Crop management, including nitrogen (N) fertilization and irrigation management, has a significant impact on the crop yield, economic profit, and the environment. Although management guidelines exist, it is challenging to find the optimal management practices given a specific planting environment and a crop. Previous work used reinforcement learning (RL) and crop simulators to solve the problem, but the trained policies either have limited performance or are not deployable in the real world. In this paper, we present an intelligent crop management system which optimizes the N fertilization and irrigation simultaneously via RL, imitation learning (IL), and crop simulations using the Decision Support System for Agrotechnology Transfer (DSSAT). We first use deep RL, in particular, deep Q-network, to train management policies that require all state information from the simulator as observations (denoted as full observation). We then invoke IL to train management policies that only need a limited amount of state information that can be readily obtained in the real world (denoted as partial observation) by mimicking the actions of the previously RL-trained policies under full observation. We conduct experiments on a case study using maize in Florida and compare trained policies with a maize management guideline in simulations. Our trained policies under both full and partial observations achieve better outcomes, resulting in a higher profit or a similar profit with a smaller environmental impact. Moreover, the partial-observation management policies are directly deployable in the real world as they use readily available information.

Artificial Intelligence is extending its footprints in almost every nook and corner of the scientific research whereby its tremendous calibre and path breaking operational driver has exposed almost every arena to a new light. Generally speaking, Artificial Intelligence is a new and robust substitute to almost every conventional method which otherwise lacks expertise to resolve, handle and tackle the situations in possible adequate manner. It has the potential to revolutionize and metamorphose science and technology to new attire. Amid use and deployment of Artificial Intelligence in almost every arena, how can someone forget to mention the essence of Artificial Intelligence in the field of Agriculture? Precisely speaking, more than one half of the population of India directly depends upon farming and agriculture as their core livelihood in as much as the matter does not limit to the factum of livelihood only but it obviously feeds a nation for its survival.

One of the challenges of aerial imagery, whether from an airplane or a satellite, is making sense of what you see. What is that image telling you? Ceres Imaging, a California startup with offices in Nebraska and Washington, is using artificial intelligence to answer that question. The company is entering its ninth crop season of providing high-resolution crop imagery for customers. However, John Bourne, vice president of marketing, Ceres Imaging, says the company wanted to work on ways to "productize" the good science it was developing, so three years ago it brought artificial intelligence technology to irrigation issue identification.

If a recent demonstration in Canada is any indication, the digital vineyard of the future might very well receive scientific data in real-time gathered by drones and transferred via a cell network. Global UAV Technologies Ltd., Jöst Vineyards, VineView (Scientific Aerial Imaging Inc.) and a major Canadian telecommunications company recently completed a 4G proof-of-concept mission in in Malagash, Nova Scotia, to demonstrate a real-word application of drone technology for a project called the "Digital Vineyard of the Future." "Fine wine making is in the growing of grapes with specific qualities, where many variables have to be taken into consideration," said Jonathan Rodwell, director of viticulture and winemaking for Jöst Vineyards. "We see these emerging technologies offering excellent opportunities for integrated measurement and management of our vineyards and focus on precision viticulture." Global UAV provided a 4G-enabled, Procyon 800E helicopter drone platform with a specialized multi-spectral imaging payload.

Accurate estimates of daily crop evapotranspiration (ET) are needed for efficient irrigation management in regions where crop water demand exceeds rainfall. Daily grass or alfalfa reference ET values and crop coefficients are widely used to estimate crop water demand. Inaccurate reference ET estimates can hence have a tremendous impact on irrigation costs and the demands on freshwater resources. ET networks calculate reference ET using precise measurements of meteorological data. These networks are typically characterized by gaps in spatial coverage and lack of sufficient funding, creating an immediate need for alternative sources that can fill data gaps without high costs. Although non-agricultural weather stations provide publicly accessible meteorological data, there are concerns that the data may be unsuitable for estimating reference ET due to factors such as weather station siting, data formats and quality control issues. The objective of our research is to enable the use of alternative data sources, adapting sophisticated machine learning algorithms such as Gaussian process models and neural networks to discover and model the nonlinear relationships between non-ET weather station data and the reference ET computed by ET networks. Using data from the Texas High Plains region in the U.S., we demonstrate significant improvement in estimation accuracy in comparison with baseline regression models typically used for irrigation management applications.