| Fruit industry is efficient in planting industry. The growth of fruit trees is closely related to their growth environment and asscociated with orchard management. Real-time accessing to environmental information and the nutritional status of fruit trees could offer an approach to orchard management. Wireless sensor networks could be used to monitor environmental information and nutritional status of fruit trees, avoiding the wiring in the orchard. The nutritional content of fruit trees could be monitored by spectroscopy, avoiding the time comsuption of chemical diagnosis. The hyperspectral image could obtain the spatial and spectral information at the same time, thus could be used to detect the bruise on apples. The main content of the thesis are as follows:[1] Orchard information acquisition based on wireless sensor networksThe orchard information acquisition system was developed based on the wireless sensor networks to gather the air temperature and humidity, soil temperature, soil moisture, and vegetation index, etc. The system consisted of three parts:(1) PDA integrated with a ZigBee coordinator, GPS module, GPRS module, and RF1D module;(2) Environmental information acquisition nodes based on ZigBee, to gather the air temperature and humidity, soil temperature, soil moisture, etc.;(3) Crop growth status detection device based on ZigBee. The PDA was mainly used to collect the GPS information, RFID information, the ZigBee sensor node information, and then bind the orchard information (such as the soil moisture) and the GPS information to upload to the remote PC. The environmental information acquisition nodes were manily used to turn on the sensor power supply and gather the sensor information when receiving the gather command from the PDA. The crop growth status detection device was used to gather8-channel light intensity information, which could be processed to vegetation index later.Hardware design included the integration of PDA and the design of the environmental information acquisition nodes. Software design included the programming for PDA, the custom communication protocols, the programming for ZigBee coordinator and the Zigbee router, and the modification of the existing crop growth status detection device.The system test consisted of the test for the wireless performance, the test for the temperature sensor, and the application test. The application test showed that the system could get the realtime orchad information, save and then upload the data.[2] Nutrient content monitoring of apple leaves and jujube leaves using reflectanceThe chlorophyll content and nitrogen content of apple leaves and the nitrogen content of jujube leaf were measureed. The effects of different preprocessing methods on the models were discussed. Partial least squares regression (PLSR) through the full wavebands and characteristic wavebands PLSR and support vector machine (SVM) were built for a prediction of apple leaf chlorophyll content, apple leaf nitrogen content, and jujube leaves nitrogen content. Preprocessing methods such as sample set partitioning methods, outlier identification and elimination methods, noise elimination methods, and wavelet and wavelet packet noise denoising methods were discussed. Sample set partitioning methods such as random sampling method (RS), the content gradient method (CGM), Kennard-Stone Algorithm (KS), Duplex algorithm, sample set partitioning based on joint x-y distances algorithm (SPXY) were discussed. The SPXY partition method and KS method had the highest prediction accuracy. Identification and elimination methods such as principal component score map, boxplot, leverage versus squared residual plot, and the Monte Carlo cross-validation method were discussed. Except for the principal component score map, the other three methods could reduce the root mean square error of cross-validation (RMSECV). Preprocessing methods such as normalization, Savitzky-Golay smoothing (SG), multiplicative scatter correction (MSC), variable standardization correction (SNV), detrending, and first order derivative (1st derivative) were discussed. Normalization is the best one among all these six preprocessing methods Wavelet and wavelet packet de-noising were discussed, including the wavelet basis function, decomposition layers, and the default threshold. Considering the RMSECV and operation steps, db4wavelet5layer decomposition with default global threshold was chosen.Therefore, in order to improve the prediction accuracy, the following steps could be adopted:(1) Outlier detection by leverage versus squared residual plot or the Monte Carlo cross-validation method;(2) Normalization;(3) db4wavelet5layer decomposition with default global threshold;(4) KS or SPXY method to partition the sample set.The chlorophyll content and nitrogen content of apple leaf in different time were measured. SPXY algorithm was used to partition sample set. Wavelet denoising, normalization, direct orthogonal signal correction (DOSC), continuous projection algorithm (SPA) were used as the preprocessing methods for the full waveband PLSR, the selected wavebands PLSR, and the SVM modeling.For predicting apple leaf chlorophyll content, DOSC-SPA-PLSR modeling was the best modeling methods in its growing stages. In sprouting period, the Rc, Rp, RMSEC, RMSEP, RPD were0.9980,0.9991,0.9704,0.7238, and18.1841, respectively. During the same period, the Rc, RP, and RPD for normalized full waveband PLSR modeling were0.5345,0.6705, and1.1665, respectively.For predicting apple leaf nitrogen content, the full waveband PLSR modeling and SPA-PLSR modeling got better results in sprouting period and flower bud differentiation period. In sprouting period, Rc, Rp, RPD were0.9968,0.9969,11.1241, respectively. During the same period, Rc, RP, RPD for normalized full waveband PLSR modeling were0.7420,0.5177,1.0433, respectively.For predicting jujube leaf nitrogen content, DOSC-SPA-PLSR modeling reduced the input variables and the number of latent variables (LV). Meanwhile, DOSC-SPA-PLSR modeling reduced RMSEP, and improved the prediction accuracy.[3] Detection of apple bruises caused by picking with the manipulator based on hyperspectral imagesDifferent forces were applied on apples by a manipulator, and then hyperspectral images were taken after different storing days. As the Red Fuji apples were bi-color with red and yellow, the red side and yellow side of the apples were chosen to be the contacted area with the manipulator. Hyperspectral images (Hypercube data size600×1004×881) were processed in MATLAB. Average gray values of pixels selected from the contacted region and non-contacted region on both red and yellow side were extracted. They were then calibrated to obtain the reflectance respectively. The reflectance from hyperspectral images of the apples applied with different forces at the same time was compared. The reflectance from different storing time was also compared to observe changes over time after the forces applied by the manipulator. The standard deviation of the average gray value in contacted area and non-contacted area was calculated. The peak and valley wavelengths were seletcted. Then monochrome images at these wavelengths were selected to combine by ratio and difference operator to detect the apple bruices. |
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