Analysis and Prediction of Soybean Plant Disease

Abstract

Disease detection of the plant is one of the
absorbing research areas in the farming field. This
field needs a dependable prediction methodology to
understand elements influencing disease. Plant
diseases become major threats for us because it harms
our body by reducing our immune system and also
degrade the quality of crops. As the production of
soybean is decreased due to natural hazard, overuse of
pesticides, soil problem, and lack of agricultural
knowledge and mainly due to diseases.
Analyzing data from different aspects and
summarizing it into valuable information is the
operation done in machine learning. It permits users to
analyses data from different dimensions, categorize
and the relationships are identified. WEKA is a data
analysis tool for machine learning classification.
Machine learning classification is a vital technique
with more applications in various fields. It is used to
classify each item in a set of data into one predefined
set of classes. This paper presents the analysis of
soybean plant diseases based on the dataset of plant
growth, seed germination, damaged area, external
decay, leafspot size, seed discolor, etc. The proposed
work focuses on machine learning techniques and
using the WEKA tool.