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Salient edge map in keras data augmentation
Salient edge map in keras data augmentation











Let’s be clear, this article is not intended to totally demonise and discredit the use of digital tools in agriculture. Or, said perhaps a little less naively, that these actors do not really want to consider insofar as these issues are not totally consistent with their objectives and personal interests. The human and social sciences point to numerous ethical, social, societal, cultural and political issues that the historical agricultural players and the new digital entrants do not seem to have grasped. Although some researchers in these disciplines have been working on digital agriculture for several years, the subject has really exploded in the literature over the last 4-5 years (just look at the publication dates of the articles cited in the bibliography). As a discipline that is still too little known and followed by technologists, I let the humanities and social sciences have their say in this synthesis, often through targeted extracts from their research work.

salient edge map in keras data augmentation

This work is the result of numerous readings of scientific articles in the humanities and social sciences on digital agriculture (the entire scientific bibliography is available at the bottom of the article), supplemented by personal reflections. Here, no overly technical subject or long interviews with professionals in the sector. This article is very different from those I have written in the past. I wanted to show these digital tools in a different way… Big Data, Connected Objects, Robotics, Decision Support Tools, Block Chain, Artificial Intelligence, 5G … – these technologies and buzzwords that are supposed to respond to all the challenges facing agriculture are increasingly present in the mouths of the players who gravitate in and around the agricultural ecosystem, even if it is not certain that these players have really understood their scope. While La Tribune recently reported that French start-ups in the Agtech and Foodtech sector – meaning digital technologies applied to agriculture and agri-food – had raised no less than 560 million euros over the year 2020, putting France in first place in Europe and fifth place in the world in terms of investments, we now learn that the French government, through its Minister of Agriculture Julien Denormandie and its Secretary of State for Digital Transition Cédric O, is launching a vast plan of more than €200 million to support Agtech and Foodtech companies: French AgriTech.įor its promoters, digital technology in agriculture is presented as both a revolution and a necessity.

  • Digital technology at the service of agro-ecology?.
  • Bringing multi-disciplinary actors to the table.
  • Creating and anticipating new imaginaries.
  • Toward responsible research and innovation.
  • It is up to us to decide on the direction of digital technology.
  • Culture and Society – Our relationship to agriculture and the land.
  • Taking into account the energy cost of Agtech tools.
  • Culture and Nature: a risk of anthropomorphism.
  • salient edge map in keras data augmentation

  • Digital technology and our relationship with the living world.
  • Towards a better evaluation of digital tools.
  • Agtech: A very large and abundant ecosystem.
  • Who are digital tools in agriculture really for?.
  • Tracking the process of digital tool adoption still lacking.
  • Rethinking the adoption and evaluation of digital tools in agriculture.
  • Regulations and codes of conduct for agricultural data use.
  • Multi-speed agriculture, fractured on all sides.
  • Digital technologies adapted to agricultural systems that are still not sufficiently diversified.
  • A fragile agricultural model and the risk of a two-speed agriculture.
  • A need for training and skills development.
  • The advisor and his/her relationship with the farmer.
  • Towards a change in the farming profession?.
  • Digital technology is transforming the business of agriculture.
  • The myths of precision, data and algorithms.
  • A neo-Malthusian discourse centred on techno-solutionism.
  • The dominant narratives of the Agtech ecosystem.
  • salient edge map in keras data augmentation

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    #Salient edge map in keras data augmentation code#

  • robosuite_panda_pick_place_can nights_stayĮxcept as otherwise noted, the content of this page is licensed under the Creative Commons Attribution 4.0 License, and code samples are licensed under the Apache 2.0 License.
  • rlu_dmlab_rooms_select_nonmatching_object.
  • rlu_atari_checkpoints_ordered nights_stay.
  • for ex in tfds.load('cifar10', split='train'): They are all accessible in ourįor a quick introduction. In the current tensorflow-datasets package. Note: The datasets documented here are from HEAD and so not all are available
  • diabetic_retinopathy_detection (manual).










  • Salient edge map in keras data augmentation