Machine learning models for early detection of pest infestation in crops

Abstract

Pest infestations pose a significant threat to agricultural productivity and food security, necessitating timely and accurate detection methods. This study investigates the efficacy of various machine-learning models in the early detection of pest infestations in crops. We employ a comparative approach, analyzing the performance of models such as decision trees, random forests, support vector machines (SVM), convolutional neural networks (CNN), and long short-term memory (LSTM) networks. The dataset utilized includes multispectral images and environmental parameters collected from diverse agricultural settings, ensuring a robust evaluation across different crop types and growth stages. Each model’s performance is assessed based on accuracy, precision, recall, and F1-score, with additional consideration given to computational efficiency and scalability. Our findings reveal that deep-learning models, particularly CNNs and LSTMs, demonstrate superior accuracy in detecting early signs of pest infestation compared to traditional machine-learning algorithms. However, trade-offs in computational requirements and implementation complexity are discussed. The study also explores the potential integration of these models into real-time monitoring systems, offering practical insights for farmers and agronomists aiming to adopt advanced technological solutions in pest management. This research underscores the transformative potential of machine learning in enhancing agricultural resilience and productivity, paving the way for future innovations in precision agriculture.

Topics & Concepts