Non-contact multispectral and thermal sensing techniques for detecting leaf surface wetness

Leaf surface wetness detection is important in plant production for pesticide application evaluation, disease management, and misting control. Efficient application of pesticides may be possible using the feedback from the leaf wetness detection system reducing both the overall input cost and environmental contamination. The goal of this study was to develop non-contact sensing techniques for leaf surface wetness detection. Several non-contacting techniques using spectral, thermal, and imaging sensors were evaluated for the development of an automated feedback controlled spraying system. The study was divided into several sub studies focusing on leaf level and canopy level experiments inside a laboratory under artificial illumination, and canopy level experiments in a greenhouse under natural solar illumination.; Leaf wetness was assessed using the relative differences in the spectral and thermal measurements recorded before and after spraying. Leaf surface wetness was quantified as the difference between the average equivalent water thickness (EWT) values of sprayed and non-sprayed canopy. The EWT values were calculated using model inversion techniques from the measured multispectral reflectance. Leaf and canopy temperatures were measured using infrared thermometry. The studies on both the leaf and canopy levels indicated that the multispectral reflectance and infrared thermometry techniques were able to differentiate plants with and without surface wetness. It was found that the multispectral sensors could be used to detect leaf surface wetness resulting from a high volume pesticide application. The temperatures of the canopies without surface water were found to be 4.4--5.5°C higher than that of the canopies with surface water.; In the canopy level studies under solar illumination, the feasibility of using the developed sensing methodology to detect leaf surface wetness was evaluated in a greenhouse. A non-contact sensor array consisting of spectral, temperature, and imaging sensors was constructed and mounted on a commercial irrigation boom in the greenhouse. Algorithms were developed to compensate for outdoor lighting variation and background interference on the reflectance measurements of the vegetation. Spectral ratioing techniques were used to differentiate canopies with different surface moisture conditions. The irrigation boom had capabilities to be controlled locally using a handheld controller and also remotely from a personal computer. An onboard computer was used to collect data from the sensor array, process the information, and make spraying decisions. (Abstract shortened by UMI.)...