Author(s): Ambarish G. Mohapatra,Bright Keswani,Saroj Kumar Lenka

Improving farm productivity is essential for increasing farm profitability and fulfilling the rapidly growing demand for food that is fuelled by fast population growth throughout the world. Farm productivity can be raised by understanding and forecasting crop performance in many different environmental conditions. Crop recommendation is presently based on data gathered in field-based agricultural research that capture crop performance under a variety of ailments (e.g., soil quality and environmental conditions). The standard of manually collected crop performance data is extremely low, since it doesn't take into account earlier conditions that have not been observed by the individual operators. Emerging Internet of Things (IoT) technologies, such as IoT devices (e.g., wireless sensor networks, network-connected weather stations, cameras, and smart telephones) can be used to collate vast number of harvest and environmental performance data, ranging from time series data from detectors, to spatial data from cameras, to individual observations accumulated and recorded via cellular smart phone software. Such data can then be analyzed to filter out invalid data and compute personalized crop recommendations for any particular farm. In this report, the IoT-based study that may automate the selection of ecological, soil, fertilization, and irrigation information; mechanically correlate such data and filter-out invalid data from the view of assessing crop operation; and compute crop predictions and personalized harvest recommendations for any particular farm are introduced.