Control And Monitoring Systems Of A Smart Hybrid-Powered Integrated Soil-Less Farming Facility With A Fuzzy Logic-Based Decision Support Module For Crop Planning To Enhance Agricultural Sustainability

Abstract

This paper describes the design and operation of the following subsystems as an enhancement to the existing aquaponics greenhouse on the UTAS-Al Musanna campus in order to continually promote agricultural sustainability: 1) an aeroponics system that allows crops to grow using air mist, transforming the greenhouse into a fully integrated soil-less farming facility; 2) IoT-based smart control and monitoring systems that use Particle Photon microcontrollers connected with sensing devices and actuators to reduce operating costs and improve farm owners' access to all water quality and environmental parameters; 3) a 2.8KW micro-hybrid renewable energy (wind and solar) conversion system for continuous and stable power supply, 4) a plant health monitoring system using a Raspberry PI microcontroller interfaced with PI camera, and 5) a fuzzy logic-based crop planning decision support module implemented in MATLAB. The Particle Photon microcontrollers read and analyzed water quality indicators in the recirculating aquaculture tanks and hydroponic beds, as well as environmental parameters in the greenhouse and aeroponics grow chambers. The IoT-based microcontroller systems activated the necessary actuators when sensor readings fell outside of acceptable ranges for effective nitrification, oxidation, and photosynthesis processes. The events and data from the systems were published in the Thing Speak cloud platform for real-time monitoring and displayed in the SEAAGSAO mobile app created with MIT App Inventor. In addition, the Raspberry PI-based plant health monitoring system classified the crop's health status with 94% accuracy and sent a supervisory warning signal to the Thing Speak cloud and mobile app to alert the farmer of the condition. Furthermore, the Fuzzy logic-based decision support module for crop planning (FL-DSSCP) was able to forecast vegetable crops that are suited for cultivation and will provide a high yield for a certain season.