Abstract
A methodology was established for early, non-contact, and quantitative detection of plant water stress with machine vision extracted plant features. Top projected canopy area (TPCA) of the plants was extracted from plant images using image processing techniques. Water stress induced plant movement was decoupled from plant diurnal movement and plant growth using coefficient of variation of TPCA (COV TPCA) and was found to be effective for the water stress detection. Threshold value of COV TPCA as an indicator of water stress was determined by a parametric approach. The effectiveness of the sensing technique was evaluated against the timing of stress detection by a grower. Results of this study suggested that the objective water stress detection using projected canopy area based feature of the plants was feasible.
Original language | English (US) |
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Pages | 239-252 |
Number of pages | 14 |
State | Published - 2000 |
Externally published | Yes |
Event | 2000 ASAE Annual International Meeting, Technical Papers: Engineering Solutions for a New Century - Milwaukee, WI., United States Duration: Jul 9 2000 → Jul 12 2000 |
Other
Other | 2000 ASAE Annual International Meeting, Technical Papers: Engineering Solutions for a New Century |
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Country/Territory | United States |
City | Milwaukee, WI. |
Period | 7/9/00 → 7/12/00 |
Keywords
- Image processing
- Irrigation
- Machine vision
- Plant movement
- Water stress
ASJC Scopus subject areas
- General Engineering