Study on Innovation System and Innovation Strategy of Chinese Agricultural Product Processing Industry

The agricultural product processing industry is the inevitable choice for the agriculture to realize the industrialization, integration and modernization. Although the agricultural product processing industry has become the bright point in Chinese economy development, the whole development level falls behind the developed countries. The thesis brings up that the inherent reasons that Chinese agricultural product processing industry falls behind is that Chinese agricultural product processing industry has not an integrated industrial innovation system and has not a proper innovation strategy. So this thesis deeply discusses how to construct innovation system of Chinese agricultural product processing industry and puts forward the innovation strategy in order to improve the technology innovation capability and the development level.

Recognition and Classification of Pomegranate Leaves Diseases by Image Processing and Machine Learning Techniques

Disease recognition in plants is one of the essential problems in agricultural image processing.This article focuses on designing a framework that can recognize and classify diseases on pomegranate plants exactly.The framework utilizes image processing techniques such as image acquisition,image resizing,image enhancement,image segmentation,ROI extraction(region of interest),and feature extraction.An image dataset related to pomegranate leaf disease is utilized to implement the framework,divided into a training set and a test set.In the implementation process,techniques such as image enhancement and image segmentation are primarily used for identifying ROI and features.An image classification will then be implemented by combining a supervised learning model with a support vector machine.The proposed framework is developed based on MATLAB with a graphical user interface.According to the experimental results,the proposed framework can achieve 98.39%accuracy for classifying diseased and healthy leaves.Moreover,the framework can achieve an accuracy of 98.07%for classifying diseases on pomegranate leaves.

Classification and recognition scheme for vegetable pests based on the BOF-SVM model

The current detection technology for vegetable pests mainly relies on artificial statistics,which exists many shortages such as requiring a large amount of labor,low efficiency,feedback delay and artificial faults.By rapid detection and image processing technology targeting at vegetable pests,not only can reduce manpower and pesticide use,but also provide decision support for precise spraying and improve the quality of vegetables.Practical research achievements are still relatively lacking on the rapid identification technology based on image processing technology in vegetable pests.Given the above background,this paper presents a classification and recognition scheme based on the bag-of-words model and support vector machine(BOF-SVM)on four important southern vegetable pests including Whiteflies,Phyllotreta Striolata,Plutella Xylostella and Thrips.This paper consists of four sub-algorithms.The first sub-algorithm is to compute the character description of pest images based on scale-invariant feature transformation.The second sub-algorithm is to compute the visual vocabulary based on bag of features.The third sub-algorithm is to compute the classifier of pests based on support vector machines.The last one is to classify the pest images using the classifier.In this study,C++and Python language were used as implementation technologies with OpenCV and LibSVM function library based on BOF-SVM classification algorithm.Experiments showed that the average recognition accuracy was 91.56%for a single image category judgment with 80 images from the real environment,and the average time was 0.39 seconds.This algorithm has achieved the ideal operating speed and precision.It can provide decision support for UAV precise spraying,and also has good application prospect in agriculture.

Studies of ultrasonic dehydration efficiency

The aim of this investigation was to define the effectiveness of non-contact drying using ultrasonic vibrations. Disk radiators were used for carrying out experiments, and a special drying chamber was designed to provide resonant amplification of ultrasonic vibrations (from 130 to 150 dB). Drying of ginseng and other vegetables demonstrated that the application of ultrasonic vibrations reduced power inputs by 20% in comparison with convective drying. It also led to a decrease of 6% in final moisture content, if the duration of drying was constant. The level of intensification of ultrasonic drying was high (up to 50 g for 1 kg of drying material), which helped to lower the temperature of the drying agent and improve the quality of the dried products.