The AC PASS (Analysis, Comparison, Prediction, Alert, Symptoms, and Solutions) algorithm is an innovative data analysis tool designed for crop health monitoring using hyperspectral and multispectral imagery. This research project focuses on enhancing precision in detecting and managing crop health issues by integrating advanced analytical techniques. The algorithm processes and analyzes remote sensing imagery to extract detailed spectral information, compares current data with historical benchmarks, predicts future crop health scenarios, and generates automated alerts for early intervention. Additionally, it identifies symptoms of biotic and abiotic stress and provides actionable solutions for mitigation and treatment. AC PASS represents a significant advancement in agricultural technology, offering a scalable, real-time solution for improving crop health monitoring and decision-making.
AC PASS delivers a precise and comprehensive approach to crop health monitoring, improving early detection and management of stress factors. Its predictive capabilities enhance future planning, while automated alerts enable timely interventions. The algorithm’s integration of remote sensing technologies ensures robust and scalable solutions, optimizing decision-making and intervention strategies in agriculture.
Detailed spectral analysis for accurate crop condition identification.
Forecasts future crop health based on detected patterns.
Automated alerts for early intervention and immediate action.
Provides solutions and recommendations for effective mitigation.
