Machine Learning Strategies for Predicting Crop Diseases

Abstract

Prevalence of crop diseases is a major hindrance for successful crop production. These diseases can be identified in less time and more accurate using Machine Learning (ML) strategies as compared to any manual approach. Agronomy plays a key role in anticipating crop diseases at an early stage. With the advent of computer vision, plants can be classified as diseased or healthy by extracting architectural characteristics of a leaf using various image processing techniques. Support Vector Machines (SVM) classification technique is used in distinguishing between diseased and healthy leaf from the datasets that are publicly available. SVM method exhibited high fitting and predictive precision. The proposed paper is organized in various steps such as identifying the features, extraction of features using a computer vision technique known as Scale Invariant Feature Transform (SIFT), model training and testing. Predominantly, crop diseases on a larger scale are predicted by harmonizing speed and accuracy using computer vision and machine learning strategies.

Predictive Study on the Occurrence of Wheat Blossom Midges Based on Gene Expression Programming with Support Vector Machines

“…This approach addresses the limitations of traditional methods by providing more accurate and scientific predictions. SVM's ability to distinguish between diseased and healthy leaves with high fitting and predictive precision has been well-documented, making it a valuable tool in crop disease diagnosis [9]. Furthermore, SVM has been extensively used for disease classification, demonstrating its versatility and effectiveness in different agricultural contexts [10].…”