SPECTRAL IMAGING BASED NON-CONTACT DETECTION OF BIOTIC AND ABIOTIC STRESS IN BERRY CROPS

Zongmei Gao

State of Washington is the one of key producers of grapevine (ranked number 2) and blueberry (ranked number 1) crops in the U.S. Abiotic and biotic crop stressors cause series of morphological, physiological and biochemical changes to these berry crop plants. In abiotic crop stress management, growers face critical challenge to detect and manage viral diseases, such as grapevine leafroll disease (GLD) and is one of key hindrance for sustainability of the state’s grape and wine industry. Similarly, freeze damage to the blueberry buds is considered as the major hindering factor for its production in the central Washington. Conventionally, laboratory-based laborious and destructive methods are used for detecting of the GLD virus symptoms and blueberry buds freeze damage in respective berry crops. The critical need is thus to have rapid and nondestructive method for resolving such issues. Recently, hyperspectral imaging (HSI) has been explored to detect crop stressors as rapid, noncontact and often nondestructive method. Ideally, HSI data can help identify the most sensitive wavelengths which then can be used to build the miniaturized and portable optical sensing module for field level detection of crop stressors. The overall goal of this research was to find important spectral signatures for detecting the GLD symptoms in a red-fruited wine grape cultivar and freeze damage of the blueberry buds. Collectively, findings indicated that the individual feature wavelength was not sensitive sufficiently to detect the GLD infected samples, which was leading to find the combination of few wavelengths. Analytics aided in finalizing six salient common wavelengths (690, 715, 731, 1409, 1425 and 1582 nm) for identification of GLD infected leaves at early stage. Furthermore, the individual feature wavelength was not sensitive to detect the bud injury. The combination of nine salient common wavelengths (566, 599, 698, 715, 896, 1012, 1384, 1442 and 1599 nm) were found reliable to detect bud freeze injury levels (low, medium, and severe mortality). The results achieved in this study laid a foundation for future studies to develop customized multispectral sensor for identifying GLD-infected grapevines and freeze injured blueberry buds in field.

SPECTRAL IMAGING BASED NON-CONTACT DETECTION OF BIOTIC AND ABIOTIC STRESS IN BERRY CROPS

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