PRECISION AGRICULTURE: UTILIZING REMOTE SENSING AND IOT FOR OPTIMIZED CROP MANAGEMENT AND YIELD PREDICTION

Abstract

This study investigates the integration of remote sensing and Internet of Things (IoT) technologies to enhance crop management and improve yield prediction in precision agriculture. The research was conducted across multiple agricultural sites with diverse crop types, utilizing high-resolution satellite imagery, drones equipped with multispectral sensors, and IoT sensors to collect real-time data on soil moisture, temperature, and crop health indicators. The yield prediction model, developed using machine learning algorithms, demonstrated high accuracy, with prediction errors as low as 0.1 tons/ha for certain crops. The integration of remote sensing and IoT data significantly improved the monitoring of crop health and facilitated timely decision-making on irrigation, fertilization, and pest management. Studies showed precision agriculture technology implementation brought profitable returns on investment because it boosted rice and corn yields by 15%. The high beginning costs were offset by enduring resource efficiency advantages combined with raised productivity which turned the technology financially suitable for every agricultural operation. This research shows that precision farming strategies achieve agricultural productivity gains through their dual ability to enhance farming output levels and cut environmental consequences. The broad implementation of this technology is hindered by difficulties in infrastructure and high costs and technology accessibility issues. The research confirms remote sensing and IoT's combined power for creating sustainable farming operations that enhance efficiency.