Advanced Machine Learning Framework for Efficient Plant Disease Prediction

TL;DR

This paper presents an integrated machine learning framework combining deep learning and NLP techniques to assist farmers in diagnosing plant diseases and ranking solutions through a social media-based communication platform, demonstrating high accuracy on benchmark data.

Contribution

It introduces a novel combination of deep learning and NLP for plant disease diagnosis and solution ranking within a social media communication system for farmers.

Findings

High accuracy in disease identification from images

Effective ranking of community-posted solutions

Robustness to concept drift in solution effectiveness

Abstract

Recently, Machine Learning (ML) methods are built-in as an important component in many smart agriculture platforms. In this paper, we explore the new combination of advanced ML methods for creating a smart agriculture platform where farmers could reach out for assistance from the public, or a closed circle of experts. Specifically, we focus on an easy way to assist the farmers in understanding plant diseases where the farmers can get help to solve the issues from the members of the community. The proposed system utilizes deep learning techniques for identifying the disease of the plant from the affected image, which acts as an initial identifier. Further, Natural Language Processing techniques are employed for ranking the solutions posted by the user community. In this paper, a message channel is built on top of Twitter, a popular social media platform to establish proper communication among farmers. Since the effect of the solutions can differ based on various other parameters, we extend the use of the concept drift approach and come up with a good solution and propose it to the farmer. We tested the proposed framework on the benchmark dataset, and it produces accurate and reliable results.