Titlebook: Computer and Computing Technologies in Agriculture XI; 11th IFIP WG 5.14 In Daoliang Li,Chunjiang Zhao Conference proceedings 2019 IFIP Int

deviate

Bph773

Design and Implementation of Water Spectrum Observation System for Aquaculture Pond,ometer is set as the standard value. The accuracy of the data measured by this system is above 98%. The result shows that, this system could take the place of spectrometer to measure the spectrum of specific bands. It can realize remote, real-time data observation.

Horizon

Going Beyond the Basics with Spry and Ajax,d other real-time data acquisition, transmission, processing, and text messages warning and quinoa disease quinoa products full traceability, etc. This project research has completed a preliminary test in the experimental base, basic functions required to complete the project, can achieve the goal of the quinoa products traceability.

特別容易碎

寵愛

Storing User Records in a Database, disease pressure across the region. In addition, the temporal dispersal of YR can also be delineated by the model. Through a comparison with some classic methods, the Bayesian network outperformed BP neutral network and FLDA in accuracy, which thereby suggested a great potential of Bayesian network in disease forecasting at a regional scale.

Bombast

柳樹；枯黃

carbohydrate

receptors

Study of Machine Learning Based Rice Breeding Decision Support Methods and Technologies,and patterns, and spectral biclustering algorithm can find required varieties with their local features follow certain patterns. The system can help breeders find useful knowledge and patterns more quickly, and improves the accuracy and efficiency of crop breeding.

不來

A Bayesian Network Model for Yellow Rust Forecasting in Winter Wheat, disease pressure across the region. In addition, the temporal dispersal of YR can also be delineated by the model. Through a comparison with some classic methods, the Bayesian network outperformed BP neutral network and FLDA in accuracy, which thereby suggested a great potential of Bayesian network in disease forecasting at a regional scale.