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
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.
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.
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.