Modern agriculture faces grand challenges to meet increased demands for food, fuel, feed, and fiber with population growth under the constraints of climate change and dwindling natural resources. Data innovation is urgently required to secure and improve the productivity, sustainability, and resilience of our agroecosystems. As various sensors and Internet of Things (IoT) instrumentation become more available, affordable, reliable, and stable, it has become possible to conduct data collection, integration, and analysis at multiple temporal and spatial scales, in real-time, and with high resolutions. At the same time, the sheer amount of data poses a great challenge to data storage and analysis, and the \textit{de facto} data management and analysis practices adopted by scientists have become increasingly inefficient. Additionally, the data generated from different disciplines, such as genomics, phenomics, environment, agronomy, and socioeconomic, can be highly heterogeneous. That is, datasets across disciplines often do not share the same ontology, modality, or format. All of the above make it necessary to design a new data management infrastructure that implements the principles of Findable, Accessible, Interoperable, and Reusable (FAIR). In this paper, we propose Agriculture Data Management and Analytics (ADMA), which satisfies the FAIR principles. Our new data management infrastructure is intelligent by supporting semantic data management across disciplines, interactive by providing various data management/analysis portals such as web GUI, command line, and API, scalable by utilizing the power of high-performance computing (HPC), extensible by allowing users to load their own data analysis tools, trackable by keeping track of different operations on each file, and open by using a rich set of mature open source technologies.

Highly increased interest in Artificial Neural Networks (ANNs) have resulted in impressively wide-ranging improvements in its structure. In this work, we come up with the idea that instead of static plugins that the currently available loss functions are, they should by default be flexible in nature. A flexible loss function can be a more insightful navigator for neural networks leading to higher convergence rates and therefore reaching the optimum accuracy more quickly. The insights to help decide the degree of flexibility can be derived from the complexity of ANNs, the data distribution, selection of hyper-parameters and so on. In the wake of this, we introduce a novel flexible loss function for neural networks. The function is shown to characterize a range of fundamentally unique properties from which, much of the properties of other loss functions are only a subset and varying the flexibility parameter in the function allows it to emulate the loss curves and the learning behavior of prevalent static loss functions. The extensive experimentation performed with the loss function demonstrates that it is able to give state-of-the-art performance on selected data sets. Thus, in all the idea of flexibility itself and the proposed function built upon it carry the potential to open to a new interesting chapter in deep learning research.