Controlling crop diseases and pests is essential for intelligent agriculture (IA) due to the significant reduction in crop yield and quality caused by these problems. In recent years, the remote sensing (RS) areas has been prevailed over by unmanned aerial vehicle (UAV)-based applications. Herein, by using methods such as keyword co-contribution analysis and author co-occurrence analysis in bibliometrics, we found out the hot-spots of this field. UAV platforms equipped with various types of cameras and other advanced sensors, combined with artificial intelligence (AI) algorithms, especially for deep learning (DL) were reviewed. Acknowledging the critical role of comprehending crop diseases and pests, along with their defining traits, we provided a concise overview as indispensable foundational knowledge. Additionally, some widely used traditional machine learning (ML) algorithms were presented and the performance results were tabulated to form a comparison. Furthermore, we summarized crop diseases and pests monitoring techniques using DL and introduced the application for prediction and classification. Take it a step further, the newest and the most concerned applications of large language model (LLM) and large vision model (LVM) in agriculture were also mentioned herein. At the end of this review, we comprehensively discussed some deficiencies in the existing research and some challenges to be solved, as well as some practical solutions and suggestions in the near future.

Remote estimation of flower number in oilseed rape under different nitrogen (N) treatments is imperative in precision agriculture and field remote sensing, which can help to predict the yield of oilseed rape. In this study, an unmanned aerial vehicle (UAV) equipped with Red Green Blue (RGB) and multispectral cameras was used to acquire a series of field images at the flowering stage, and the flower number was manually counted as a reference. Images of the rape field were first classified using K-means method based on Commission Internationale de l’Éclairage (CIE) L*a*b* space, and the result showed that classified flower coverage area (FCA) possessed a high correlation with the flower number (r² = 0.89). The relationships between ten commonly used vegetation indices (VIs) extracted from UAV-based RGB and multispectral images and the flower number were investigated, and the VIs of Normalized Green Red Difference Index (NGRDI), Red Green Ratio Index (RGRI) and Modified Green Red Vegetation Index (MGRVI) exhibited the highest correlation to the flower number with the absolute correlation coefficient (r) of 0.91. Random forest (RF) model was developed to predict the flower number, and a good performance was achieved with all UAV variables (r² = 0.93 and RMSEP = 16.18), while the optimal subset regression (OSR) model was further proposed to simplify the RF model, and a better result with r² = 0.95 and RMSEP = 14.13 was obtained with the variable combination of RGRI, normalized difference spectral index (NDSI _{(944, 758)}) and FCA. Our findings suggest that combining VIs and image classification from UAV-based RGB and multispectral images possesses the potential of estimating flower number in oilseed rape.

Introduction: Agriculture is a cornerstone of human society but faces significant challenges, including pests, diseases, and the need for increased production efficiency. Large models, encompassing large language models, large vision models, and multimodal large language models, have shown transformative potential in various domains. This review aims to explore the potential applications of these models in agriculture to address existing problems and improve production.Methods: We conduct a systematic review of the development trajectories and key capabilities of large models. A bibliometric analysis of literature from Web of Science and arXiv is performed to quantify the current research focus and identify the gap between the potential and the application of large models in the agricultural sector.Results: Our analysis confirms that agriculture is an emerging but currently underrepresented field for large model research. Nevertheless, we identify and categorize promising applications, including tailored models for agricultural question-answering, robotic automation, and advanced image analysis from remote sensing and spectral data. These applications demonstrate significant potential to solve complex, nuanced agricultural tasks.Discussion: This review culminates in a pragmatic framework to guide the choice between large and traditional models, balancing data availability against deployment constraints. We also highlight critical challenges, including data acquisition, infrastructure barriers, and the significant ethical considerations for responsible deployment. We conclude that while tailored large models are poised to greatly enhance agricultural efficiency and yield, realizing this future requires a concerted effort to overcome the existing technical, infrastructural, and ethical hurdles.

Accurate categorization and timely control of leaf diseases are crucial for citrus growth. We proposed the Multi-Models Fusion Network (MMFN) for citrus leaf diseases detection based on model fusion and transfer learning. Compared to traditional methods, the algorithm (integrating transfer learning Alexnet, VGG, and Resnet) we proposed can address the issues of limited categories, slow processing speed, and low recognition accuracy. By constructing efficient deep learning models and training and optimizing them with a large dataset of citrus leaf images, we ensured the broad applicability and accuracy of citrus leaf disease detection, achieving high-precision classification. Herein, various deep learning algorithms, including original Alexnet, VGG, Resnet, and transfer learning versions Resnet34 (Pre_Resnet34) and Resnet50 (Pre_Resnet50) were also discussed and compared. The results demonstrated that the MMFN model achieved an average accuracy of 99.72% in distinguishing between diseased and healthy leaves. Additionally, the model attained an average accuracy of 98.68% in the classification of multiple diseases (citrus huanglongbing (HLB), greasy spot disease and citrus canker), insect pests (citrus leaf miner), and deficiency disease (zinc deficiency). These findings conclusively illustrate that deep learning model fusion networks combining transfer learning and integration algorithms can automatically extract image features, enhance the automation and accuracy of disease recognition, demonstrate the significant potential and application value in citrus leaf disease classification, and potentially drive the development of smart agriculture.

Accurate and timely prediction of oilseed rape yield is crucial in precision agriculture and field remote sensing. We explored the feasibility and potential for predicting oilseed rape yield through the utilization of a UAV-based platform equipped with RGB and multispectral cameras. Genetic algorithm–partial least square was employed and evaluated for effective wavelength (EW) or vegetation index (VI) selection. Additionally, different machine learning algorithms, i.e., multiple linear regression (MLR), partial least squares regression (PLSR), least squares support vector machine (LS-SVM), back propagation neural network (BPNN), extreme learning machine (ELM), and radial basis function neural network (RBFNN), were developed and compared. With multi-source data fusion by combining vegetation indices (color and narrow-band VIs), robust prediction models of yield in oilseed rape were built. The performance of prediction models using the combination of VIs (RBFNN: R_pre = 0.8143, RMSEP = 171.9 kg/hm²) from multiple sensors manifested better results than those using only narrow-band VIs (BPNN: R_pre = 0.7655, RMSEP = 188.3 kg/hm²) from a multispectral camera. The best models for yield prediction were found by applying BPNN (R_pre = 0.8114, RMSEP = 172.6 kg/hm²) built from optimal EWs and ELM (R_pre = 0.8118, RMSEP = 170.9 kg/hm²) using optimal VIs. Taken together, the findings conclusively illustrate the potential of UAV-based RGB and multispectral images for the timely and non-invasive prediction of oilseed rape yield. This study also highlights that a lightweight UAV equipped with dual-image-frame snapshot cameras holds promise as a valuable tool for high-throughput plant phenotyping and advanced breeding programs within the realm of precision agriculture.

Citrus leaf diseases significantly affect production efficiency and fruit quality in the citrus industry. To effectively identify and classify citrus leaf diseases, this study proposed a classification approach leveraging deep learning techniques (YOLOV8 equipped with CSPPC, MultiDimen, SpatialConv, YOLOV8-CMS). Additionally, a segmentation method was utilized to extract leaf and lesion areas for disease severity grading based on their pixel ratio. By collecting and preprocessing a citrus leaf image dataset, the YOLOV8-CMS model was trained for disease classification. The model integrated MultiDimen attention, SpatialConv, and the CSPPC module to enhance performance. Furthermore, a segmentation approach was applied to precisely segment both leaf and lesion areas, enabling a quantitative assessment of disease severity. To verify the effectiveness of the proposed approach, multiple YOLO-based architectures, including different YOLOV8 series models, YOLOV5, and YOLOV3, were compared and analyzed. Results demonstrated that the proposed method achieved outstanding performance in citrus leaf disease classification, with an mAP50 of 98.2% in distinguishing healthy and diseased leaves and an accuracy of 97.9% in multi-class disease classification tasks. The proposed YOLOV8-CMS model outperformed traditional methods in citrus leaf disease classification, while the segmentation-based approach enabled an accurate and quantitative assessment of disease severity. These findings highlighted the potential of deep learning in precision agriculture, contributing to more effective disease management in citrus production.