Fecha de publicación: 17/07/2020 Fuente: WIPO Agriculture Portada Agriculture is a basic and most important profession of any country as it balances the food requirement and also the essential raw materials of industry. In the similar sense, the adaptation of implementing smart technology in agriculture practices needs to be focused on better land productivity. Irrigation optimization is a significant practice in Precision Agriculture (PA) based farm management. A PLSR and fuzzy logic based hybrid smart decision support system (DSS) for crop specific irrigation notification and control in precision agriculture is proposed and this can be implemented in farm land, green-house and poly-house. The proposed DSS model can work on real-time mode using National Instruments LabVIEW. This hybrid smart DSS prediction algorithm is implemented in a test land located in Bhubaneswar, the eastern region of India. A comparative analysis is also performed by calculating RMSE, RSE, MSE, RPD and algorithm running time. Crop wise evapotransiration is also calculated using Blaney-Criddle method. The model is attached to the DSS and crop wise required evapotranspiration is found out by considering the planting date. This technique compensates the amount of water that may be lost through evapotranspiration and many other criteria by utilizing the proposed model to predict future irrigation requirements by taking weather, soil, water, and crop data into considerations.Similarly, the fertilizer and manure should be precisely applied during agriculture practice throughout the countryside with the use of digital technologies. This motivates us to develop a reactive web application which will accurately predict the required soil N-P-K (Nitrogen-Phosphorus-Potassium) content by utilizing one time soil testing results of available soil N-P-K contents as per the yield target. This predictive model is designed by considering standard experimental data sets from Indian Council for Agriculture Research (ICAR), India. The prediction model is designed using Random Forest (RF) algorithm which is capable of handling large dataset. The predicted N-P-K content are shown in a reactive R Shiny user interface to notify required N-P-K values for the necessary action by the farmer. The complete web based prediction model is efficiently conveying the required N-P-K content information of the particular farm location to the farmer as well as the agriculture specialists.An integrated irrigation monitoring and control mechanism, and fertilizer management mechanism is developed in this work. The complete system was tested in a test land located in the eastern region of India.