Field Monitoring Using IoT: A Neural Network Approach

A healthy mind requires healthy body and healthy body needs healthy food. Therefore, to provide healthy food to the rapidly growing population of a country is a challenge within the limited fertile land. To fulfill the healthy food demand requires the high production of yield. To achieve high production, it is essential for the farmers to monitor the field from time to time for a good yield, as a little miss can cause disaster and the entire efforts could go waste. The manual monitoring of the field is quite tough and expensive. Therefore, in this paper Internet of Things (IoT) applications are addressed to monitor the field. An Arduino microcontroller board with the soil moisture, temperature, and humidity sensors is used to collect the data from the remote field on the fly. The data once received is analyzed by applying cascade forward and function fitting neural network. Further, the data is tested against an already trained dataset of the field in normal conditions collected over a period of 1 year. The test data, when applied to the trained data, provides a dataset which is used for analysis of the ideal condition of the field. In case of alarming changes in the field properties, a signal is generated and the farmer is informed to take necessary action. A preventive action can be taken to save the crop and also maintain the productivity of the field.