The population is increasing tremendously and with this increase the demand of food. The traditional methods which were used by the farmers were not sufficient enough to fulfil these requirements. Thus, new automated methods (Drone technology) were introduced. These new methods satisfied the food requirements and also provided employment opportunities to billions of people. Drones technologies saves the excess use of water, pesticides, and herbicides, maintains the fertility of the soil, also helps in the efficient use of man power and elevate the productivity and improve the quality. The objective of this paper is to review the usage of Drones in agriculture applications. Based on the literature, we found that a lot of agriculture applications can be done by using Drone. In the methodology, we used a comprehensive review from other researches in this world. This paper summarizes the current state of drone technology for agricultural uses, including crop health monitoring and farm operations like weed management, Evapotranspiration estimation, spraying etc. The research article concludes by recommending that more farmers invest in drone technology to better their agricultural outputs.

The Indian Sundarbans are considered one of the zones of highest vulnerability in the world in terms of climate change. About 4.43 million people living in the Indian Sundarbans face a lack of freshwater availability due to the erratic behaviour of monsoon rains, frequent cyclonic storms, intrusion of saline water, and other factors, all of which affect the fisheries and agriculture activities of this area. In this study, estimates of freshwater availability through past and predicted future rainfall and evapotranspiration change scenarios in the Sundarbans are presented. Due to the lack of high-quality in situ data, various sources of gridded rainfall and evapotranspiration data were used. Between 1948 and 2010, half of the 19 administrative blocks showed a decreasing trend of monsoonal rainfall while the rest showed an increasing trend. Freshwater availability showed a decreasing trend during the monsoon season over different blocks of the Sundarbans, which is a matter of great concern for fisheries and agricultural activities. Statistical downscaling was used to generate future rainfall and evapotranspiration scenarios, using coarse resolution Global Climate Models from the Coupled Model Inter-comparison Project, Phase Five for a smaller area like the Sundarbans. Downscaled global climate models project an increasing trend in future monsoon rainfall in both RCP 4.5 and RCP 8.5 emission scenarios. The increasing rainfall can trigger excessive run-off and flooding, which would in turn affect aquaculture infrastructure and damage lentic aquaculture productions across the Sundarbans. However, increased rainfall may expand the flood plain area and extend the feeding grounds of fish. Hence, the impact of rainfall change is quite unpredictable. Proper adaptation techniques may be required to harness the positive impacts while preventing negative effects.

No abstract

Rice is the staple food for over half of the world population. Crop models potentially offer a means to readily explore management options to increase yield, and to determine trade-off between yield, resource-use efficiency and environmental outcomes. This paper reviews the performance of CERES-Rice model in different regions of the world in relation to their potential application towards increasing resource use efficiency and yield of rice. In this article, the CERES-Rice model evaluation by using the simulated and observed values on crop phenology (anthesis, physiological maturity) and final grain yield mainly over Asian countries by different authors has been compiled and described. Mainly the model was evaluated based on different statistical measures such as RMSE and D-index. Several datasets for the prediction of grain yield and phenological period across different parts of Asia were examined. This particular model predicted those with high-accuracy (nRMSE1-5% for anthesis and 1-4% for physiological maturity days). For various data sets for grain yield, the nRMSE varied between 0.05-5.00 percent with error percentage of 2-5%. The model sometimes over-estimated or under-estimated the values of grain yield, especially under water stress conditions.