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

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

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 databa...

Ausführliche Beschreibung

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Bibliographische Detailangaben
Hauptverfasser: WANG ZHIBO, YANG CHEN, GUAN ZHENGCHAO, LU YUHENG, GUO HUAWEI, XIAO QIANG
Format: Patent
Sprache:chi ; eng
Schlagworte:
CALCULATING
COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
COMPUTING
COUNTING
DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FORADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORYOR FORECASTING PURPOSES
HANDLING RECORD CARRIERS
PHYSICS
PRESENTATION OF DATA
RECOGNITION OF DATA
RECORD CARRIERS
SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE,COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTINGPURPOSES, NOT OTHERWISE PROVIDED FOR
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Beschreibung
Zusammenfassung: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