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– ABSTRACT
This work presents a machine learning model, in the form of a Jupyter
Notebook.The business logic in Python uses Hybrid ensemble Learning techniques in
order to predict the disease present in the plant or in its leaves. The proposed system
will integrate the data obtained from clicking 2k photos and by applying machine
learning algorithms and a prediction of most suitable crops according to current
environmental conditions is made. This provides young farmers with a variety of options
of crops that can be cultivated. Machine learning is concerned with the development of
computer programs that can access data and learn on their own. Plant Disease
prediction solves one of precision agriculture’s most difficult issues, and several models
have been proposed and validated so far. Since plant diseases are affected by a variety
of factors such as climate, weather, soil, fertilizer usage, and seed variety, this issue
necessitates the use of multiple datasets. This suggests that predicting plant disease is
not a simple task; rather, it entails a series of complex steps. Farmers are the core of the
agricultural system. Agriculture is an important part of a country’s development because,
as everyone knows, a country’s gross domestic product must be equitable. Farmers are
critical to the agricultural system’s success because crops must be planted and
processed for it to work. A prototype for a real-time plant disease prediction algorithm in
Python using Hybrid Machine Learning and Data Analytics was created to assist these
farmers. Machine learning is a subset of artificial intelligence (AI) that enables
computers to learn and evolve without being explicitly programmed..
