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[学术文献] 时空协同的精准农业遥感研究 进入全文

地球信息科学学报

高分辨率遥感对地观测为我们从空间与时间2个维度客观反演地表格局—过程提供了有效的技术支撑。本文遵循时空协同的研究思路,基于高分辨率遥感影像,开展了农业遥感领域2个典型的问题研究:①提出了一种基于影像视觉特征的耕地分区分层提取方法,该方法在利用DEM数据进行分区的基础上,根据不同区域内耕地所呈现的几何特征和纹理特征差异,分别设计了不同的耕地提取模型;②构建了一种地块尺度的作物生长参数反演方法,方法以地块为基本单元,在空间、时间及属性组合约束下进行作物理化参数反演。本研究以贵州省安顺市西秀区和广西扶绥县耕地提取进行了耕地地块提取示范,以扶绥县进行了基于耕地地块和中空间分辨率时间序列遥感数据的甘蔗叶面积指数反演。其中,对于安顺市西秀区的耕地地块提取结果而言,形态精度(IoU)大于0.7的地块超过60%,规则耕地、梯田以及林草地等的类型精度均超过了80%;对于扶绥县甘蔗叶面积指数反演的结果而言,其结果可以较为精确地反映出基地甘蔗与非基地甘蔗的差异,基地甘蔗在品质上要优于非基地甘蔗。西南山地区的耕地形态提取/类型判别和地块甘蔗叶面积指数应用验证均证明了方法的可行性。结果表明,协同使用多源高分辨率数据是实现精准农业遥感研究的有效途径。

[学术文献] 基于RC峰值检测的土壤水分传感器设计与性能试验 进入全文

农业机械学报

针对现有电容式土壤水分传感器精度低、功耗高、价格高、标定过程复杂等问题,基于RC稳态响应峰值检测原理,设计了一款土壤水分传感器,并对传感器敏感区域、电学特性、标定模型、温度和电导率特性进行了测试。实验结果表明,传感器测量体积含水率平均灵敏度为12. 187 mV,敏感区域为3. 8 cm×2. 5 cm×7. 2 cm;输出信号不受供电电压影响,消耗电流仅为3~4 mA;通过在不同介电常数溶液中标定,结合TOPP经验公式,建立的指数标定模型的决定系数R~2均大于0. 96;传感器温漂引起的测量误差约为0. 5%,在0~2 000μS/cm范围内电导率引起的最大测量误差小于4. 2%,传感器最大实测误差为2. 17%。 

[会议论文] Modeling and Control of Heterogeneous Agricultural Field Robots based on Ramadge-Wonham Theory 进入全文

IEEE

Cooperation among heterogeneous agricultural field robots in an agricultural environment guarantees the advantages of effectiveness and scalability. However, traditional control theories for coordination among multiple heterogeneous robots lack a systematic modeling method and a control strategy under unstructured and uncertain environments. To handle these limitations, a novel approach based on the Ramadge-Wonham theory and discrete event system is proposed in this letter. Specifications and a supervisory controller based on discrete event models were defined from an agricultural perspective considering cooperation among heterogeneous agricultural field robots. Discrete event systems were modeled through the automata theory and the behavior of heterogeneous field robots satisfied the designed specifications. The resulting supervisor ensures that the control objectives of formation control, obstacle avoidance, movements, and path following are satisfied. The approach and architecture proposed in this study were validated using a physics-based simulator and field experiments.

[学术文献] 新世纪以来我国农村农业信息化研究的热点识别与趋势预测 进入全文

科技管理研究

从中国知网(CNKI)的CSSCI期刊文献数据库中获取2000—2016年农村农业信息化研究文献849篇,利用CiteSpaceⅢ软件绘制有关科学知识图谱,结合文献的二次检索阅读,探讨我国农村农业信息化研究热点与趋势。研究结果表明:农村农业信息化研究的发文量总体呈递增趋势,作者和机构比较分散,受到多学科共同关注;研究热点集中于农村农业信息化基础理论、农村农业信息技术发展与应用、农村农民教育信息化、农村农业信息化与产业结构优化等;未来趋向农村社区治理信息化、农村信息化与乡村产业兴旺、农村教育信息化功能挖掘等研究。 

[学术文献] Automated segmentation of soybean plants from 3D point cloud using machine learning 进入全文

Computers and Electronics in Agriculture

Image-based plant phenotyping has become a promising method for high-throughput measurement of plant traits in breeding programs. Plant geometric features that are essential for understanding plant growth can be obtained from the point cloud built using three-dimensional (3D) reconstruction of plant imagery data. A key task in the data processing pipeline is the automated and accurate segmentation of individual plants. Machine learning is a promising approach due to its strong ability in the extraction of details from images and has been successfully applied in plant leaf segmentation from two-dimensional (2D) images. The aim of this paper was to evaluate the performance of three machine learning methods, i.e. boosting, Support Vector Machine (SVM) and K-means clustering, inthe segmentation of non-overlapped and overlapped soybean plants at early growth stages using 3D point cloud. Images of 75 soybean plants at two growth stages in a greenhouse were collected using an image-based high-throughput phenotyping platform and were used to develop 3D point cloud using the Structure from Motion (SfM) method. Plant features including position (coordinate x, y, and z), and color (Red, Green, Blue, hue, saturation and Triangular Greenness Index) were used for background removal and the separation of non-overlapped plants. A Histogram of Oriented Gradient (HOG) descriptor was used for the separation of overlapped plants. The percentage of mismatched points between manual and automated segmentation was calculated and results showed that K-means clustering had the least mean error rates (0.36% and 0.20%) for the background removal and the non-overlapped plant separation. The least mean error rate for the separation of overlapped plants was 2.57% using SVM with labeled HOG descriptor. The developed image segmentation pipeline was evaluated in a case study where 69 plants at different growth stages were continuously monitored. Results showed that it took three minutes on average for completing all procedures in the pipeline and the extracted features (i.e. height and shooting area) were able to quantify the plant growth.  

[会议论文] Intelligent Agricultural Farming System using Internet of Things 进入全文

IEEE

In the present world, especially in our own country, farmers, if not rich, have a hard time adapting to the rising prices each day. Intelligent Agricultural Farming System instills on harnessing of Innovative Information Technologies as a driver of more effective, productive, and money-making agricultural organizations. These technologies must be thoughtfully combined to deliver meaningful information in near real-time.

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