Digital Insights into Plant Health: Exploring Vegetation Indices Through Computer Vision

The earth’s vegetation plays a pivotal role in the ecosystem equilibrium and serves as an environmental health indicator. Monitoring vegetation is essential for informed agriculture, resource management, ecological understanding, and environmental tracking. Remote sensing data offers valuable insights into plant life, benefiting biodiversity, agriculture, forestry, and urban green systems. In agriculture, this data provides an unbiased foundation for yield management and crop production prediction. Vegetation indices (VIs) are vital for assessing vegetation health, growth, and physiological conditions. They mitigate atmospheric interference and are widely used in agriculture to monitor plant health, estimate crop yields, and study vegetation dynamics, including chlorophyll content estimation. Current techniques such as handheld spectrometers and satellite imagery are effective but limited. Handheld spectrometers require time-consuming field measurements, restricting spatial coverage. Satellite methods face spatial resolution, cloud interference, cost, and real-time insight challenges. Recent advancements in computer vision, driven by machine learning, offer transformative potential. Computer vision can process real-time data from drone imagery and automated accurate VI measurements. This integration opens avenues for precision agriculture and environmental monitoring. The chapter explores the synergy between VI assessment and computer vision, delving into technical aspects, application, challenges, and future opportunities. It envisions a promising future for vegetation assessment through computer vision and remote sensing integration.