Tea plant disease and insect pest predicting and forecasting method based on intelligent recognition of crowd source data

Abstract

The invention belongs to the field of tea plant disease and insect pest prediction, and particularly relates to a tea plant disease and insect pest predicting and forecasting method based on the intelligent recognition of the crowd source data. The method comprises the steps of establishing a database of a tea plant disease and insect pest recognition system; collecting and processing the modeling data of the tea plant disease and insect pest recognition system; building a convolutional neural network model; training the convolutional neural network model; testing a recognition result; developing a tea plant disease and insect pest recognition software; establishing a tea plant disease and insect pest prediction platform; and displaying a prediction result. The method has the characteristics of high timeliness, high research and development efficiency, convenience in use, visualized result and the like, is an iterative upgrade of a traditional tea plant disease and insect pest prediction method, and provides the technical support for the green prevention and control of a tea garden.

Description

technical field [0001] The invention belongs to the field of tea tree disease and insect pest forecasting, and in particular relates to a tea tree disease and insect pest forecasting method based on intelligent identification of crowd-source data. Background technique [0002] Tea is one of the three major beverages in the world, and its unique drinking flavor and unique health care functions have been loved by more and more people. At the same time, tea originated in China, and it is also a window for Chinese traditional culture to be displayed to the outside world. As a dual-core carrier of products and culture, tea also puts forward higher requirements in terms of food safety. At present, my country's tea certified products have pollution-free, green, organic and other products. The quality and safety risks of these products mainly come from the prevention and control of pests and diseases in the management and maintenance of tea gardens. Pest identification and predict...

AI Technical Summary

Problems solved by technology

The accuracy of prediction results has a great relationship with the contribution value of parameter factors and modeling parameters. In addition, based on the characteristics of insects' own environmental adaptability and the complexity of modeling factors, the prediction results are often not very unsatisfactory.

At the same time, each research and development process can only target a certain kind of pests and diseases, and the research and development efficiency needs to be improved

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