# A Total Crop-Diagnosis Platform Based on Deep Learning Models in a Natural Nutrient Environment
## 2 Related Works
### A Design of a Crop Self-Diagnosis Module (CSM) Based on Deep Learning
#### 3.4.2 A Design of the TEDS
When the number of the training samples is 2000, the diagnosis success rate is 92.10%. In summary, the success rate of TEDS was higher than the CNN by an average of 5% and the LSTM by an average of 7%. For the CNN, when the number of samples is small, the difference between the TEDS and the CNN is about 2%. When the number of samples is 2000, the difference between the TEDS and the CNN is about 9%. For the LSTM, as the number of samples gets larger, the success rate is increased, but the success rate of the LSTM is much lower than that of the TEDS. In summary, in the first experiment of learning time, the TEDS is slower than the CNN by 0.27 and faster than the LSTM by 0.43 s. In the second experiment of success rate, the TEDS is higher than the CNN by an average of 5% and the LSTM by an average of 7%. The TEDS is slower than the CNN in learning speed, but as data gets increased, the success rate of the TEDS gets higher. Therefore, the TEDS is the best neural network model when the state of a farm is judged. ## 5 Conclusions

Because an existing smart farm has to diagnose only a specific area of a farm or prepare agricultural robots by function according to the environment of farms and the kinds of crops, they are expensive to use and to manage. To solve these problems, this paper proposes a total crop-diagnosis platform (TCP) based on deep learning models in natural nutrient environment which collects the weather information based on a farm's location information, diagnoses the collected weather information and the crop soil sensor data with a deep learning technique, and notifies a farm manager of the diagnosed result. The TCP based on a cloud server does not depend on the environment of farms and the kinds of crops. Because the TCP does not need to buy and manage various agricultural robots, contrary to an existing farm, this cuts costs significantly. If the environment of farms and the kinds of crops are changed, the TCP has only to customize its software. In addition, the TCP based on deep learning interconnects the communication protocols of various sensors, solves the maximum data size that sensor can transfer, predicts in advance crop disease occurrence in the external environment, and provides important information to the farmers for managing their crops. The proposed TCP has some expected effects, as follows. First, the OFSG interconnects the various communication protocols of sensors and solves the limitation of data size that sensors can transfer. Second, because the OFSG uses the compression and fragmentation applied to the headers, the number of the fragmentation times gets smaller and the processing speed gets higher. Third, because the APDS uses a Sigmoid function as an activation function in analysis process, it is better than a tanh function by about 7% in accuracy. Fourth, the TEDS is faster than the LSTM by 0.43 in learning time and higher than the LSTM by about 7% in success rate. As the data size gets bigger, the success rate of the TEDS gets more stable. However, the TCP also has the following uncertainty and limitations. First, it processed the experiment by using the past weather data which our weather center provides and the past soil data which our neighboring farms provides. Second, because a deep learning model designer has much difficulty in establishing a proper threshold value by analyzing past weather and soil data, it takes much time to find a proper threshold value during the initial design process. In the future, if researchers including us can obtain and process experiments with real time weather and soil sensor data, more accurate and better results can be obtained in the experiments. In addition, researches will have to search for how to reduce the time to find a proper threshold value. **Author Contributions:** [PERSON] proposed the idea. [PERSON], [PERSON] and [PERSON] made the paper based on the idea and carried out the experiments in Korean Language. [PERSON] guided the research and translated the paper into English. All authors read and approved the final manuscript.