Artificial Intelligence for Digital Agriculture at Scale: Techniques, Policies, and Challenges

• URL: http://arxiv.org/abs/2001.09786v1
• Date: Tue, 21 Jan 2020 06:02:38 GMT
• Title: Artificial Intelligence for Digital Agriculture at Scale: Techniques, Policies, and Challenges
• Authors: Somali Chaterji, Nathan DeLay, John Evans, Nathan Mosier, Bernard Engel, Dennis Buckmaster and Ranveer Chandra
• Abstract summary: Digital agriculture has the promise to transform agricultural throughput. It can do this by applying data science and engineering for mapping input factors to crop throughput, while bounding the available resources. This paper is the first one to bring together the important questions that will guide the end-to-end pipeline for the evolution of a new generation of digital agricultural solutions.
• Score: 1.1245087602142634
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Digital agriculture has the promise to transform agricultural throughput. It can do this by applying data science and engineering for mapping input factors to crop throughput, while bounding the available resources. In addition, as the data volumes and varieties increase with the increase in sensor deployment in agricultural fields, data engineering techniques will also be instrumental in collection of distributed data as well as distributed processing of the data. These have to be done such that the latency requirements of the end users and applications are satisfied. Understanding how farm technology and big data can improve farm productivity can significantly increase the world's food production by 2050 in the face of constrained arable land and with the water levels receding. While much has been written about digital agriculture's potential, little is known about the economic costs and benefits of these emergent systems. In particular, the on-farm decision making processes, both in terms of adoption and optimal implementation, have not been adequately addressed. For example, if some algorithm needs data from multiple data owners to be pooled together, that raises the question of data ownership. This paper is the first one to bring together the important questions that will guide the end-to-end pipeline for the evolution of a new generation of digital agricultural solutions, driving the next revolution in agriculture and sustainability under one umbrella.

Related papers

• Generating Diverse Agricultural Data for Vision-Based Farming Applications [74.79409721178489]
  This model is capable of simulating distinct growth stages of plants, diverse soil conditions, and randomized field arrangements under varying lighting conditions. Our dataset includes 12,000 images with semantic labels, offering a comprehensive resource for computer vision tasks in precision agriculture.
  arXiv  Detail & Related papers  (2024-03-27T08:42:47Z)
• Data-Centric Digital Agriculture: A Perspective [23.566985362242498]
  Digital agriculture is rapidly evolving to meet increasing global demand for food, feed, fiber, and fuel. Machine learning research in digital agriculture has predominantly focused on model-centric approaches. To fully realize the potential of digital agriculture, it is crucial to have a comprehensive understanding of the role of data in the field.
  arXiv  Detail & Related papers  (2023-12-06T11:38:26Z)
• Open-sourced Data Ecosystem in Autonomous Driving: the Present and Future [130.87142103774752]
  This review systematically assesses over seventy open-source autonomous driving datasets. It offers insights into various aspects, such as the principles underlying the creation of high-quality datasets. It also delves into the scientific and technical challenges that warrant resolution.
  arXiv  Detail & Related papers  (2023-12-06T10:46:53Z)
• Empowering Agrifood System with Artificial Intelligence: A Survey of the Progress, Challenges and Opportunities [86.89427012495457]
  We review how AI techniques can transform agrifood systems and contribute to the modern agrifood industry. We present a progress review of AI methods in agrifood systems, specifically in agriculture, animal husbandry, and fishery. We highlight potential challenges and promising research opportunities for transforming modern agrifood systems with AI.
  arXiv  Detail & Related papers  (2023-05-03T05:16:54Z)
• AGI for Agriculture [30.785325834651644]
  Artificial General Intelligence (AGI) is poised to revolutionize a variety of sectors, including healthcare, finance, transportation, and education. This paper delves into the potential future applications of AGI in agriculture, such as agriculture image processing, natural language processing (NLP), robotics, knowledge graphs, and infrastructure.
  arXiv  Detail & Related papers  (2023-04-12T19:39:49Z)
• Affordable Artificial Intelligence -- Augmenting Farmer Knowledge with AI [1.9992810351494297]
  This article presents the AI technology for predicting micro-climate conditions on the farm. This publication is the fifth in the E-agriculture in Action series, launched in 2016 and jointly produced by FAO and ITU. It aims to raise awareness about existing AI applications in agriculture and to inspire stakeholders to develop and replicate the new ones.
  arXiv  Detail & Related papers  (2023-03-04T02:29:52Z)
• Synthetic Data: Opening the data floodgates to enable faster, more directed development of machine learning methods [96.92041573661407]
  Many ground-breaking advancements in machine learning can be attributed to the availability of a large volume of rich data. Many large-scale datasets are highly sensitive, such as healthcare data, and are not widely available to the machine learning community. Generating synthetic data with privacy guarantees provides one such solution.
  arXiv  Detail & Related papers  (2020-12-08T17:26:10Z)
• Learning from Data to Optimize Control in Precision Farming [77.34726150561087]
  Special issue presents the latest development in statistical inference, machine learning and optimum control for precision farming. Satellite positioning and navigation followed by Internet-of-Things generate vast information that can be used to optimize farming processes in real-time.
  arXiv  Detail & Related papers  (2020-07-07T12:44:17Z)
• Farmland Parcel Delineation Using Spatio-temporal Convolutional Networks [77.63950365605845]
  Farm parcel delineation provides cadastral data that is important in developing and managing climate change policies. This data can also be useful for the agricultural insurance sector for assessing compensations following damages associated with extreme weather events. Using satellite imaging can be a scalable and cost effective manner to perform the task of farm parcel delineation.
  arXiv  Detail & Related papers  (2020-04-11T19:49:09Z)
• Crop Knowledge Discovery Based on Agricultural Big Data Integration [2.597676155371155]
  Agricultural data can be generated through various sources, such as: Internet of Thing (IoT), sensors, satellites, weather stations, robots, farm equipment, agricultural laboratories, farmers, government agencies and agribusinesses. We propose an agricultural data integration method using a constellation schema which is designed to be flexible enough to incorporate other datasets and big data models.
  arXiv  Detail & Related papers  (2020-03-11T00:13:17Z)
• Data Warehouse and Decision Support on Integrated Crop Big Data [0.0]
  We designed and implemented a continental level agricultural data warehouse (ADW) ADW is characterised by its (1) flexible schema; (2) data integration from real agricultural multi datasets; (3) data science and business intelligent support; (4) high performance; (5) high storage; (6) security; (7) governance and monitoring; (8) consistency, availability and partition tolerant; (9) cloud compatibility.
  arXiv  Detail & Related papers  (2020-03-10T00:10:22Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.