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    <title language="eng">Spatial analytics of self-organized vegetation pattern in semi-arid regions: an example on tiger-bush patterns in Sudan</title>
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    <title language="eng">ChatGPT and general-purpose AI count fruits in pictures surprisingly well without programming or training</title>
    <abstract language="eng">General-purpose artificial intelligence (AI) can facilitate agricultural digitalization as many tools do not require coding. Yet, it remains unclear how well the emerging general-purpose AI technologies can perform object counting, which is a fundamental task in agricultural digitalization, in comparison to the current standard practice. We show that ChatGPT (GPT4 V) demonstrated moderate performance in counting coffee cherries from images, while the T-Rex, foundation model for object counting, performed with high accuracy. Testing with a hundred images, we examined that ChatGPT can count cherries, and the performance improves with human&#13;
feedback (R2 = 0.36 and 0.46, respectively). The T-Rex foundation model required only a few samples for training but outperformed YOLOv8, the conventional best practice model (R2 = 0.92 and 0.90, respectively). Obtaining the results with these models was 100x shorter than the conventional best practice. These results bring two surprises for deep learning users in applied domains: a foundation model can drastically save effort and achieve higher accuracy than a conventional approach, and ChatGPT can reveal a relatively good performance especially with guidance by providing some examples and feedback. No requirement for coding skills can impact&#13;
education, outreach, and real-world implementation of generative AI for supporting farmers.</abstract>
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      <firstName>Juan C.</firstName>
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      <firstName>Masahiro</firstName>
      <lastName>Ryo</lastName>
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      <value>Foundation model</value>
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    <title language="eng">Deep learning for sustainable agriculture needs ecology and human involvement</title>
    <abstract language="eng">Deep learning is an emerging data analytic tool that can improve predictability, efficiency and sustainability in agriculture. With a bibliometric analysis of 156 articles, we show how deep learning methods have been applied in the context of sustainable agriculture. As a general publication trend, China and India are leading countries for publication, international collaboration is still minor. Deep learning has been popularly applied in the context of smart agriculture across scales for individual plant monitoring, field monitoring, field operation and robotics, predicting soil, water and climate conditions and landscape‐level monitoring of land use and crop types. We identified that the potential of deep learning had been investigated mainly for predicting soil (abiotic), water, climate and vegetation dynamics, but ecological characteristics are critically understudied. We also highlight key themes that can be better addressed with deep learning for fostering sustainable agriculture: (i) including above‐ and belowground ecological dynamics such as ecosystem functioning and ecotone, (ii) evaluating agricultural impacts on other ecosystems and (iii) incorporating the knowledge and opinions of domain experts and stakeholders into artificial intelligence. We propose that deep learning needs to go beyond automatic data analysis by integrating ecological and human knowledge to foster sustainable agriculture.</abstract>
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With a bibliometric analysis of 156 articles, we show how deep learning methods have been applied in the context of sustainable agriculture. As a general publication trend, China and India are leading countries for publication, international collaboration is still minor. Deep learning has been popularly applied in the context of smart agriculture across scales for individual plant monitoring, field monitoring, field operation and robotics, predicting soil, water\u00a0and climate conditions\u00a0and landscape\u2010level monitoring of land use and crop types. We identified that the potential of deep learning had been investigated mainly for predicting soil (abiotic), water, climate\u00a0and vegetation dynamics, but ecological characteristics are critically understudied. We also highlight key themes that can be better addressed with deep learning for fostering sustainable agriculture: (i) including above\u2010 and belowground ecological dynamics such as ecosystem functioning and ecotone, (ii) evaluating agricultural impacts on other ecosystems\u00a0and (iii) incorporating the knowledge and opinions of domain experts and stakeholders into artificial intelligence. We propose that deep learning needs to go beyond automatic data analysis by integrating ecological and human knowledge to foster sustainable agriculture.&lt;\/jats:p&gt;","DOI":"10.1002\/sae2.12036","type":"journal-article","created":{"date-parts":[[2022,12,16]],"date-time":"2022-12-16T13:30:25Z","timestamp":1671197425000},"page":"40-44","update-policy":"http:\/\/dx.doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Deep learning for sustainable agriculture needs ecology and human involvement"],"prefix":"10.1002","volume":"2","author":[{"given":"Masahiro","family":"Ryo","sequence":"first","affiliation":[{"name":"Research Platform \u201cData Analysis &amp;amp; Simulation\u201d Leibniz Centre for Agricultural Landscape Research (ZALF)  M\u00fcncheberg Germany"},{"name":"Environment and Natural Sciences Brandenburg University of Technology Cottbus\u2010Senftenberg  Cottbus Germany"}]},{"given":"Josepha","family":"Schiller","sequence":"additional","affiliation":[{"name":"Research Platform \u201cData Analysis &amp;amp; Simulation\u201d Leibniz Centre for Agricultural Landscape Research (ZALF)  M\u00fcncheberg Germany"},{"name":"Environment and Natural Sciences Brandenburg University of Technology Cottbus\u2010Senftenberg  Cottbus Germany"}]},{"given":"Stefan","family":"Stiller","sequence":"additional","affiliation":[{"name":"Research Platform \u201cData Analysis &amp;amp; Simulation\u201d Leibniz Centre for Agricultural Landscape Research (ZALF)  M\u00fcncheberg Germany"},{"name":"Environment and Natural Sciences Brandenburg University of Technology Cottbus\u2010Senftenberg  Cottbus Germany"}]},{"given":"Juan Camilo","family":"Rivera Palacio","sequence":"additional","affiliation":[{"name":"Research Platform \u201cData Analysis &amp;amp; Simulation\u201d Leibniz Centre for Agricultural Landscape Research (ZALF)  M\u00fcncheberg Germany"},{"name":"Environment and Natural Sciences Brandenburg University of Technology Cottbus\u2010Senftenberg  Cottbus Germany"},{"name":"Climate Action Program, Regional Office for Latin America Alliance of Bioversity International and International Center for Tropical Agriculture\u2014Americas Hub  Valle del Cauca Cali Columbia"}]},{"given":"Konlavach","family":"Mengsuwan","sequence":"additional","affiliation":[{"name":"Research Platform \u201cData Analysis &amp;amp; Simulation\u201d Leibniz Centre for Agricultural Landscape Research (ZALF)  M\u00fcncheberg Germany"},{"name":"Environment and Natural Sciences Brandenburg University of Technology Cottbus\u2010Senftenberg  Cottbus Germany"}]},{"given":"Anastasiia","family":"Safonova","sequence":"additional","affiliation":[{"name":"Research Platform \u201cData Analysis &amp;amp; Simulation\u201d Leibniz Centre for Agricultural Landscape Research (ZALF)  M\u00fcncheberg Germany"},{"name":"Department of Information Systems Saint Petersburg Electrotechnical University \u201cLETI\u201d  Saint Petersburg Russia"},{"name":"Department of Artificial Intelligence Systems Siberian Federal University  Krasnoyarsk Russia"}]},{"given":"Yuqi","family":"Wei","sequence":"additional","affiliation":[{"name":"Research Platform \u201cData Analysis &amp;amp; Simulation\u201d Leibniz Centre for Agricultural Landscape Research (ZALF)  M\u00fcncheberg Germany"},{"name":"Department of Grassland Science, College of Grassland Science &amp;amp; Technology China Agricultural University  Beijing China"},{"name":"Key Laboratory of Grasslands Management and Utilization Ministry of Agriculture and Rural Affairs  Beijing China"}]}],"member":"311","published-online":{"date-parts":[[2022,12,16]]},"reference":[{"key":"e_1_2_9_2_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.joi.2017.08.007"},{"key":"e_1_2_9_3_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41893-020-0510-0"},{"key":"e_1_2_9_4_1","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0268989"},{"key":"e_1_2_9_5_1","doi-asserted-by":"publisher","DOI":"10.3390\/rs11030274"},{"key":"e_1_2_9_6_1","doi-asserted-by":"publisher","DOI":"10.1111\/2041-210X.13256"},{"key":"e_1_2_9_7_1","doi-asserted-by":"publisher","DOI":"10.3390\/agronomy12030748"},{"key":"e_1_2_9_8_1","volume-title":"State of knowledge of soil biodiversity\u2014status, challenges and potentialities: report 2020","author":"FAO, ITPS, GSBI, SCBD and EC","year":"2020"},{"key":"e_1_2_9_9_1","doi-asserted-by":"publisher","DOI":"10.3390\/s20051487"},{"key":"e_1_2_9_10_1","volume-title":"Deep learning","author":"Goodfellow I","year":"2016"},{"key":"e_1_2_9_11_1","doi-asserted-by":"publisher","DOI":"10.3390\/s18103576"},{"key":"e_1_2_9_12_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.compag.2018.02.016"},{"key":"e_1_2_9_13_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41893-020-00631-0"},{"key":"e_1_2_9_14_1","volume-title":"Cognitive data science in sustainable computing","author":"Poonia R","year":"2022"},{"key":"e_1_2_9_15_1","unstructured":"R Core Team. 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