Active and Passive Electro-Optical Sensors for Health Assessment in Food Crops

Area of Science:

• Agricultural Science
• Remote Sensing Technology
• Plant Pathology

Background:

• Early detection of plant stresses is crucial for preventing crop yield losses in agriculture.
• Remote sensing offers non-destructive, spatialized methods for crop health monitoring and disease quantification.
• Advancements in sensor technologies are driving novel techniques for precision agriculture.

Purpose of the Study:

• To review key sensor characteristics, platform integration, and data analysis techniques in precision agriculture for plant stress detection.
• To highlight the benefits and challenges of emerging remote sensing technologies like hyperspectral imaging and Light Detection and Ranging (LIDAR).
• To explore the potential of multi-sensor systems and data fusion with artificial intelligence for accurate plant disease detection.

Main Methods:

• Review of recent advancements in multispectral imaging, hyperspectral imaging, and Light Detection and Ranging (LIDAR) technologies.
• Examination of multi-sensor systems and data fusion techniques, including blending LIDAR with electro-optical sensors.
• Analysis of artificial intelligence techniques for processing large-scale, spatially and temporally distributed data from various sensor platforms.

Main Results:

• Light Detection and Ranging (LIDAR) offers advantages in data flexibility, delivery rate, and automation, overcoming limitations of passive remote sensing systems.
• Multi-sensor systems combined with data fusion and artificial intelligence significantly increase accuracy in plant disease detection.
• Various platforms (handheld, ground-based, airborne, robotic, satellite) enable diverse applications of electro-optical sensors for plant stress prediction.

Conclusions:

• Remote sensing, especially LIDAR and integrated multi-sensor approaches, will define the future of plant stress detection, yield estimation, and quality assessment in precision agriculture.
• The adoption of artificial intelligence and data fusion techniques is critical for leveraging big data in plant health monitoring.
• Further research into sensor characteristics, platform integration, and data analysis is essential for advancing precision agriculture and addressing plant stress effectively.