A REVIEW ANALYSIS OF REMOTE SENSING AND MACHINE LEARNING APPLICATIONS IN HUANGLONGBING DETECTION

   Huanglongbing (HLB), also known as citrus greening disease, poses a significant threat to global citrus production. Traditional detection methods, such as visual inspections and PCR-based testing, have limitations in terms of time, cost, and scalability. Recent advancements in remote sensing technologies and artificial intelligence (AI) techniques have shown promise in providing early, non-destructive detection of HLB. This study reviews the latest applications of remote sensing and AI in HLB detection, addressing challenges like environmental variability and high computational demands. Innovative methods, including fluorescence imaging, laser-induced breakdown spectroscopy (LIBS), and chemiresistor sensors, are explored for their potential to improve detection accuracy. The study emphasizes the need for further research to refine these techniques, including the use of new spectral bands and indices. The integration of remote sensing and AI represents a comprehensive approach to mitigating HLB's impact, with the potential to reduce economic losses and enhance the health of citrus plantations worldwide.