Utilizing SMART space technology in determining planting sustainability and soil moisture stress / Harold R. Lucero

Dissertation (Doctor in Information Technology) --AMA University, October 2023.

This study aimed to utilize smart space technologies in determining
planting sustainability and soil moisture stress that will optimize loT and data
mining technique to help in achieving the government's goal of empowering and
strengthening the nation's agricultural sector. The study employed the combined
experimental, developmental, and quantitative research approach towards
achieving the objectives of the study. In order to gather abiotic and edaphic data,
including soil moisture, light, temperature, humidity, pH, and NPK level, the study
constructed an loT prototype. The researcher employed LoRa technology in
transmitting collected data into the loT gateway before uploading it to Firebase
Realtime Database. The study also involved the development of a mobile
application using Blynk loT Framework and web interface for remote monitoring
and control of irrigation and UV lighting system. Comparative analysis was
conducted between Random Forest, KNN, and Naive Bayes Algorithm in
predicting planting sustainability based on available data. Based on the
calculated Kappa of 0.9901, Random Forest demonstrated the highest level of
accuracy with a "Almost Perfect" strength of agreement. Random forest was
implemented using the RubixML library to enable the web interface to perform
predictions of Planting Sustainability on data stored in the database. In the user
evaluation test based on 1SO2501 O conducted by the researcher, the overall
weighted mean for all criteria is 4.19, with an "Agree (A)" interpretation. This
indicates that the developed system is of excellent quality, excelling in
functionality, dependability, usability, efficiency, maintainability, and portability.