Plant health monitoring with machine vision

Peter P. Ling; Terence P. Russell; Gene A. Giacomelli

doi:10.1117/12.198879

6 January 1995 Plant health monitoring with machine vision

Peter P. Ling, Terence P. Russell, Gene A. Giacomelli

Proceedings Volume 2345, Optics in Agriculture, Forestry, and Biological Processing; (1995) https://doi.org/10.1117/12.198879
Event: Photonics for Industrial Applications, 1994, Boston, MA, United States

Abstract

Spectral and dynamic morphological features were investigated for plant health monitoring using machine vision techniques. The plants were stressed by withholding all nutrient salts. The spectral reflectance of healthy and stressed lettuce leaves (Latuca sativa cv. `Ostinata') was measured to determine at which wavelength(s) a stressed condition would be apparent. The measured wavebands were between 400 and 1000 nm. A reference waveband was utilized to account for photometric variables such as lighting and surface geometry differences during image acquisition. The expansion of the top projected leaf area (TPLA) was found to be an effective feature to identify stressed plants. The nutrient stressed plant was identifiable within two days after nutrients were withheld from a healthy plant. This was determined by a clearly measurable reduction in TPLA expansion.

Citation Download Citation

Peter P. Ling, Terence P. Russell, and Gene A. Giacomelli "Plant health monitoring with machine vision", Proc. SPIE 2345, Optics in Agriculture, Forestry, and Biological Processing, (6 January 1995); https://doi.org/10.1117/12.198879

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