# A Total Crop-Diagnosis Platform Based on Deep Learning Models in a Natural Nutrient Environment
## 1 Introduction  
A recent report by UN's Intergovernmental Panel on Climate Change (IPCG) says that there will be a number of effects of climate change on agriculture. Climate change can also bring about environmental consequences, such as changes to seasonal events in the life cycle of plants and animals [1]. In addition, Food and Agriculture Organization (FAO) predicts that agricultural output should rise by more than 70% by 2050. To produce high-quality food and feed a growing world population with a given amount of arable land in a sustainable manner, we must develop new methods of sustainable farming that increase yield while minimizing chemical inputs such as fertilizers, herbicides, and pesticides [2]. In order to increase food production efficiently, farms have been becoming more like factories. Agricultural farmers use the technology of the Internet of Things (IoT) and artificial intelligence (AI) to remotely manipulate agricultural machines such as drones and tractors and to forecast the yields. The agriculture farmers prepare for sudden climate changes (e.g., flood, storm, drought, etc.) and control the state of the farm. Farms using this technology are called \"smart farms\" [3]. In the smart farm, ongoing developments in information and communication technology (ICT) offer significant potential to manage information at the farm level. Sensing technologies, at least in principle, offer farmers the ability to monitor their farms with an unprecedented level of detail, in a multiplicity of dimensions and in near real time. This offers an intriguing possibility of developing farm-specific models that the individual farmer can use to plan their activities in response to changing circumstances, thus enabling the exploration of the various trade-offs inherent in any decision-making process whilst managing the information overload problem [4]. In order to prepare for the rapid changes in the environment, a sensor has been developed through recent studies that can confirm the situation of the farm in real time and inform farmers of the state of the farm [5]. Agricultural machines autonomously manage the smart farm by analyzing the collected sensor data. Another study of smart farms is as follows. In reference [6], they show the effectiveness of the combined use of numerical weather predictions and hydrological modeling to forecast soil moisture and crop water requirements in order to optimize irrigation scheduling. Recently, a number of studies on smart farms diagnosing specific elements necessary to a farm by using sensors or transferring the big data collected from sensors to an agriculture machine and analyzing the farm status in the machine were proposed. However, contrary to these, this paper focuses on collecting the big data from sensors, transferring the collected sensor data to a cloud server and diagnosing the crop status by using deep learning in the cloud server. Because an existing smart farm diagnoses only a specific area of a farm or prepares agricultural robots by function according to the environment of farms and the kinds of crops, it costs a lot to use and manage them. To solve these problems, this paper proposes a total crop-diagnosis platform (TCP) which collects the weather information based on a farm's location information, analyzes the collected weather information and the crop soil sensor data with a deep learning technique and notifies a farm manager of the fully analyzed result. The TCP based on a cloud server does not depend on the environment of farms and the kinds of crops. Since the TCP is not required to buy and manage various agricultural robots, contrary to an existing farm, the cost is drastically reduced. If the environment of farms and the kinds of crops are changed, the TCP has only to customize its software.