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.

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A field experiment was conducted during 2018 at the Instructional Farm, Bidhan Chandra Krishi Viswavidyalaya, Jaguli, Nadia to study the effects of three irrigation regimes (CPE 60 mm, CPE 50 mm and CPE 40 mm) and five nutrition (control, FYM @ 2.5 t/ha, cowdung @ 10 t/ha, poultry manure @ 2.0 t/ha and RDF) on summer cowpea. The results showed that under plenty water supply condition, scheduling of irrigation at CPE 40 accompanied with 100% recommended dose of fertilizers (12.5:25:12.5 kg/ha of N, P and K, respectively) as basal was found to be the best treatment combination for obtaining maximum growth, yield components, pod and seed yield and moderate crop water use efficiency. Under limited or water constraint, deficit irrigation schedule at CPE 50 or CPE 60 in association with 100% RDF was the alternative option for achieving relatively higher pod and seed yield and higher to maximum crop water use efficiency. The seasonal yield response factor for cowpea was found to be 4.64.

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.

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