Rice bacterial leaf blight (BLB), caused by the bacterium Xanthomonas oryzae pv. oryzae, severely damages leaves during rice growth, leading to reduced yield or even death. This study aimed to develop an automated identification and assessment method for rice BLB based on multispectral UAV imagery to overcome the limitations of in-field inspection methods. By obtaining multispectral image data of rice fields and extracting color features (CFs), texture features (TFs), and vegetation indices (VIs) of rice canopy using image processing techniques, three algorithms, namely, Support Vector Machine (SVM), Random Forest (RF), and Back Propagation Neural Network (BPNN), were utilized to establish a monitoring model for the severity levels of rice BLB. The classification results of several models are compared, with the overall Correct Identification Rate (CIR) of the three-feature fusion classification algorithm generally higher than the other two. Among the three algorithms, the RF algorithm performs the best, with a CIR reaching 93.4% and a Kappa coefficient of 0.91. The BPNN algorithm follows, with a CIR of 82.1% and a Kappa coefficient of 0.76, showing moderate effectiveness. Lastly, the SVM algorithm performs the poorest, with a CIR of 65.1% and a Kappa coefficient of 0.54. A rice BLB large-scale detection framework based on unmanned aerial vehicle (UAV) images was designed, and a graphical user interface (GUI) was developed using Python language to achieve automated processing from image input to final recognition results, achieving good results.

Traditional monitoring methods rely on manual field surveys, which are subjective, inefficient, and unable to meet the demand for large-scale, rapid monitoring. By using unmanned aerial vehicles (UAVs) to capture high-resolution images of rice canopy diseases and pests, combined with deep learning (DL) techniques, accurate and timely identification of diseases and pests can be achieved. We propose a method for identifying rice canopy diseases and pests using an improved YOLOv5 model (YOLOv5_DWMix). By incorporating deep separable convolutions, the MixConv module, attention mechanisms, and optimized loss functions into the YOLOv5 backbone, the model’s speed, feature extraction capability, and robustness are significantly enhanced. Additionally, to tackle the challenges posed by complex field environments and small datasets, image augmentation is employed to train the YOLOv5_DWMix model for the recognition of four common rice canopy diseases and pests. Results show that the improved YOLOv5 model achieves 95.6% average precision in detecting these diseases and pests, a 4.8% improvement over the original YOLOv5 model. The YOLOv5_DWMix model is effective and advanced in identifying rice diseases and pests, offering a solid foundation for large-scale, regional monitoring.

To address the issues of resource wastage, environmental pollution, and low crop productivity associated with traditional agricultural fertilization methods, we present a smart fertilization robot system based on multi-sensor fusion. This system integrates visual (OpenMV), temperature and humidity (DHT11), and light intensity (BH1750) sensors, leveraging advanced algorithms such as Kalman filtering, Bayesian inference, and Long Short-Term Memory (LSTM) networks to achieve robust environmental perception and precise fertilization decision-making in complex agricultural settings. Experimental results demonstrate significant improvements in fertilization accuracy and resource efficiency, with a 20% reduction in fertilizer waste and an increase in fertilization precision from ±20% to ±2% compared to manual fertilization. The system also exhibits strong adaptability and stability under varying light intensities, soil moistures, and crop growth stages. Our study not only provides an effective technological solution for precision agriculture but also paves a new way for the application of multi-sensor fusion in agricultural robots.

[Objective]Determining the geographic coordinates of the operation area is one of the key problems for the implementation of precision target spraying by UAV.If there are many operation areas to be sprayed or the operation areas to be sprayed are paddy fields,etc.,the traditional manual point-by-point measurement method in the field has the disadvantages of large workload,cumbersome measurement steps,and difficult access to the measurement area.Therefore,the geographic coordinates of a limited number of locations located at the boundary or outside the boundary of the operation area are measured,and then the geographic coordinates of each operation area are calculated according to a certain algorithm,which will overcome the above disadvantages and have great practical application value,and will greatly improve the efficiency and application of precision target spraying.[Method]According to the solving requirements of the geographic coordinates of the operation area for precision target spraying,a method for obtaining the relative position of ground points is found in surveying.At the same time,this method is introduced and improved.Two algorithms for calculating the geographic coordinates of the operation area are proposed,which are based on the geometry method assuming that the earth is a sphere and the projection method assuming that the earth is an ellipsoid.[Result]The experiments show that the errors of the two algorithms are in the range of centimeters compared with the measured value,which meets the requirements of precision spraying on the target.In terms of solving speed,the geometric method is faster than the projection method.In terms of solving complexity,the geometric method is better than the projection method.[Conclusion]The two algorithms proposed in this paper to solve the geographic coordinates of the operating cell-the geometric method and the projection method can meet the requirements of precision spraying on the target in terms of precision.However,in the process of solving the geographic coordinates of the operating cell using the two algorithms respectively,it is found that in terms of calculation complexity and calculation speed,the geometric method is better,simpler and more suitable for solving the geographic coordinates of the operating cell when implementing precision spraying on the target.

Crop nutrient biochemical information (mainly including chlorophyll class and nutrient elements mainly nitrogen, phosphorus and potassium) is an important basis for revealing crop growth and development patterns and their relationship with the environment. Hyperspectral technology has been rapidly developed and applied in crop nutrient biochemical information monitoring research. This paper firstly describes the theoretical basis of hyperspectral technology for monitoring crop nutrients and biochemical information. Then, the research progress of hyperspectral technology in monitoring nutrient and biochemical information of crops in different growth periods or different growth environments is outlined. Meanwhile, the shortcomings of the current technology in these research directions and the future research trends are discussed. Finally, the modeling methods for building crop nutrient biochemical information monitoring models by applying hyperspectral data are systematically outlined. And the effects of different spectral pre-processing methods, spectral effective information extraction methods and modeling algorithms on the accuracy of monitoring models are analyzed. On this basis, the challenges and prospects of hyperspectral technology in monitoring crop nutrient biochemical information are presented, aiming to provide relevant theoretical basis and technical reference for the research related to monitoring and inversion of crop physiological parameters based on hyperspectral technology.

Disease and pest stress is one of the important threats to crop growth and development, which have a very adverse impact on crop yield and quality every year, even leading to crop failure. Currently, the use of plant protection unmanned aerial vehicles (UAVs) for pesticide spraying is the most effective means of controlling crop diseases and pests. However, the areas where crop pests and diseases occur are often presented in "point-like" or "patchy" forms, and the UAV's full-coverage spraying method results in a great waste of pesticides. Therefore, there is an urgent need to research a method for identifying the area of diseases and pest stress to achieve precise targeted spraying, in order to reduce the amount of pesticides used and improve their utilization. By analyzing the low-altitude remote sensing images of crop fields taken by UAVs, real-time pesticide spraying prescription maps can be generated to meet the demand for precise targeted spraying. This review focuses on the actual needs of precise targeted spraying by plant protection UAVs. Firstly, the RS monitoring mechanism of crop diseases and pests by UAVs is studied. Secondly, a comprehensive investigation of the literature on UAV Low-altitude Remote Sensing (UAV-LARS) technology for monitoring and identifying crop diseases and pests is conducted, summarizing the research progress in monitoring and identifying crop diseases and pests, especially in wheat, cotton, and rice. Finally, the key issues to be addressed and the future development direction of UAV-LARS monitoring of crop diseases and pests are proposed.

The realization that mobile phones can detect rice diseases and insect pests not only solves the problems of low efficiency and poor accuracy from manually detection and reporting, but it also helps farmers detect and control them in the field in a timely fashion, thereby ensuring the quality of rice grains. This study examined two Improved detection models for the detection of six high-frequency diseases and insect pests. These models were the Improved You Only Look Once (YOLO)v5s and YOLOv7-tiny based on their lightweight object detection networks. The Improved YOLOv5s was introduced with the Ghost module to reduce computation and optimize the model structure, and the Improved YOLOv7-tiny was introduced with the Convolutional Block Attention Module (CBAM) and SIoU to improve model learning ability and accuracy. First, we evaluated and analyzed the detection accuracy and operational efficiency of the models. Then we deployed two proposed methods to a mobile phone. We also designed an application to further verify their practicality for detecting rice diseases and insect pests. The results showed that Improved YOLOv5s achieved the highest F1-Score of 0.931, 0.961 in mean average precision (mAP) (0.5), and 0.648 in mAP (0.5:0.9). It also reduced network parameters, model size, and the floating point operations per second (FLOPs) by 47.5, 45.7, and 48.7%, respectively. Furthermore, it increased the model inference speed by 38.6% compared with the original YOLOv5s model. Improved YOLOv7-tiny outperformed the original YOLOv7-tiny in detection accuracy, which was second only to Improved YOLOv5s. The probability heat maps of the detection results showed that Improved YOLOv5s performed better in detecting large target areas of rice diseases and insect pests, while Improved YOLOv7-tiny was more accurate in small target areas. On the mobile phone platform, the precision and recall of Improved YOLOv5s under FP16 accuracy were 0.925 and 0.939, and the inference speed was 374 ms/frame, which was superior to Improved YOLOv7-tiny. Both of the proposed improved models realized accurate identification of rice diseases and insect pests. Moreover, the constructed mobile phone application based on the improved detection models provided a reference for realizing fast and efficient field diagnoses.

5G技术与无人机图传的结合,有助于提高图传的画质和传输速率,推进无人机在多个行业的深入应用,并且运用在植保无人机上,还能促进植保无人机对靶喷洒技术的发展.通过梳理无人机图传的几种方式,分析5G无人机图传的优势,综述国内外5G无人机图传和植保无人机对靶喷洒的研究现状,指出5G信号不稳定、图传系统功能单一、5G无人机图传在植保无人机对靶喷洒中研究缺乏等问题,最后提出提高5G信号覆盖与安全建设、增加地面端功能、加强5G无人机图传与植保无人机对靶喷洒研究相结合的发展建议.期望为应用5G无人机图传发展植保无人机精准施药技术提供参考.

Effective detection of rice spikelet flowering is crucial to the determination of optimal pollination timing for hybrid rice seed production. Currently, the detection of rice spikelet flowering status relies on manual observation of farmers, which has low efficiency and large errors. This study attempts to acquire rice spikelet flowering information using a hyperspectral technique and machine learning in order to meet the needs of hybrid rice seed pollination rapidly and automatically. Hyperspectral data of rice male parents with flowering and non-flowering in two experimental sites were collected with an ASD FieldSpec® HandHeld™2 spectrometer. Three traditional classifiers, Random Forest (RF), Support Vector Machine (SVM) and Back Propagation (BP) neural network, and Convolutional Neural Network (CNN), were used to build classification models for rice spikelets flowering detection. Three data processing methods, PCA feature extraction, GA feature selection, and the PCA and GA combination algorithm, were used for data dimensionality reduction. By comparing the precision and recall rate of different algorithms and data processing methods, the algorithms applicable to identify rice spikelet flowering were investigated. Results show that by evaluating different feature reduction methods and classifiers, the optimal model for rice spikelets flowering detection is the BP model with PCA feature extraction. The accuracy of the model reaches up to 96–100%. Hyperspectral technology and machine learning algorithm are capable of effective detection of rice spikelet flowering. This study provides technical reference for accurate judgment of rice flowering and helps to determine the optimal operation time for supplementary pollination of hybrid rice.

Multispectral technology has a wide range of applications in agriculture. By obtaining spectral information during crop production, key information such as growth, pests and diseases, fertilizer and pesticide application can be determined quickly, accurately and efficiently. The scientific analysis based on Web of Science aims to understand the research hotspots and areas of interest in the field of agricultural multispectral technology. The publications related to agricultural multispectral research in agriculture between 2002 and 2021 were selected as the research objects. The softwares of CiteSpace, VOSviewer, and Microsoft Excel were used to provide a comprehensive review of agricultural multispectral research in terms of research areas, institutions, influential journals, and core authors. Results of the analysis show that the number of publications increased each year, with the largest increase in 2019. Remote sensing, imaging technology, environmental science, and ecology are the most popular research directions. The journal Remote Sensing is one of the most popular publishers, showing a high publishing potential in multispectral research in agriculture. The institution with the most research literature and citations is the USDA. In terms of the number of papers, Mtanga is the author with the most published articles in recent years. Through keyword co-citation analysis, it is determined that the main research areas of this topic focus on remote sensing, crop classification, plant phenotypes and other research areas. The literature co-citation analysis indicates that the main research directions concentrate in vegetation index, satellite remote sensing applications and machine learning modeling. There is still a lot of room for development of multi-spectrum technology. Further development can be carried out in the areas of multi-device synergy, spectral fusion, airborne equipment improvement, and real-time image processing technology, which will cooperate with each other to further play the role of multi-spectrum in agriculture and promote the development of agriculture.

Automatic acquisition of the canopy volume parameters of the Citrus reticulate Blanco cv. Shatangju tree is of great significance to precision management of the orchard. This research combined the point cloud deep learning algorithm with the volume calculation algorithm to segment the canopy of the Citrus reticulate Blanco cv. Shatangju trees. The 3D (Three-Dimensional) point cloud model of a Citrus reticulate Blanco cv. Shatangju orchard was generated using UAV tilt photogrammetry images. The segmentation effects of three deep learning models, PointNet++, MinkowskiNet and FPConv, on Shatangju trees and the ground were compared. The following three volume algorithms: convex hull by slices, voxel-based method and 3D convex hull were applied to calculate the volume of Shatangju trees. Model accuracy was evaluated using the coefficient of determination (R2) and Root Mean Square Error (RMSE). The results show that the overall accuracy of the MinkowskiNet model (94.57%) is higher than the other two models, which indicates the best segmentation effect. The 3D convex hull algorithm received the highest R2 (0.8215) and the lowest RMSE (0.3186 m3) for the canopy volume calculation, which best reflects the real volume of Citrus reticulate Blanco cv. Shatangju trees. The proposed method is capable of rapid and automatic acquisition for the canopy volume of Citrus reticulate Blanco cv. Shatangju trees.

Aerial electrostatic spray technology for agriculture is the integration of precision agricultural aviation and electrostatic spray technology. It is one of the research topics that have been paid close attention to by scholars in the field of agricultural aviation. This study summarizes the development of airborne electrostatic spray technology for agricultural use in China, including the early research and exploration of Chinese institutions and researchers in the aspects of nozzle structure design optimization and theoretical simulation. The research progress of UAV-based aerial electrostatic spray technology for agricultural use in China was expounded from the aspects of nozzle modification, technical feasibility study, influencing mechanism of various factors, and field efficiency tests. According to the current development of agricultural UAVs and the characteristics of the farmland environment in China, the UAV-based aerial electrostatic spray technology, which carries the airborne electrostatic spray system on the plant protection UAVs, has a wide potential in the future. At present, the application of UAV-based aerial electrostatic spray technology has yet to be further improved due to several factors, such as the optimization of the test technology for charged droplets, the impact of UAV rotor wind field, comparison study on charging modes, and the lack of technical accumulation in the research of aerial electrostatic spray technology. With the continuous improvement of the research system of agricultural aviation electrostatic spray technology, UAV-based electrostatic spray technology will give play to the advantages in increasing the droplets deposition on the target and reducing environmental pollution from the application of pesticides. This study is capable of providing a reference for the development of the UAV-based agricultural electrostatic spray technology and the spray equipment.

为探讨植保无人机(UAV)减量施药对水稻病虫害的防治效果,该研究采用P20型植保无人机进行水稻田间施药作业.分别在水稻分蘖末期、孕穗期开展了两种施药液量(15、22.5 L/hm2)的水稻冠层雾滴沉积试验,以及两种施药液量下480、540、600 mL/hm23种农药剂量(阿维·氯苯酰推荐剂量的80％、90％、100％)的防治稻纵卷叶螟减量施药田间药效试验,并与背负式电动喷雾器(Knapsack Electric Sprayer,KES)人工施药的常规防治方法进行施药效果对比.雾滴沉积试验结果表明,水稻冠层上部的雾滴分布均匀性优于水稻冠层下部;施药液量15、22.5 L/hm2的冠层上部雾滴沉积有显著差异,且施药液量22.5 L/hm2的冠层上部雾滴沉积显著优于施药液量15 L/hm2.药效试验结果表明,农药剂量越大稻纵卷叶螟防治效果越好,采用农药剂量100％的植保无人机施药防治效果最好,并优于KES人工施药;施药液量15、22.5 L/hm2的稻纵卷叶螟防治效果有显著差异,且施药液量22.5 L/hm2较15 L/hm2的防治效果更好;施药液量15 L/hm2且农药剂量90％、施药液量22.5 L/hm2且农药剂量80％与KES人工施药的防治效果没有显著差异.采用植保无人机施药防治稻纵卷叶螟,施药液量22.5 L/hm2可以获得更好的雾滴沉积和稻纵卷叶螟防治效果;施药液量22.5 L/hm2时,减少20％的农药剂量也能保证稻纵卷叶螟防治效果.该结果对水稻田间植保无人机减量施药具有实践指导意义.

Real-time analysis of UAV low-altitude remote sensing images at airborne terminals facilitates the timely monitoring of weeds in the farmland. Aiming at the real-time identification of rice weeds by UAV low-altitude remote sensing, two improved identification models, MobileNetV2-UNet and FFB-BiSeNetV2, were proposed based on the semantic segmentation models U-Net and BiSeNetV2, respectively. The MobileNetV2-UNet model focuses on reducing the amount of calculation of the original model parameters, and the FFB-BiSeNetV2 model focuses on improving the segmentation accuracy of the original model. In this study, we first tested and compared the segmentation accuracy and operating efficiency of the models before and after the improvement on the computer platform, and then transplanted the improved models to the embedded hardware platform Jetson AGX Xavier, and used TensorRT to optimize the model structure to improve the inference speed. Finally, the real-time segmentation effect of the two improved models on rice weeds was further verified through the collected low-altitude remote sensing video data. The results show that on the computer platform, the MobileNetV2-UNet model reduced the amount of network parameters, model size, and floating point calculations by 89.12%, 86.16%, and 92.6%, and the inference speed also increased by 2.77 times, when compared with the U-Net model. The FFB-BiSeNetV2 model improved the segmentation accuracy compared with the BiSeNetV2 model and achieved the highest pixel accuracy and mean Intersection over Union ratio of 93.09% and 80.28%. On the embedded hardware platform, the optimized MobileNetV2-UNet model and FFB-BiSeNetV2 model inferred 45.05 FPS and 40.16 FPS for a single image under the weight accuracy of FP16, respectively, both meeting the performance requirements of real-time identification. The two methods proposed in this study realize the real-time identification of rice weeds under low-altitude remote sensing by UAV, which provide a reference for the subsequent integrated operation of plant protection drones in real-time rice weed identification and precision spraying.

农业航空静电喷雾技术作为中国发展精准农业航空应用技术的内容之一,对农药的有效利用和减少环境污染有积极意义.农业航空静电喷雾技术在国外发展较早也相对成熟,美国已有应用于有人机的商业化产品,并在美国、巴西等国各类粮食作物、经济作物和杂草防治作业中开展了大规模田间应用.该研究首先从基础研究、田间应用和优化工作等方面梳理了国外农业航空静电喷雾技术的研究进展,分析了农业航空静电喷雾技术在增加雾滴沉积、减少飘移和具备低施药液量等方面的优势.在此基础上结合中国植保无人机快速发展的实际对研究和应用适合中国国情的农业航空静电喷雾技术进行思考,提出了农业航空静电喷雾技术的研究路线,最后从采用接触式等非感应式充电方式、开发农业航空静电喷雾的测量技术,以及思考荷质比作为衡量指标的意义等方面探讨了可进行深入研究的方向.中国农业航空静电喷雾技术研究特别在植保无人机静电喷雾技术方面的研究与应用有很大的发展空间,可参考国外经验,围绕航空静电喷雾技术的基础性研究、田间试验、成果转化、示范推广和服务指导全方面制定发展规划,把单一强调对雾滴带电的实现转向对技术系统的整体研究.

[目的]研究多旋翼植保无人机减量施药对雾滴沉积效果以及稻飞虱防治效果的影响,促进水稻减量施药技术发展.[方法]采用M45多旋翼植保无人机开展水稻施药田间试验,选取15.0和22.5?L/hm2的施药液量,以及人工施药推荐剂量100％、90％、80％的3种减量农药剂量,研究不同施药液量和减量农药剂量对雾滴沉积效果以及稻飞虱防治效果的影响.[结果]水稻冠层上部的雾滴沉积量明显优于冠层下部,2种施药液量以及3种减量农药剂量对雾滴沉积量的影响不显著;施药1周后稻飞虱数量显著减少.在相同施药液量条件下,减量农药剂量的变化对稻飞虱防治效果影响不明显.80％的农药剂量能满足稻飞虱防治要求.[结论]无人机水稻施药作业中可选择80％的农药剂量进行减量施药.本研究可为水稻减量施药、减少水稻植保作业成本提供有益参考.

The timely and efficient generation of weed maps is essential for weed control tasks and precise spraying applications. Based on the general concept of site-specific weed management (SSWM), many researchers have used unmanned aerial vehicle (UAV) remote sensing technology to monitor weed distributions, which can provide decision support information for precision spraying. However, image processing is mainly conducted offline, as the time gap between image collection and spraying significantly limits the applications of SSWM. In this study, we conducted real-time image processing onboard a UAV to reduce the time gap between image collection and herbicide treatment. First, we established a hardware environment for real-time image processing that integrates map visualization, flight control, image collection, and real-time image processing onboard a UAV based on secondary development. Second, we exploited the proposed model design to develop a lightweight network architecture for weed mapping tasks. The proposed network architecture was evaluated and compared with mainstream semantic segmentation models. Results demonstrate that the proposed network outperform contemporary networks in terms of efficiency with competitive accuracy. We also conducted optimization during the inference process. Precision calibration was applied to both the desktop and embedded devices and the precision was reduced from FP32 to FP16. Experimental results demonstrate that this precision calibration further improves inference speed while maintaining reasonable accuracy. Our modified network architecture achieved an accuracy of 80.9% on the testing samples and its inference speed was 4.5 fps on a Jetson TX2 module (Nvidia Corporation, Santa Clara, CA, USA), which demonstrates its potential for practical agricultural monitoring and precise spraying applications.

[目的]获取水稻田的低空遥感图像并分析得到杂草分布图,为田间杂草精准施药提供参考.[方法]使用支持向量机(SVM)、K最近邻算法(KNN)和AdaBoost 3种机器学习算法,对经过颜色特征提取和主成分分析(PCA)降维后的无人机拍摄的水稻田杂草可见光图像进行分类比较;引入一种无需提取特征和降维、可自动获取图像特征的卷积神经网络(CNN),对水稻田杂草图像进行分类以提升分类精度.[结果]SVM、KNN和AdaBoost对测试集的测试运行时间分别为0.5004、2.2092和0.4111 s,分类精度分别达到89.75％、85.58％和90.25％,CNN对图像的分类精度达到92.41％,高于上述3种机器学习算法的分类精度.机器学习算法及CNN均能有效识别水稻和杂草,获取杂草的分布信息,生成水稻田间的杂草分布图.[结论]CNN对水稻田杂草的分类精度最高,生成的水稻田杂草分布图效果最好.

[目的]探究研究区农作物分类最佳时相;结合遥感指数探究一种有效的多时相分类方法,提取主要农作物种植分布情况.[方法]基于多时相Sentinel-2卫星数据,采用支持向量机的分类方法对不同时相进行分类,对比分类精度;融合时间序列的NDVI、MNDWI指数之后采用支持向量机的方法进行分类,之后分别利用MNDWI和CI指数结合决策树的分类方法提取水域和田埂.[结果]7月份的分类效果最好,总体精度达到91.05％,Kappa系数达到0.8518;通过时相数据不同组合的分类精度比较,采用3—10月NDVI数据叠加后分类的效果较好,总体精度达到92.25％,Kappa系数达到0.8736;对比3种不同分类方法,以支持向量机的分类结果精度最高,总体精度达到94.19％,Kappa系数达到0.9024.[结论]7月份是研究区农作物分类的最佳时相;多时相分类精度明显高于单景数据分类;结合多时相NDVI、MNDWI、CI 3种遥感指数进行分类可以有效提取研究区主要农作物的种植分布情况.

Rice is the most important food crop in the world, which is meaningful to ensure the quality and quantity of rice production. During the plantation process, weeds are the key factor to influence the rice yields. In recent years, the chemical control becomes the most widely used means to control the weed infestation because of its advantage in pesticide effects and efficiency. However, excessive use of herbicides has caused negative effects on the rice quality as well as the environment. An accurate weed cover map can provide support information for specific site weed management (SSWM) applications, which may well address the problem of traditional chemical controls. In this work, the unmanned aerial vehicle (UAV) imagery was captured on four different dates over two different rice fields. Object-based image analysis (OBIA) and deep learning approaches were applied to the weed mapping task of the UAV imagery. For the OBIA methods, the multiresolution segmentation and an improved k-means method were applied to segment the imagery into different objects; the colour and texture features were extracted and concatenated into a feature vector; back propagation (BP) neural network, support vector machine (SVM) and random forest were used for classification. After careful hyperparameter optimization and model selection, it was proven that the OBIA method achieved the accuracy of 66.6% mean intersection over union (MIU) on the testing set, and the inference speed is 2343.5 ms for an image sample. For the deep learning approach, the fully convolutional network (FCN) was applied for the pixel-wise classification task; transfer learning was used, and four pretrained convolutional neural networks (AlexNet, VGGNet, GoogLeNet, and ResNet) were transferred to our dataset via fine-tuning technique. Traditional skip architecture and fully connected conditional random fields (CRF) were used to improve the spatial details of FCN; after that, this work proposed to use a partially connected CRF as post processing, which may significantly accelerate the inference speed of fully connected CRF. Besides one single improvement method, hybrid improvement methods were applied and tested. Experimental results showed that the VGGNet-based FCN achieved the highest accuracy; for the improvement methods, the skip architecture and newly proposed partially connected CRF effectively improved the accuracy, and the hybrid improvement method (skip architecture and partially connected CRF) further improved the performance. The hybrid improvement method achieved 80.2% MIU on the testing set, and the inference speed for an image sample is 326.8 ms. The experimental results of this work demonstrated that the UAV remote-sensing utilizing deep learning method can provide reliable support information for SSWM applications in rice fields.