## 3 Results
Figure 17: Test program and program output. Figure 19: General algorithm of the system. Figure 18: Deep learning test algorithm. More than one algorithm was used in the study. Especially in automatic control, this was very important for the system to function by itself. The greenhouse air conditioning algorithm was designed to provide the ideal living environment for the plant. Figure 20 shows the indoor air conditioning algorithm. The work carried out covers the entire process from the planting of the strawberry seedling to the end of the crop yield. During this process, the detection and treatment of diseases that may occur in the strawberry plants were carried out. For this reason, diseases that originated from five different strawberry plants were detected. At the same time, the determination of the three different life stages of the strawberry plant was carried out. This study was carried out while growing strawberry plants in the greenhouse. One of the most important differences between this study and other studies in the literature is that this study was carried out in a real greenhouse, not in a laboratory environment. All procedures were performed while the strawberry plant was growing. For this, strawberry planting was carried out in a 16 m\({}^{2}\) greenhouse. Following this planting, every stage of the strawberry plant was photographed. The dataset was created from these photographs. This dataset was used by nine different deep learning architectures. The  
Figure 20: Automatic greenhouse air conditioning algorithm. same dataset was used by each architecture. The architectures used, accuracy rates, and other data are shown in Table 1.  
Nine different algorithms were used in the study. Of the algorithms used, the ResNet101 architecture gave the highest accuracy rate. ResNet101 architecture had 99.80% accuracy, while a normal person had an error rate of 5-10%. An error rate that is less than human error was determined in this study. All algorithms were loaded with the same database. Although the photos in the database were the same, their sizes differed. Some algorithms used 224 \(\times\) 224 photos, while others required 227 \(\times\) 227 or 229 \(\times\) 229 photos. For the training times, the highest training time was the SqueezeNet architecture, with 981 min. The lowest training time was GoogleNet, with 24 min. GoogleNet had the lowest training time but the highest error rate. The same computer was used for all training periods of the algorithms. Thus, the error rate arising from the system was minimized. Accuracy rates were not directly proportional to the training times. Thus, the parameters and depth of the algorithm were effective. Although SqueezeNet had the highest training time, the accuracy rate remained at 91.31%. Its 1.24 million parameters and 18 depths were far below those of the ResNet101 architecture. This study was carried out to increase the productivity of strawberry plants. With this in mind, a disease that can occur in the growth phase of the strawberry plant was determined. With this determination, the necessary chemical or chemical mixture was given to the plant and recovery was achieved as soon as possible. At the same time, three life stages of the plant--seedling, blooming, and crop--were determined. Between these stages of the strawberry plant, the climate needs to be adjusted. The sooner that the indoor air conditioning becomes suitable for the stage of the plant, the more that the efficiency will increase. In Figure 21, the efficiency of the system irrigated with the normal and the hybrid system is compared. In addition to the outdoor temperature and humidity values, soil moisture was also measured in the irrigation system. Since the soil moisture was very low in the first seedling planting, the water yield was less. Afterwards, the water yield increased a lot. In this study, the strawberry plant grew very quickly. A total of 5652 g of crop was obtained in the plants grown with deep learning. A total of 5150 g of strawberry crop was obtained in the plants grown normally.