A Machine Learning Approach for Crop Yield and Disease Prediction
  Integrating Soil Nutrition and Weather Factors
Forkan Uddin Ahmed Annesha Das Md Zubair
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.