Development of a remote data acquisition system for real-time monitoring of some elemental variations in nutrient solutions in hydroponic farming using machine learning, and a Yb/CIGS photodetector for measuring light intensity affecting plant growth

In this Ph.D. dissertation, a remote data acquisition system was developed to detect real-time changes in Mg, P, and K elements, designed for use by students and academics conducting studies in hydroponic agriculture at universities, as well as for potential commercial applications. Additionally, a Yb/CIGS thin-film photosensor was produced to measure light intensity, which plays a crucial role in plant development. During the development phase of the system that can remotely read element changes, a total of 300 nutrient solution samples were prepared, and their ICP-OES and EC measurements were used to create a dataset. This dataset was expanded using artificial neural networks in a Python interface and uploaded to the cloud. A remote EC meter capable of communicating with the cloud and comparing the conductivity values of randomly prepared solutions was developed. The experimental success rate of the system, based on 10 different samples, was found to be 81.68%. In the production of the photosensor, Mo was used as the bottom contact, CIGS as the photoactive layer, and Yb as the top contact. The structural, electrical, and surface properties of the CIGS-based photodiode were examined, and the detector performance was tested under sunlight and infrared light. The measurements showed that the Yb/CIGS photosensor is suitable for detecting light intensity in both the visible and near-infrared regions.