Togari, Y. (1940) Damages to Rice Crops Caused by a Wind Storm, with Special Reference to a Typhoon That Visited Kansai District, Japan. Part I. Local Differences in the Damage Caused and a Discussion on the Cause of Damage from Both the Meteorological and Geographical Points of View. Japanese Journal of Crop Science, 12, 250-264.
https://doi.org/10.1626/jcs.12.250

has been cited by the following article:

• TITLE: Real-Time Crop Prediction Based on Soil Fertility and Weather Forecast Using IoT and a Machine Learning Algorithm

  AUTHORS: Anne Marie Chana, Bernabé Batchakui, Boris Bam Nges

  KEYWORDS: Smart Farming, Crop Selection, Recommendation of Crops, IoT, Machine Learning, Weather Forecast

  JOURNAL NAME: Agricultural Sciences, Vol.14 No.5, May 25, 2023

  ABSTRACT: The aim of this article is to assist farmers in making better crop selection decisions based on soil fertility and weather forecast through the use of IoT and AI (smart farming). To accomplish this, a prototype was developed capable of predicting the best suitable crop for a specific plot of land based on soil fertility and making recommendations based on weather forecast. Random Forest machine learning algorithm was used and trained with Jupyter in the Anaconda framework to achieve an accuracy of about 99%. Based on this process, IoT with the Message Queuing Telemetry Transport (MQTT) protocol, a machine learning algorithm, based on Random Forest, and weather forecast API for crop prediction and recommendations were used. The prototype accepts nitrogen, phosphorus, potassium, humidity, temperature and pH as input parameters from the IoT sensors, as well as the weather API for data forecasting. The approach was tested in a suburban area of Yaounde (Cameroon). Taking into account future meteorological parameters (rainfall, wind and temperature) in this project produced better recommendations and therefore better crop selection. All necessary results can be accessed from anywhere and at any time using the IoT system via a web browser.