## 3 Results  
The study which is carried out here covers the complete production process. This process is 45 days from planting the strawberry plant to the crop time. In this study, 70% of the data were used for training and 30% of the data were used for testing. Figure 12 shows the training process of the AlexNet architecture used in the system. AlexNet is one of the first architectures used in convolutional neural networks. In the AlexNet architecture, a photo with dimensions of 227 \(\times\) 227 is used. The depth parameter is 6 million. The rate of error in the AlexNet architecture varies between 15.4% and 26.2%. The main reason for the high level of change in this rate is the dataset. The more similar the photos which made make up the dataset are, the lower the error rate is. In this study, a trial plant was created to minimize the rate of error. Using photographs from the planting seedling of the strawberry plant planting to the end of the crop stage in the trial planting, a  
Figure 11: Control, sensor, graphic, and strawberry status interface display of mobile application. dataset was created. Because of this, the accuracy rate was 95.74% and the rate of error was 4.26%. The detection error rate of a normal person varies between 5 and 10%; the AlexNet architecture reached a lower error rate than a normal person. Figure 13 shows the training process of the VGGNet architecture used in the system. In VGGNet, the depth is 19 and there are 144 million parameters. VGGNet is greater than AlexNet in both depth and parameters. This is reflected in the rate of accuracy. The accuracy rate of VGGNet was 97.70%, approximately 2% higher than the accuracy rate of AlexNet. The training time in VGGNet was 961 min. Buddha was the longest training period. Figure 14 shows the training process of the GoogleNet architecture used in the system. GoogleNet took had a training period of 24 min. The accuracy rate was the lowest, with 68.74%. It had a much higher error rate than normal people. Figure 15 shows the training process of the ResNet architecture used in the system. This study was performed in the program MATLAB. A picture was taken with the webcam and processed at 10:00 every morning. It worked with ResNet101 architecture in MATLAB for every process. Training was done with ResNet101 architecture. Testing was done with training files. The test program and program output are shown in Figure 17.  
This study was carried out in the real-life environment of the plant. The photo of the plant was assessed at 10:00 every morning. The size of the captured image was first reduced. ResNet101 architecture, which gave the highest accuracy rate in the study, uses 224 \(\times\) 224 photos as an input. After the size of the captured photo was reduced, the test process was carried out in the ResNet101 architecture. After the test process was finished, the result was obtained and the captured picture was shown on the computer screen. There was a USB connection between MATLAB and the microcontroller. The code for the microcontroller was written in MATLAB. After the test process was finished, the result was sent to the microcontroller. Figure 18 shows the test algorithm. Figure 14: GoogleNet training progress. Figure 15: ResNet101 training progress. In this study, Canbus communication protocol was used for the communication of internal sensor, external sensor, irrigation, humidification, air conditioning, and the control part of the interface. Thanks to Canbus, communication was provided over long distances with less cables. All systems were connected via a single line with Canbus. Each system was given an ID number. Thus, the required system ID number can be easily found. In this study, 50 Kbit/sec was the preferred communication speed. In this way, data transmission was provided up to 1 km away. Figure 19 shows the general algorithm of the system. Figure 16: (a) ROC graph of AlexNet architecture. (b) ROC graph of GoogleNet architecture. (c) ROC graph of VGG19 Net architecture. (d) ROC graph of the ResNet101 architecture.