PWD. Furthermore, segmentation accuracy did not improve as the depth of the backbone network increased, and neither ResNet34 nor ResNet50 was the appropriate backbone for most segmentation models.

LNC (leaf nitrogen content) in crops is significant for diagnosing the crop growth status and guiding fertilization decisions. Currently, UAV (unmanned aerial vehicles) remote sensing has played an important role in estimating the nitrogen nutrition of crops at the field scale. However, many existing methods of evaluating crop nitrogen based on UAV imaging techniques usually have used a single type of imagery such as RGB or multispectral images, seldom considering the usage of information fusion from different types of UAV imagery for assessing the crop nitrogen status. In this study, GS (Gram–Schmidt Pan Sharpening) was utilized to fuse images from two sensors of digital RGB and multispectral cameras mounted on UAV, and the specific bands of the multispectral cameras are blue, green, red, rededge and NIR. The color space transformation method, HSV (Hue-Saturation-Value), was used to separate soil background noise from crops due to the high spatial resolution of UAV images. Two methods of optimizing feature variables, the Successive Projection Algorithm (SPA) and the Competitive Adaptive Reweighted Sampling method (CARS), combined with two regularization regression algorithms, LASSO and RIDGE, were adopted to estimate the LNC, compared to the commonly used Random Forest algorithm. The results showed that: (1) the accuracy of LNC estimation using the fusion image is improved distinctly by a comparison to the original multispectral image; (2) the denoised images performed better than the original multispectral images in evaluating LNC in rice; (3) the RIDGE-SPA combined method, using SPA to select the MCARI, SAVI and OSAVI, had the best performance for LNC in rice, with an R² of 0.76 and an RMSE of 10.33%. It can be demonstrated that the information fusion of multiple-sensor imagery from UAV coupling with the methods of optimizing feature variables can estimate the rice LNC more effectively, which can also provide a reference for guiding the decision making of fertilization in rice fields.