Research On Weed Detection And Navigation Parameters Acquisition Of Pesticide Spraying Robot

The machine-vision-based research on weed detection and navigation parameter acquisition of pesticide spraying robot has a great significance both on fixed-point variable spraying of chemicals aiming to reduce the environmental pollution of chemical and on vision navigation of agricultural machinery to achieve the goal of precision agriculture. It is an inevitable trend of agricultural development in this century that intelligence agricultural machinery will be widely applied, resource will be replaced by technology, and precision agriculture will take effect.Basic research has been made on weed detection and location and navigation vision system of pesticide spraying robot on the basis of adequate summary about the advanced achievements at home and aboard. Aiming to realize the application of fixed-point variable spraying along navigation path, this paper build up the weed detection and location and navigation vision system of pesticide spraying robot based on the image processing technology, to achieve rapid and exact weed detection and accurate navigation parameter acquisition. The main contents and conclusions of the research are as follows:(1) Build up the weed detection and location and navigation vision system of pesticide spraying robot on the platform of tractor Foton European Leopard 4040. Develop system software under the Windows XP environment using C# objected-oriented language to collect the weed detection images and location and navigation images acquisition.(2) Combining the real-time demand, the trials on the basic image processing methods and the explorations on segmentation methods of plants and soil background, which aim at the application of weed detection and location and navigation, form the basis of dedicated special image processing methods of the system.(3) The accomplishment of the detection of wheat associated weed based on the 4-10-4 BP neural network takes the advantage of applying the morphological features of weed canopy and combining the shape features of weed blade. Average correct detection rate is 88.7% and the highest correct detection rate is 93.1%. Construct the decision binary tree by adopting the largest voting mechanism to extract and screen the shape and texture features of maize seeding and its associated weeds. The SVM-based decision binary tree achieved the inter-class and intra-class classification between monocotyledons and dicotyledons. Average correct detection rate is 91.4% and the highest correct detection rate is 95.5%. The experimental results indicate that the SVM classifier is superior to BP network classifier on detection rate and detection accuracy.(4) Present an acquisition method of actual navigation path data according to the navigation path information in the left and right images, which based on the camera on the left side, in order to avoid the problems of vast computation and the shortage in real-time of binocular stereo matching. Actually, it projects the measured data to the left image, and then implements navigation in terms of the path information on the left image.(5) After morphology operation on the area of dibbling crops, according to the centroid of the area of crops, achieve the navigation path recognition of dibbling crops using the improved Hough transform. DHT(double-Hough transform), which gives attention to both imaginary point detection and perspective, has been present to settle the issues that traditional Hough Transform fails to detect the centroid of drilling crop row, and achieves the navigation path recognition of drilling crops.(6) Divide the original image into three sub-images and then combine them by line spacing when the crop row features are unobvious, furthermore identify the navigation path by the combined images with obvious row features. Under the condition that crops possess no obvious row features, the above method can still accurately identify the navigation path. Present the horizontal scanning method to get the precise navigation path. Improve the horizontal scanning method by selecting the appropriated value of d and the Bezier curve fitting, which smooth the navigation path and improve the real-time simultaneously.(7) Set ROI window to recursively track navigation path and present the concept of state curvature, and introduce the principles of feedback and previewing to pesticide spraying robot guidance control. By combining the feedback navigation information with the current navigation information and the previewing navigation information, design the serial navigation path state BP network and guidance control parameters BP network, which achieves the pesticide spraying robot navigation parameters acquisition based on closed-loop control strategy. Experimental results show the biggest deviation between practical with network output value in lateral deviation is -7cm and mean value is -0.6cm and mean square deviation is 3.2cm. The biggest deviation between practical with network output value in steering angle is -3°and mean value is -0.45°and mean square deviation is 1.2°.There is a little deviation in lateral deviation and in steering angle, and high precision in navigation parameter acquisiton.