## 5 Experimental Result and Discussion  
The proposed experimental plan was implemented on a prototype that was tested, as shown in Figure 7, considering two crops under various conditions. The three primary phases of experimentation were the creation of basic model-embedded systems for plant growth and feeding, the construction of a sensor net for intelligent greenhouse monitoring, and the automation of actuators. The proposed approach employed an embedded system to analyse greenhouse execution parameters such as CO2, soil moisture, temperature, and plant light, yielding accurate results. All the readings were observed under several conditions and monitored on a personal computer using serial communication, as shown in Figure 8. In addition, all these sensor data were monitored via the Internet on the Adafruit IO Cloud dashboard by publishing data from the nodes to the broker of Adafruit. Then, the user could subscribe to these data to access them in real-time, as defined in Figures 9 and 10. Figure 8: Sensor output on serial monitor with timestamp values. Figure 7: Experimental testbench. Figure 9: Sensor output on Adafruit IO Cloud IoT dashboard for Case 1. The greenhouse doors/windows could also be rolled up/down depending on the soil moisture conditions. The plant photosynthesis process requires higher CO\({}_{2}\) concentration and water level in the evenings than during the day; with these two sources of energy, the photosynthesis method keeps the plant cool and encourages rapid growth. Because the greenhouse absorbs CO\({}_{2}\) from day to night, it maintains a maximum CO\({}_{2}\) level at night; this was confirmed after completing a CO\({}_{2}\) concentration level experiment in a greenhouse, as illustrated in Figure 11. As a result, as seen in Figure 11, the CO\({}_{2}\) level decreased during the day. Because too much water can induce a fungal infection in the plant and too little water can cause the plant to grow dry or even die, the amount of water in the soil is critical. As a result, the amount of water required by the plant is crucial. Plants need more water with CO\({}_{2}\) during the night for photosynthesis. When the soil moisture sensor gave a negative value, the plants were entirely covered in water, as shown in Figure 11. The greenhouse windows/doors closed automatically using a DC motor. The positive value indicated that the soil was dry and had to be re-wetted, as shown in Figure 11. Temperature is one of the most critical parameters in a greenhouse; it should be kept as high as feasible. Blooming, fruiting, photosynthesis, seed germination, and other processes are all aided by warmth. Consequently, compared with the temperature and relative humidity range outside the greenhouse, the humidity and temperature range within the greenhouse were preserved to the greatest extent feasible. The numerous colours of sunshine enhance the photosynthetic system, which is among the systems used by plants and is essential for plant development, flowering, and shape. Consequently, in contrast with the average light penetration outside the greenhouse, a sustainable quantity of light penetration within the greenhouse was maintained. Furthermore, based on the sensor utilized for research and the technology used to design the system, our suggested model was far superior to those proposed in previous work performed by various authors, as described in Table 1.  
Figure 10: Sensor output on Adafruit IO Cloud IoT dashboard for Case 2. Figure 11: Sensor parameter representation relative to outside and inside the greenhouse for two crops, Gerbera and Broccoli. The classification of the analytics and decision-making model was conducted on a laptop with a 2.30 GHz Intel (R) Core (TM) CPU, 8 GB RAM, and Windows 10 (64 bit) operating system, with no other processes running in the background. MATLAB IDE was used to program the intelligent model, statistics, and machine learning toolbox from MATLAB. The RMSE performance metrics were evaluated to check the system's efficacy.