Automated Machine Learning Classification Framework to Predict Crop Yield and Detect Pest Patterns

Abstract

Plant disease identification is crucial to food security and agricultural product availability. Traditional disease diagnosis can be tedious, annoying, and inaccurate. The investigation examines how modern machine learning algorithms might improve plant disease diagnostics for efficacy and precision. Despite this, machine learning faces many obstacles, including model training, processing costs, and rising demand for large data sets. This study proposes a novel method called Automated Machine Learning Classification Framework (AMLCF) to predict crop yield and detect pest patterns. This framework simplifies model selection, hyperparameter adjustment, and feature engineering for non-experts. The amount of time and computational resources needed have additionally been greatly reduced. The suggested AMLCF is evaluated on different unique agricultural datasets to validate its plant disease detection versatility. Our extensive simulation analysis found that AMLCF exceeds existing machine learning methods in speed, accuracy, and usability. AMLCF's detailed demonstration shows this; besides predicting plant illnesses, this system can predict crop yield and detect pests. Those findings suggest AMLCF could transform farming. Better plant health monitoring, early disease identification, and farmer selection could be achieved. The experimental results show that the proposed AMLCF model increases the accuracy ratio by 92.6%, computational efficiency analysis by 97.4%, versatility analysis by 98.3%, user accessibility ratio by 99.1%, and crop health tracking analysis by 94.8% compared to other existing models.