Nondestructive Measurement And Control Of Post-harvest Quality Of Broccoli

The kinetic models of post-harvest broccoli color changes were developed in this paper. And the new grading standard based on yellowness was established. The feature parameters of broccoli color and shape were extracted by computer vision and were graded by the statistical mode and artificial neural network. The insect pests (drosophila melanogaster) of post-harvest broccoli were controlled by the semi-conductor laser technique at the same time.1. According to the developed kinetic models of post-harvest broccoli color changes, the three color feature parameters (b~*, TCD, H~°) associated with artificial grading index were confirmed and their threshold in each class were given out respectively. The kinetic models showed that the rate constants of color parameters b~* and TCD were following a first-order Arrhenius-type reaction, and the polynomial model was suitable for the changes of a~* and H~°value. Meanwhile, on the basis of actual merchandise value changes of broccoli, the new grading standard was established with 4 classes based on the proportion of yellowness area.2. The feature parameters of 320 broccoli images were extracted and analyzed with computer vision analysis technique. The five feature parameters of color and shape (b~*, TCD, H~°, yellowness area proportion, roundness) were extracted from those images by the image analysis method, such as background purification, color segmentation, gray transform etc. The statistical mode (Multiple Linear Regression) was used to develop the model of grading based on those feature parameters, with undesired result at a accuracy of 56.9%. The automatic extraction and grading system based on the feature parameters of broccoli was developed by the program design in Visual C++ language.3. Five artificial neural networks (3-Layer Back Propagation Neural Network, Probabilistic Neural Network, Self-Organizing Competition Neural Network, Learning Vector Quantization Neural Network, Self-Organizing Feature Map Neural Network) were used as classifier in MATLAB7.0. The feature parameters of 320 images were used to train these networks and self-regression validity test, while that of 100 images were used for forecasting Validity test. The results showed that 4 kinds of classify neural networks (PNN, SOC, LVQ, SOM) and BP neural network were suitable for broccoli grading with the forecasting accuracy at the range of 68.2-93.4%. By contrast of these networks, BP neural network was the best network with the forecasting accuracy at 93.4%. PNN has certain application value since the difference of forecasting accuracy and self-regression validity between PNN and BP network were not great, and provided with the only 1/5 of running required time of BP.4. In order to study the control effects of the main insect pests (drosophila melanogaster) on post-harvest broccoli with the semi-conductor laser technique, the experiment designed with response surface investigated the biological effects of drosophila melanogaster for different laser power and irradiation time, such as dead. The results showed that the death rate was above 99% with the condition of laser power of 60 mW and irradiation time of 1282s in the wave-length of 650nm when the larva of drosophila melanogaster had been dealt with semi-conductor laser. While the weight of drosophila melanogaster was reduced, the required eclosion time was decreased. Therefore, the semi-conductor laser has strong biological effects to larva of drosophila melanogaster. However, when the power was below 40 mW, the laser light has the effect of promoting the growth of drosophila melanogaster. The experiment disclosed that drosophila melanogaster did not cause anti-laser effects on the third generation under the same experimental conditions in contrast to comparative groups.