GPS (Global Positioning System) navigation in agriculture is facing many challenges, such as weak signals in orchards and the high cost for small plots of farmland. With the reduction of camera cost and the emergence of excellent visual algorithms, visual navigation can solve the above problems. Visual navigation is a navigation technology that uses cameras to sense environmental information as the basis of an aircraft flight. It is mainly divided into five parts: Image acquisition, landmark recognition, route planning, flight control, and obstacle avoidance. Here, landmarks are plant canopy, buildings, mountains, and rivers, with unique geographical characteristics in a place. During visual navigation, landmark location and route tracking are key links. When there are significant color-differences (for example, the differences among red, green, and blue) between a landmark and the background, the landmark can be recognized based on classical visual algorithms. However, in the case of non-significant color-differences (for example, the differences between dark green and vivid green) between a landmark and the background, there are no robust and high-precision methods for landmark identification. In view of the above problem, visual navigation in a maize field is studied. First, the block recognition method based on fine-tuned Inception-V3 is developed; then, the maize canopy landmark is recognized based on the above method; finally, local navigation lines are extracted from the landmarks based on the maize canopy grayscale gradient law. The results show that the accuracy is 0.9501. When the block number is 256, the block recognition method achieves the best segmentation. The average segmentation quality is 0.87, and time is 0.251 s. This study suggests that stable visual semantic navigation can be achieved under the near color background. It will be an important reference for the navigation of plant protection UAV (Unmanned Aerial Vehicle).
It is a crucial step locating the maize plant or even its target location precisely during the intelligent agricultural equipment working in farmland. Therefore, the segmentation of plants from the background image is one of the important research contents of agricultural machine vision. Under the background of significant color difference, the current method can effectively complete maize canopy segmentation and plant location identification. However, under the background of no-significant color difference, there is no robust and high-precision method for maize canopy segmentation and plant location identification. In this study, it was found that the grayscale of maize canopy had gradient distribution trend along the radial direction. The Hue Saturation Value color space and Support Vector Machine method was used to segment 600 maize canopy images, then the polynomial regression method was used to find out the functional relationship between grayscale gradient and canopy diameter. The functional relationship gave identification results of canopy central region under different gray gradient distribution. The result provided a theoretical basis for accurate identification and rapid location of maize plant center at seedling stage, and provided accurate position coordinate and yaw information for field navigation of agricultural intelligent equipment such as plant protection UAV.
The wavenumbers combination selection of near infrared spectroscopy (NIRS) analysis was very important for improving model prediction effect, reducing model complexity and designing special NIRS instruments with high signal noise ratio. Based on the prediction effect of single wavenumber linear regression model, a wavenumbers combination selection method of NIRS analysis of glucose in human serum was developed. 25 wavenumbers with good prediction effect were selected. All wavenumber combinations of the twenty-five wavenumbers were used to establish multiple linear regression (MLR) models respectively. According to the prediction effect, the optimal MLR model was the eleven wavenumbers combination of 7340, 7328, 7311, 7253, 7251, 7234, 7228, 7220, 7218, 7207, 7203 (cm -1 ), the corresponding root mean squared error of predication (RMSEP) was 0.384 mmol/L. And the prediction effect was obvious better than one of partial least squares (PLS) model. These indicated that the wavenumbers combination selection method based on the prediction effect of single wavenumber linear regression model could be applied to the NIRS analysis and could provide valuable reference for designing minitype special NIRS instruments.
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