Browsing by Subject "growth chamber"

Crop monitoring in commercial indoor farming is a commonly used method in assessing the general productivity of the cultivated plants. This assessment practice is typically conducted manually by greenhouse workers and is sometimes supplemented by certain hand-held or stationary devices. An interesting example of novel device-assisted crop monitoring technologies utilizes digital imaging devices and computer-driven image analysis algorithms that have been prominently employed within the field of plant phenotyping. In the context of botanical studies, they have been used in e.g. characterizing various complex interactions between the genotypes of important food crops and their agronomic traits in specific prevailing environmental conditions. Additionally, image-based data acquisition technologies also present very interesting prospects for precision agriculture management practices. They could be harnessed to scan entire greenhouse compartments continuously and acquire massive amounts of data on multiple morphological and physiological aspects of crop growth and development in a non-destructive fashion. The acquired data could be implemented into mathematical greenhouse control models and utilized in a plethora of useful applications, including e.g. estimating and predicting biomass production and yield, detecting and localizing potential abiotic/biotic stress symptoms at an early stage, and ultimately enhancing overall crop production efficiency. In this thesis, these imaging technologies were explored in practice by designing and constructing a growth chamber embedded with automatic climate control and a low-cost multispectral imaging subsystem. The final assembly was tested by conducting a simple experiment involving drought-stressed sweet basil plants (Ocimum basilicum L. cv. ‘Genovese’) to determine how early drought-stress related symptoms could be detected purely from multispectral images. While the system carried out the tasks of automated climate control and continuous image capture adequately, the implemented approach in drought-stress detection was deemed unsuccessful. Significant differences between drought-stressed plants and their respective controls were not observed until visible symptoms were present. This was assumed to be due to incompatibility of the camera module’s spectral sensitivity in detecting changes in water content in plant tissue.