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The potential loss of world crop production from the effect of pests, including weeds, animal pests, pathogens and viruses has been quantified as around 40%. In addition to the economic threat, plant diseases could have disastrous consequences for the environment. Accurate and timely disease detection requires the use of rapid and reliable techniques capable of identifying infected plants and providing the tools required to implement precision agriculture strategies. The combination of suitable remote sensing (RS) data and advanced analysis algorithms makes it possible to develop prescription maps for precision disease control. This chapter shows some case studies on the use of remote sensing technology in some of the world’s major crops; namely cotton, avocado and grapevines. In these case studies, RS has been applied to detect disease caused by fungi using different acquisition platforms at different scales, such as leaf-level hyperspectral data and canopy-level remote imagery taken from satellites, manned airplanes or helicopter, and UAVs. The results proved that remote sensing is useful, efficient and effective for identifying cotton root rot zones in cotton fields, laurel wilt-infested avocado trees and esca-affected vines, which would allow farmers to optimize inputs and field operations, resulting in reduced yield losses and increased profits. |
