Estimation of crop biophysical and biochemical characteristics is the key element for crop growth monitoring with remote sensing. With the application of unmanned aerial vehicles (UAV) as a remote sensing platform worldwide, it has become important to develop general estimation models, which can interpret remote sensing data of crops by different sensors and in different agroclimatic regions into comprehensible agronomy parameters. Leaf chlorophyll content (LCC), which can be measured as a soil plant analysis development (SPAD) value using a SPAD-502 Chlorophyll Meter, is one of the important parameters that are closely related to plant production. This study compared the estimation of rice (Oryza sativa L.) LCC in two different regions (Ningxia and Shanghai) using UAV-based spectral images. For Ningxia, images of rice plots with different nitrogen and biochar application rates were acquired by a 125-band hyperspectral camera from 2016 to 2017, and a total of 180 samples of rice LCC were recorded. For Shanghai, images of rice plots with different nitrogen application rates, straw returning, and crop rotation systems were acquired by a 5-band multispectral camera from 2017 to 2018, and a total of 228 samples of rice LCC were recorded. The spectral features of LCC in each study area were analyzed and the results showed that the rice LCC in both regions had significant correlations with the reflectance at the green, red, and red-edge bands and 8 vegetation indices such as the normalized difference vegetation index (NDVI). The estimation models of LCC were built using the partial least squares regression (PLSR), support vector regression (SVR), and artificial neural network (ANN) methods. The PLSR models tended to be more stable and accurate than the SVR and ANN models when applied in different regions with R² values higher than 0.7 through different validations. The results demonstrated that the rice canopy LCC in different regions, cultivars, and different types of sensor-based data shared similar spectral features and could be estimated by general models. The general models can be implied to a wider geographic extent to accurately quantify rice LCC, which is helpful for growth assessment and production forecasts.

Accurate counting of the number of rice panicles per unit area is essential for rice yield estimation. However, intensive planting, complex growth environments, and the overlapping of rice panicles and leaves in paddy fields pose significant challenges for precise panicle detection. In this study, we propose YOLO-Rice, a rice panicle detection model based on the You Only Look Once version 8 nano (YOLOv8n). The model employs FasterNet, a lightweight backbone network, and incorporates a two-layer detection head to improve rice panicle detection performance while reducing the overall model size. Additionally, we integrate a Normalization-based Attention Module (NAM) and introduce a Minimum Point Distance-based IoU (MPDIoU) loss function to further improve the detection capability. The results demonstrate that the YOLO-Rice model achieved an object detection accuracy of 93.5% and a mean Average Precision (mAP) of 95.9%, with model parameters reduced to 32.6% of the original YOLOv8n model. When deployed on a Raspberry Pi 5, YOLO-Rice achieved 2.233 frames per second (FPS) on full-sized images, reducing the average detection time per image by 81.7% compared to YOLOv8n. By decreasing the input image size, the FPS increased to 11.36. Overall, the YOLO-Rice model demonstrates enhanced robustness and real-time detection capabilities, achieving higher accuracy and making it well-suited for deployment on low-cost portable devices. This model offers effective support for rice yield estimation, as well as for cultivation and breeding applications.

This study combines hyperspectral imaging technology with biochemical parameter analysis to facilitate the disease severity evaluation and early detection of lettuce downy mildew. The results reveal a significant negative correlation between the disease index (DI) and the levels of flavonoids (r = −0.523) and anthocyanins (r = −0.746), indicating the role of these secondary metabolites in enhancing plant resistance. Analysis of hyperspectral data identified that spectral regions (410–503 nm, 510–615 nm, and 630–690 nm) and vegetation indices like PRI and ARI2 were highly correlated with DI, flavonoids, and anthocyanins, providing potential spectral indicators for disease assessment and early detection. Moreover, regression models developed using Partial Least Squares (PLS), Random Forest (RF), and Convolutional Neural Network (CNN) algorithms demonstrated high accuracy and reliability in predicting DI, flavonoids, and anthocyanins, with the highest R² of 0.857, 0.910, and 0.963, respectively. The classification model using PLS, RF, and CNN successfully detected early physiological changes in lettuce within 24 h post-infection (highest accuracy = 0.764), offering an effective tool for early disease detection. The key spectral parameters in the PLS-DA model, like PRI, also demonstrated strong correlations with DI. These findings provide a scientific basis and practical tools for managing lettuce downy mildew and resistance breeding while laying a foundation for broader applications of hyperspectral imaging in plant pathology.

Accurate quantification of field-grown rice seedlings is essential for evaluating yield potential and guiding precision field management. Unmanned aerial vehicle (UAV)-based remote sensing, with its high spatial resolution and broad coverage, provides a robust basis for accurate seedling detection and population density estimation. However, in previous studies, UAVs were typically employed at relatively low altitudes, which provided high-resolution imagery and facilitated seedling recognition but limited efficiency. To enable large-area monitoring, higher flight altitudes are required, which reduces image resolution and adversely affects rice seedling recognition accuracy. In this study, UAVs were flown at a height of 30 m, and the resulting lower-resolution imagery, combined with the small size of seedlings, their dense spatial distribution, and the complex field background, necessitated algorithmic improvements for accurate detection. To address these challenges, we propose an enhanced You Only Look Once version 8 nano (YOLOv8n)-based detection model specifically designed to improve seedling recognition under high-altitude UAV imagery. The model incorporates an improved Bidirectional Feature Pyramid Network (BiFPN) for multi-scale feature fusion and small-object detection, a Global-to-Local Spatial Aggregation (GLSA) module for enriched spatial context modeling, and a Content-Guided Attention Fusion (CGAFusion) module to enhance discriminative feature learning. Experiments on high-altitude UAV imagery demonstrate that the proposed model achieves an mAP@0.5 of 94.7%, a precision of 91.0%, and a recall of 91.2%, representing a 2.3% improvement over the original YOLOv8n. These results highlight the model’s innovation in handling high-altitude UAV imagery for large-area rice seedling detection, demonstrating its effectiveness and practical potential under complex field conditions.

Accurate and timely mapping of paddy rice cultivation areas underpins food security assessment, agricultural resource management, and climate change adaptation. Because remote sensing provides repeated, large-scale observations, it has become indispensable for monitoring rice cultivation. This review synthesizes studies published since 2000, emphasizing methodological and technological advances since 2016, and critically evaluates the role of artificial intelligence (AI) in rice mapping. We examine the observational characteristics and inherent limitations of optical imagery, synthetic aperture radar (SAR), hyperspectral data, unmanned aerial vehicle (UAV) observations, and multi-source data fusion for rice identification. We then trace the evolution of mapping methods, from threshold-based classification and phenology-driven time-series analysis to machine learning, deep learning, and cloud-computing frameworks, comparing their accuracy, scalability, computational efficiency, transferability, and interpretability. Applications at global, regional, and field scales, each characterized by distinct objectives and spatial requirements, are reviewed alongside prevailing validation strategies and sources of uncertainty. Persistent challenges include cloud contamination, spectral and phenological confusion among crop types, ambiguous target definitions, limited cross-regional generalizability, difficulty in distinguishing multiple cropping cycles, insufficient evidence of model transferability, and geographical concentration of the existing evidence base. Priority directions include adaptive multi-source data fusion, self-supervised and physics-guided learning, Earth observation foundation models, explainable and uncertainty-aware AI, parcel-level product generation, and operational monitoring systems coupled with crop growth models.

To enable the rapid and accurate acquisition of phenotypic parameters during distinctness, uniformity, and stability (DUS) testing of edible mushroom varieties, and to address the low efficiency, strong subjectivity, and insufficient standardization of conventional manual measurement methods, this study used the fruiting bodies of Oudemansiella raphanipes as the research object and proposed an automatic phenotypic parameter extraction method based on deep learning instance segmentation. An instance-segmentation dataset containing 473 O. raphanipes images and 1419 annotated objects was constructed, and the YOLOv11s-seg model was improved by introducing the lightweight feature-extraction network StarNet_s050 to construct the YOLOv11s-star-seg model, thereby improving model inference efficiency and meeting practical application requirements. Under standardized imaging conditions, pileus and stipe regions were obtained from the instance-segmentation results, and image-scale calibration was combined to automatically extract DUS phenotypic parameters, including pileus diameter, pileus context thickness, stipe length, and stipe diameter, while quantitatively describing stipe shape. The experimental results showed that the YOLOv11s-star-seg model performed well in the instance segmentation of O. raphanipes, achieving an mAP50 of 99.1%, an F1 value of 98.9%, and an mIoU of 97.86%; compared with the YOLOv11s-seg model, the number of parameters was reduced by 39% and the inference time was reduced by 27%, thereby achieving model lightweighting while maintaining high segmentation accuracy. The automatically extracted results showed high consistency with the manual measurements; the coefficients of determination (R²) from linear regression fitting for stipe length, stipe diameter, and pileus diameter all exceeded 0.96, indicating that the method can effectively replace part of the manual measurement process. The results can provide technical support for the digitalization of DUS testing of O. raphanipes, intelligent breeding of edible mushrooms, and accurate phenotypic identification.