CropShield: A Literature Review on Price Prediction and Disease Detection Techniques for Efficient Smart Pesticide Advisory Systems

Abstract

CropShield represents a cutting-edge solution in smart agriculture, leveraging advanced technologies to optimize pesticide management through price prediction and disease detection. This literature review explores the state-of- the-art methodologies and techniques used in CropShield for efficient pest control advisory. The paper focuses on two key aspects: the use of machine learning models and predictive analytics for price forecasting, which aids farmers in making informed decisions regarding pesticide purchases, and the application of computer vision and AI-driven disease detection systems for early identification of crop diseases. By synthesizing existing research, the review highlights the integration of these technologies into a cohesive framework for precision agriculture, aiming to reduce pesticide misuse, lower environmental impact, and improve crop yield. Additionally, challenges and opportunities in the application of these techniques, including data accuracy, model robustness, and system scalability, are discussed. This paper serves as a comprehensive resource for researchers and practitioners seeking to advance smart pesticide advisory systems through innovative price prediction and disease detection strategies.