The development of an intelligent agricultural decision-supporting system for
crop selection and disease forecasting in Bangladesh is the main objective of
this work. The economy of the nation depends heavily on agriculture. However,
choosing crops with better production rates and efficiently controlling crop
disease are obstacles that farmers have to face. These issues are addressed in
this research by utilizing machine learning methods and real-world datasets.
The recommended approach uses a variety of datasets on the production of crops,
soil conditions, agro-meteorological regions, crop disease, and meteorological
factors. These datasets offer insightful information on disease trends, soil
nutrition demand of crops, and agricultural production history. By
incorporating this knowledge, the model first recommends the list of primarily
selected crops based on the soil nutrition of a particular user location. Then
the predictions of meteorological variables like temperature, rainfall, and
humidity are made using SARIMAX models. These weather predictions are then used
to forecast the possibilities of diseases for the primary crops list by
utilizing the support vector classifier. Finally, the developed model makes use
of the decision tree regression model to forecast crop yield and provides a
final crop list along with associated possible disease forecast. Utilizing the
outcome of the model, farmers may choose the best productive crops as well as
prevent crop diseases and reduce output losses by taking preventive actions.
Consequently, planning and decision-making processes are supported and farmers
can predict possible crop yields. Overall, by offering a detailed decision
support system for crop selection and disease prediction, this work can play a
vital role in advancing agricultural practices in Bangladesh.

为了解决孟加拉国农业领域中作物选择和疾病预测的问题，本研究开发了一种智能农业决策支持系统。通过利用机器学习方法和实际数据集，该系统结合了作物生产、土壤条件、农业气象区域、作物疾病和气象因素的综合数据，为用户推荐作物列表并预测可能的疾病，并通过决策树回归模型预测作物产量，帮助农民选择高产作物和预防作物疾病，提高孟加拉国的农业实践水平。

机器学习在作物产量和疾病预测中的应用：结合土壤营养和天气因素

A Machine Learning Approach for Crop Yield and Disease Prediction  Integrating Soil Nutrition and Weather Factors

The past several years have witnessed a huge surge in the use of social media
platforms during mass convergence events such as health emergencies, natural or
human-induced disasters. These non-traditional data sources are becoming vital
for disease forecasts and surveillance when preparing for epidemic and pandemic
outbreaks. In this paper, we present GeoCoV19, a large-scale Twitter dataset
containing more than 524 million multilingual tweets posted over a period of 90
days since February 1, 2020. Moreover, we employ a gazetteer-based approach to
infer the geolocation of tweets. We postulate that this large-scale,
multilingual, geolocated social media data can empower the research communities
to evaluate how societies are collectively coping with this unprecedented
global crisis as well as to develop computational methods to address challenges
such as identifying fake news, understanding communities' knowledge gaps,
building disease forecast and surveillance models, among others.

该文介绍了 GeoCoV19—— 一个包含 524 百万条推文的大规模 Twitter 数据集，通过基于地名词典的方法来推断推文的地理位置，而这一大规模、多语言、地理定位的社交媒体数据可以帮助研究社区评估社会如何共同应对这一前所未有的全球危机，以及建立计算方法来应对如识别假新闻，理解社区的知识差距，建立疾病预测和监测模型等挑战。