Advancements In Precision Agriculture: A Review on Image-Based Stress Detection and Predictive Yield Loss Modeling

Abstract

Precision agriculture utilizes cutting-edge technologies to maximize the management of crops, enhance utilization of resources, and maximize yield forecasting. This study critically analyzes 53 peer-reviewed articles to evaluate advancements in image-based stress detection and yield prediction techniques for precision agriculture. The review enumerates main issues, such as image-based analysis methods, different crop stress aspects (biotic, environmental, and abiotic), and remote sensing techniques for farm monitoring. The benefits of multispectral and hyperspectral imaging for crop health monitoring are also discussed, as well as the application of deep learning and machine learning in stress detection. Prediction models for yield loss, IoT and edge computing integration, and the application of optimization algorithms to enhance predictive performance are discussed. Challenges and limitations related to data processing, scalability of the model, and real-time deployment are also discussed. The research then discusses applicable tools and techniques, followed by extensively investigating the upcoming trends and opportunities. The discussion presents the possibility of integrating remote sensing, artificial intelligence-based analytics, and IoT technologies towards more accurate agriculture. The result is of great use to the researchers and practitioners who seek to develop efficient, scalable, and cost-effective systems for agricultural real-time monitoring and decision support.