@misc{HinzMengsuwanCaviedesVoullieme, author = {Hinz, Christoph and Mengsuwan, Konlavach and Caviedes-Voullieme, Daniel}, title = {Spatial analytics of self-organized vegetation pattern in semi-arid regions: an example on tiger-bush patterns in Sudan}, series = {EGU General Assembly 2021}, journal = {EGU General Assembly 2021}, doi = {10.5194/egusphere-egu21-10102}, language = {en} } @misc{MengsuwanRiveraPalacioRyo, author = {Mengsuwan, Konlavach and Rivera-Palacio, Juan C. and Ryo, Masahiro}, title = {ChatGPT and general-purpose AI count fruits in pictures surprisingly well without programming or training}, series = {Smart Agricultural Technology}, volume = {9}, journal = {Smart Agricultural Technology}, publisher = {Elsevier BV}, issn = {2772-3755}, doi = {10.1016/j.atech.2024.100688}, pages = {6}, abstract = {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 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 education, outreach, and real-world implementation of generative AI for supporting farmers.}, language = {en} } @misc{RyoSchillerStilleretal., author = {Ryo, Masahiro and Schiller, Josepha and Stiller, Stefan and Palacio, Juan Camilo Rivera and Mengsuwan, Konlavach and Safonova, Anastasiia and Wei, Yuqi}, title = {Deep learning for sustainable agriculture needs ecology and human involvement}, series = {Journal of Sustainable Agriculture and Environment}, volume = {2}, journal = {Journal of Sustainable Agriculture and Environment}, number = {1}, publisher = {Wiley}, issn = {2767-035X}, doi = {10.1002/sae2.12036}, pages = {40 -- 44}, abstract = {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.}, language = {en} }