An Intelligent Approach to Reducing Plant Disease and Enhancing Productivity Using Machine Learning

Abstract

Plant diseases are a normal part of the natural world, and they are one of the many ecological processes that work together to keep the vast number of living organisms in the world in a state of equilibrium with one another. Each plant cell has its own set of signalling pathways that help the plant fight off viruses, animals, and insects. Concerns have been raised about whether or not it is possible to use machine learning to make crop predictions based mostly on weather data. The goal of the research is to help users choose the right crop to grow so that they can maximise their yield and, as a result, the money they make from the project. In a rural area where almost half the people work in agriculture, one of the most important problems is when farmers can't use traditional or other non-scientific methods to choose a crop that will grow well in their soil. Researchers can't make use of case studies as well as they could because there isn't enough correct and up-to-date information available. With the resources at our disposal, we have proposed a model that makes use of random forests and the genetic algorithm. This model has the potential to solve this problem by providing predictive insights on the long-term viability of crops and recommendations based on machine-reading models that have been trained to take important environmental parameters into consideration..

Artificial Intelligence: A Promising Tool for Application in Phytopathology

“…In this context, AI and advanced machine learning algorithms emerge as potentially transformative tools in modern data-driven phytopathology. Machine learning models can analyze vast, disparate datasets, including weather, soil, plant omics, microbiome, and pathogen genomic information [42]. These models discern subtle multivariate relationships, predict disease outbreak risks, and enable targeted intervention strategies undetectable via conventional approaches [13,14,42].…”

“…Machine learning models can analyze vast, disparate datasets, including weather, soil, plant omics, microbiome, and pathogen genomic information [42]. These models discern subtle multivariate relationships, predict disease outbreak risks, and enable targeted intervention strategies undetectable via conventional approaches [13,14,42]. Continually learning from accumulating agricultural data streams, such AI-based systems progressively improve their predictive capabilities and decision support functionalities.…”