Rice pest and disease clasiification using ML

Keywords: Rice pest,classification,machine learning,algorithms

Background

Rice, a crucial staple crop, faces significant threats from pests and diseases impacting its yield and quality. Advancements in machine learning (ML) techniques have enabled researchers and farmers to develop predictive models and tools for effective rice pest management.

Aim & Objectives

Aim
The aim of this study is to utilize ML algorithms for rice pest and disease prediction. The study aims to identifying patterns and relationships to predict pest infestations and recognize disease symptoms in rice plants by utilizing historical data and different factors
Objective
1. Develop ML models to predict pest occurrences in rice based on environmental factors like temperature, humidity, and rainfall.
2. Train ML models to recognize disease symptoms in rice plants by analyzing images of affected leaves and other plant parts

Research Methodology

The study uses ML algorithms to analyze large datasets of pest values and environmental factors, identifying correlations between variables. ML models are trained to predict pest infestations based on environmental conditions. Additionally, ML models are trained on labeled data of diseased rice plants to accurately recognize diseases. Various ML models are implemented with different algorithms, and the best performing model is selected based on accuracy score for effective rice pest and disease prediction

Software & Tools

Jupyter, Google Collab

— Preview truncated. The complete document (full chapters, references, diagrams, and appendices) is shared with clients as part of project delivery. —