ADW: Blockchain-enabled Small-scale Farm Digitization

• URL: http://arxiv.org/abs/2003.06862v1
• Date: Sun, 15 Mar 2020 16:15:20 GMT
• Title: ADW: Blockchain-enabled Small-scale Farm Digitization
• Authors: Nelson Bore, Andrew Kinai, Peninah Waweru, Isaac Wambugu, Juliet Mutahi, Everlyne Kemunto, Reginald Bryant, Komminist Weldemariam
• Abstract summary: We present a system, called agribusiness Digital Wallet (ADW), which leverages blockchain to formalize the interactions and enable seamless data flow in small-scale farming ecosystem. We demonstrate the ability to utilize farm activities to create trusted electronic field records (EFR) with automated valuable insights.
• Score: 2.406769835641701
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Farm records hold the static, temporal, and longitudinal details of the farms. For small-scale farming, the ability to accurately capture these records plays a critical role in formalizing and digitizing the agriculture industry. Reliable exchange of these record through a trusted platform could unlock critical and valuable insights to different stakeholders across the value chain in agriculture eco-system. Lately, there has been increasing attention on digitization of small scale farming with the objective of providing farm-level transparency, accountability, visibility, access to farm loans, etc. using these farm records. However, most solutions proposed so far have the shortcoming of providing detailed, reliable and trusted small-scale farm digitization information in real time. To address these challenges, we present a system, called Agribusiness Digital Wallet (ADW), which leverages blockchain to formalize the interactions and enable seamless data flow in small-scale farming ecosystem. Utilizing instrumentation of farm tractors, we demonstrate the ability to utilize farm activities to create trusted electronic field records (EFR) with automated valuable insights. Using ADW, we processed several thousands of small-scale farm-level activity events for which we also performed automated farm boundary detection of a number of farms in different geographies.

Related papers

• AgroGPT: Efficient Agricultural Vision-Language Model with Expert Tuning [30.034193330398292]
  We propose an approach to construct instruction-tuning data that harnesses vision-only data for the agriculture domain. We utilize diverse agricultural datasets spanning multiple domains, curate class-specific information, and employ large language models (LLMs) to construct an expert-tuning set. We expert-tuned and created AgroGPT, an efficient LMM that can hold complex agriculture-related conversations and provide useful insights.
  arXiv  Detail & Related papers  (2024-10-10T22:38:26Z)
• 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)
• Self-Supervised Backbone Framework for Diverse Agricultural Vision Tasks [0.3683202928838613]
  Computer vision in agriculture is game-changing to transform farming into a data-driven, precise, and sustainable industry. Deep learning has empowered agriculture vision to analyze vast, complex visual data, but heavily rely on the availability of large annotated datasets. We propose a lightweight framework utilizing SimCLR, a contrastive learning approach, to pre-train a ResNet-50 backbone on a large, unannotated dataset of real-world agriculture field images.
  arXiv  Detail & Related papers  (2024-03-22T14:46:51Z)
• HarvestNet: A Dataset for Detecting Smallholder Farming Activity Using Harvest Piles and Remote Sensing [50.4506590177605]
  HarvestNet is a dataset for mapping the presence of farms in the Ethiopian regions of Tigray and Amhara during 2020-2023. We introduce a new approach based on the detection of harvest piles characteristic of many smallholder systems. We conclude that remote sensing of harvest piles can contribute to more timely and accurate cropland assessments in food insecure regions.
  arXiv  Detail & Related papers  (2023-08-23T11:03:28Z)
• PhenoBench -- A Large Dataset and Benchmarks for Semantic Image Interpretation in the Agricultural Domain [29.395926321984565]
  We present an annotated dataset and benchmarks for the semantic interpretation of real agricultural fields. Our dataset recorded with a UAV provides high-quality, pixel-wise annotations of crops and weeds, but also crop leaf instances at the same time. We provide benchmarks for various tasks on a hidden test set comprised of different fields.
  arXiv  Detail & Related papers  (2023-06-07T16:04:08Z)
• 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)
• 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)
• 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)
• Artificial Intelligence for Digital Agriculture at Scale: Techniques, Policies, and Challenges [1.1245087602142634]
  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.
  arXiv  Detail & Related papers  (2020-01-21T06:02:38Z)
• Agriculture-Vision: A Large Aerial Image Database for Agricultural Pattern Analysis [110.30849704592592]
  We present Agriculture-Vision: a large-scale aerial farmland image dataset for semantic segmentation of agricultural patterns. Each image consists of RGB and Near-infrared (NIR) channels with resolution as high as 10 cm per pixel. We annotate nine types of field anomaly patterns that are most important to farmers.
  arXiv  Detail & Related papers  (2020-01-05T20:19:33Z)

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.