ChatGPT and general-purpose AI count fruits in pictures surprisingly well without programming or training
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 compa...
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| Format: | Article |
| Language: | English |
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Elsevier
2024-12-01
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| Series: | Smart Agricultural Technology |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2772375524002934 |
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| author | Konlavach Mengsuwan Juan C. Rivera-Palacio Masahiro Ryo |
| author_facet | Konlavach Mengsuwan Juan C. Rivera-Palacio Masahiro Ryo |
| author_sort | Konlavach Mengsuwan |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-783ec4ea54e44d2ba71b01bd0f2ea2c5 |
| institution | DOAJ |
| issn | 2772-3755 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Smart Agricultural Technology |
| spelling | doaj-art-783ec4ea54e44d2ba71b01bd0f2ea2c52025-08-20T02:50:16ZengElsevierSmart Agricultural Technology2772-37552024-12-01910068810.1016/j.atech.2024.100688ChatGPT and general-purpose AI count fruits in pictures surprisingly well without programming or trainingKonlavach Mengsuwan0Juan C. Rivera-Palacio1Masahiro Ryo2Research Platform Data Analysis & Simulation, Leibniz Centre for Agricultural Landscape Research (ZALF), Müncheberg, Germany; Environment and Natural Sciences, Brandenburg University of Technology Cottbus‐Senftenberg, Cottbus, GermanyResearch Platform Data Analysis & Simulation, Leibniz Centre for Agricultural Landscape Research (ZALF), Müncheberg, Germany; Environment and Natural Sciences, Brandenburg University of Technology Cottbus‐Senftenberg, Cottbus, Germany; Alliance of Bioversity International and CIAT, Rome, ItalyResearch Platform Data Analysis & Simulation, Leibniz Centre for Agricultural Landscape Research (ZALF), Müncheberg, Germany; Environment and Natural Sciences, Brandenburg University of Technology Cottbus‐Senftenberg, Cottbus, Germany; Corresponding author at: Research Platform Data Analysis & Simulation, Leibniz Centre for Agricultural Landscape Research (ZALF), Müncheberg, Germany.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.http://www.sciencedirect.com/science/article/pii/S2772375524002934Foundation modelGeneral purpose aiChatgptLarge language modelLarge vision language modelagriculture |
| spellingShingle | Konlavach Mengsuwan Juan C. Rivera-Palacio Masahiro Ryo ChatGPT and general-purpose AI count fruits in pictures surprisingly well without programming or training Smart Agricultural Technology Foundation model General purpose ai Chatgpt Large language model Large vision language model agriculture |
| title | ChatGPT and general-purpose AI count fruits in pictures surprisingly well without programming or training |
| title_full | ChatGPT and general-purpose AI count fruits in pictures surprisingly well without programming or training |
| title_fullStr | ChatGPT and general-purpose AI count fruits in pictures surprisingly well without programming or training |
| title_full_unstemmed | ChatGPT and general-purpose AI count fruits in pictures surprisingly well without programming or training |
| title_short | ChatGPT and general-purpose AI count fruits in pictures surprisingly well without programming or training |
| title_sort | chatgpt and general purpose ai count fruits in pictures surprisingly well without programming or training |
| topic | Foundation model General purpose ai Chatgpt Large language model Large vision language model agriculture |
| url | http://www.sciencedirect.com/science/article/pii/S2772375524002934 |
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