Abstract: State of the art systems used for monitoring of land (for example, agricultural land), fail to accurately assess various conditions in the land area and make predictions. The disclosure herein generally relates to agricultural systems, and, more particularly, to a method and system for micro-climate management in a land area being monitored. The system groups the different plots based on sensor trend information and based on a determined homogeneity information. A micro-climate view of the land area is accordingly generated, which in turn is used to generate micro-climate predictions.

Abstract: This disclosure relates generally to a system and method for monitoring performance of low-cost sensors plied in a field for soil moisture measurement. The low-cost sensors are calibrated to give useful derived parameters to support farming such as volumetric water content (VWC) of the soil. Further, the steps are being incorporated to de-noise their response to derive stable measurements similar to expensive rugged sensors. The calibration of the low-cost sensor and normalization of incoming values from the low-cost sensor are based on values determined through rugged sensors for soil moisture measurement. The normalization involves finding a minimum value and maximum value of soil moisture. Performance of the low-cost sensors are analyzed based on a range of values of the soil moisture. Finally, the performance analysis provides degradation stages and based on the degradation stages evaluated recommendations to modify the sensor are shared with the user.

Abstract: This disclosure relates to precision agriculture that relies on monitoring micro-climatic conditions of a farm to make accurate disease forecasts for better crop protection and improve yield efficiency. Conventional systems face challenge in managing energy and bandwidth of transmission considering the humongous volume of data generated in a field through IoT based sensors. The present disclosure provides energy-efficient adaptive parameter sampling from the field by optimally configuring the parameter sampling rate thereby maximizing energy-efficiency. This helps reduce unnecessary traffic to a cloud while extending network lifetime.

Abstract: This disclosure relates to precision agriculture that relies on monitoring micro-climatic conditions of a farm to make accurate disease forecasts for better crop protection and improve yield efficiency. Conventional systems face challenge in managing energy and bandwidth of transmission considering the humongous volume of data generated in a field through IoT based sensors. The present disclosure provides energy-efficient adaptive parameter sampling from the field by optimally configuring the parameter sampling rate thereby maximizing energy-efficiency. This helps reduce unnecessary traffic to a cloud while extending network lifetime.