| Now, there are a lot of problems about orchard planting and management technology, such as labor-intensity, excessive pesticide spraying and low fruit quality and so on. Using machine vision technology to study fruit quality rating, target spraying and fruit picking robot, is significant to improve orchard automation technology, and reduce labor intensity, grab farming, reduce losses, promote high fruit quality. In summary, based on international research, this paper studies pre-harvest cherry quality detection methods, suckers real-time identification and location, and apple detection and location methods in space:1,Research on color rating for cherry fruits under natural lighting conditions. The changing mathematical model of cherry skin color is established firstly in the same light conditions. Based on the model a color classifier is established to solve the problem that cherry skin color affected by environmental color temperature. Validation test have been conducted under different lighting conditions (5700K in orchard, and 5400K,6500K in lab). The results showed that the method could automatically grade cherry samples into seven pre-defined color levels under varying lighting conditions. Using rating results obtained by an experienced horticulturist on same samples as bench marks, this developed computer rating method resulted in an acceptable accuracy level of 87% match.2,Using an active light source to conduct accurate color rating of sweet cherries in an outdoor environment. It used a camera flash to reduce the effects of two major obstacles in outdoor color rating:(1) inconsistent ambient light and (2) specular reflections on cherry skins. An image processing algorithm was developed to classify cheery colors into seven levels. Tests showed that the overall rating accuracy was 93% in three different outdoor lighting conditions(4800K,5700K, 7700K). The results validated the feasibility of using computer vision to realize accurate color rating of sweet cherries in an outdoor environment.3,Research on the measurement methods of cherry fruit width, including oval fitting, circle fitting and rotation searching three methods. Using cherry width results obtained by a ruler on same samples as bench marks, the results showed that the oval fitting method is better than others, the standard deviation (std) is 0.48mm, the coefficient of determination (R2) is 0.822.4,Developed the sucker location and recognition system based on a laser range finder and a color CCD camera. The camera mathematical model and self-calibration methods are studied. The space measurement and calibration method are developed firstly based on laser and camera. A real-time image processing algorithm was developed to detect and locate sucker based on sucker's color, texture and position. The results of field trials showed that this method could locate and recognize suckers effectively. The measurement accuracy of sucker size is 80%, the identification accuracy of trunk is 98%.5,In order to auto pick apple, an intelligent vision system was design and implemented. A single and binocular stereo vision system was developed firstly, the system consists of a binocular vision system with infrared and color vision sensor and a single vision system to measure close object. A calibration with two vision system was done to ensure that the information can transmit each other accurately. Using three-dimensional reconstruction techniques, an image processing algorithm was developed to detect the apple, based on the color, depth shape and location. Validation test show that the accuracy of identification is 83.3%, the depth's Mean Square Error (MSE) is 0.049m; when the distance is about 2.4 m from sensor to object, the accuracy is 100%, MSE is 2.3cm when the distance is about 1.5m. The results validated the feasibility of an intelligent vision system to realize accurate detection and location.
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