Agriculture is one of the most important markets in the world. For the agriculture production efficiency and cost reduction, the modern agriculture no longer exists in farm fields only, but expands quickly in information fields as well. The recent trend of agriculture is moving towards precision farming, which gives rise to great demands for IT supports. The future of precision agriculture is considered highly promising, and lots of solution packages will be developed to support farming activities during the entire farming cycle.
Abnormal detection using cameras in UAV platform become more and more popular for operation and maintenance, in particularly for large-scale constructions like building, bridge etc. UAV-used detection system could be expected to reduce the cost, ensure the safety and provide stability for O&M on infrastructures. As imaging technology, Image registration and change detection method plays a central role in an abnormal detection system. Two key factors in this respect are needed to be improved. Firstly, due to the near-distance photographing and complex surface composition of structures, a robust plane-level matching method is significant to make high-precision image registration for the change detection. However, as many part of the surface of structures do not have enough feature points, it seems difficult to make a plane matching using homography transformation based on the correspondence feature points. Secondly, plane-level change detection have much noise in the border area because of homography transfer deviation and information redundancy. In order to solve these two problems, a robust method based on a combination of edge detection and geometry constraint is proposed to make plane-level registration and change detection noise reduction. For registration, making good use of pixel information in the border area, we expand the border area to extract each plane regardless of the number of feature points. And for noise reduction, we excise the border information to reduce the effect of information redundancy. Validation experiments were performed with several sets of image pairs. We succeed to extract planes in images with a 92% coverage and 91% precision while the number of noise is reduced as 30% as before for average. The evaluation shows that our proposed method is of high precision with high robustness for abnormal detection system.
Unmanned aerial vehicles (UAVs) are being used to reduce the cost and risk of facility inspections. For the power distribution companies, power line inspection for providing stable power supply is an important but costly task. It includes deterioration diagnosis, detection of foreign matter adhesion, and estimation of power line-tree conflict risk, all of which is currently performed visually on foot. In this study, we explore the methods of detection and visualization of a power line-tree conflict using aerial images taken by drones. To detect a power line-tree conflict, we should firstly recognize the power lines and trees in the aerial images in order to identify the “candidate” regions of the conflict, and secondly, estimate the actual positional relationship between them in 3D. However, as previous studies have shown, the detection of power lines in an image is a challenging task because they are very narrow and monochromatic, which results in difficulty in extracting features. This specific character of the power lines could also cause failure in 3D reconstruction, in which feature matching among images is necessary. Here, we show that convolutional neural networks (CNNs) can be effectively applied in recognition of power lines and trees in an image. We also found that in mapping the candidate region of conflict to a 3D model the power line position could be estimated by taking the pole height into account. This way, even if it is difficult to reconstruct the power line in 3D, a user can make the final decision about the conflict by checking the depth and/or the height directional relationship.
Sugarcane, as one of the most mainstay crop in Brazil, plays an essential role in ethanol production. To monitor sugarcane crop growth and predict sugarcane sucrose content, remote sensing technology plays an essential role while accurate and timely crop growth information is significant, in particularly for large scale farming. We focused on the issues of sugarcane sucrose content estimation using time-series satellite image. Firstly, we calculated the spectral features and vegetation indices to make them be correspondence to the sucrose accumulation biological mechanism. Secondly, we improved the statistical regression model considering more other factors. The evaluation was performed and we got precision of 90% which is about 20% higher than the conventional method. The validation results showed that prediction accuracy using our sugarcane growth modeling and improved mix model is satisfied.
In modern agriculture, remote sensing technology plays an essential role in monitoring crop growth and crop yield
prediction. To monitor crop growth and predict crop yield, accurate and timely crop growth information is significant, in
particularly for large scale farming. As the high cost and low data availability of high-resolution satellite images such as
RapidEye, we focus on the time-series low resolution satellite imagery. In this research, NDVI curve, which was
retrieved from satellite images of MODIS 8-days 250m surface reflectance, was applied to monitor soybean's yield.
Conventional model and vegetation index for yield prediction has problems on describing the growth basic processes
affecting yield component formation. In our research, a novel method is developed to well model the Crop Growth
Dynamics (CGD) and generate CGD index to describe the soybean's yield component formation. We analyze the
standard growth stage of soybean and to model the growth process, we have two key calculate process. The first is
normalization of the NDVI-curve coordinate and division of the crop growth based on the standard development stages
using EAT (Effective accumulated temperature).The second is modeling the biological growth on each development
stage through analyzing the factors of yield component formation. The evaluation was performed through the soybean
yield prediction using the CGD Index in the growth stage when the whole dataset for modeling is available and we got
precision of 88.5% which is about 10% higher than the conventional method. The validation results showed that
prediction accuracy using our CGD modeling is satisfied and can be applied in practice of large scale soybean yield
Self-calibration is a fundamental technology used to estimate the relative posture of the cameras for environment recognition in unmanned system. We focused on the issue of recognition accuracy decrease caused by the vibration of platform and conducted this research to achieve on-line self-calibration using feature point's registration and robust estimation of fundamental matrix. Three key factors in this respect are needed to be improved. Firstly, the feature mismatching exists resulting in the decrease of estimation accuracy of relative posture. The second, the conventional estimation method cannot satisfy both the estimation speed and calibration accuracy at the same tame. The third, some system intrinsic noises also lead greatly to the deviation of estimation results. In order to improve the calibration accuracy, estimation speed and system robustness for the practical implementation, we discuss and analyze the algorithms to make improvements on the stereo camera system to achieve on-line self-calibration. Based on the epipolar geometry and 3D images parallax, two geometry constraints are proposed to make the corresponding feature points search performed in a small search-range resulting in the improvement of matching accuracy and searching speed. Then, two conventional estimation algorithms are analyzed and evaluated for estimation accuracy and robustness. The third, Rigorous posture calculation method is proposed with consideration of the relative posture deviation of each separated parts in the stereo camera system. Validation experiments were performed with the stereo camera mounted on the Pen-Tilt Unit for accurate rotation control and the evaluation shows that our proposed method is fast and of high accuracy with high robustness for on-line self-calibration algorithm. Thus, as the main contribution, we proposed methods to solve the on-line self-calibration fast and accurately, envision the possibility for practical implementation on unmanned system as well as other environment recognition systems.
It’s essential but challenging to retrieve spectral features as detailed as possible in current satellite imagery industry. In
this research, based on the physical model of sensor response function, we present a method to recover the reflective
spectrum at the front end of sensor in an iterative way and to greatly enhance the spectral details of satellite imagery. Our
method is able to largely increase the cost-performance ratio of current satellite multispectral imagery and also reveals
great potentials of satellite imagery in various disciplines.