Effectiveness of Generative AI Tool to Determine Fruit Quality: Watermelon Case Study
To select a good quality watermelon, one needs the ability and experience to recognize specific patterns in its visual characteristics. As buyers usually cannot taste the watermelon beforehand, the outer patterns of a good quality watermelon may vary depending on the perspective of the purchaser. As...
Saved in:
| Main Author: | |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-03-01
|
| Series: | Horticulturae |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2311-7524/11/3/308 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850204445806166016 |
|---|---|
| author | Serkan Ozdemir |
| author_facet | Serkan Ozdemir |
| author_sort | Serkan Ozdemir |
| collection | DOAJ |
| description | To select a good quality watermelon, one needs the ability and experience to recognize specific patterns in its visual characteristics. As buyers usually cannot taste the watermelon beforehand, the outer patterns of a good quality watermelon may vary depending on the perspective of the purchaser. As a result, there is a gradual adoption of new generative artificial intelligence (AI) tools in the field of horticulture. These tools are expected to minimize bias in human perception when determining the quality of a watermelon based on its outer characteristics. This study aimed to compare the quality of watermelons selected by generative AI with a panel sensory evaluation test. The results of the two case studies indicate a significant difference in the quality of the generative AI-selected watermelons. As an average, watermelon evaluators favored the watermelons selected by ChatGPT as the best based on the Wilcoxon rank sum test and paired <i>t</i>-test (<i>p</i> < 0.05). In conclusion, watermelons can be selected by ChatGPT with minimal effort, promptly meeting consumer expectations. |
| format | Article |
| id | doaj-art-a4f60e4bbf874eb1b3d27d9cdf2fe41d |
| institution | OA Journals |
| issn | 2311-7524 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Horticulturae |
| spelling | doaj-art-a4f60e4bbf874eb1b3d27d9cdf2fe41d2025-08-20T02:11:17ZengMDPI AGHorticulturae2311-75242025-03-0111330810.3390/horticulturae11030308Effectiveness of Generative AI Tool to Determine Fruit Quality: Watermelon Case StudySerkan Ozdemir0Transport and Planning Department, Delft University of Technology, 2628 CN Delft, The NetherlandsTo select a good quality watermelon, one needs the ability and experience to recognize specific patterns in its visual characteristics. As buyers usually cannot taste the watermelon beforehand, the outer patterns of a good quality watermelon may vary depending on the perspective of the purchaser. As a result, there is a gradual adoption of new generative artificial intelligence (AI) tools in the field of horticulture. These tools are expected to minimize bias in human perception when determining the quality of a watermelon based on its outer characteristics. This study aimed to compare the quality of watermelons selected by generative AI with a panel sensory evaluation test. The results of the two case studies indicate a significant difference in the quality of the generative AI-selected watermelons. As an average, watermelon evaluators favored the watermelons selected by ChatGPT as the best based on the Wilcoxon rank sum test and paired <i>t</i>-test (<i>p</i> < 0.05). In conclusion, watermelons can be selected by ChatGPT with minimal effort, promptly meeting consumer expectations.https://www.mdpi.com/2311-7524/11/3/308watermelonartificial intelligenceChatGPTvisual assessmentpanel test |
| spellingShingle | Serkan Ozdemir Effectiveness of Generative AI Tool to Determine Fruit Quality: Watermelon Case Study Horticulturae watermelon artificial intelligence ChatGPT visual assessment panel test |
| title | Effectiveness of Generative AI Tool to Determine Fruit Quality: Watermelon Case Study |
| title_full | Effectiveness of Generative AI Tool to Determine Fruit Quality: Watermelon Case Study |
| title_fullStr | Effectiveness of Generative AI Tool to Determine Fruit Quality: Watermelon Case Study |
| title_full_unstemmed | Effectiveness of Generative AI Tool to Determine Fruit Quality: Watermelon Case Study |
| title_short | Effectiveness of Generative AI Tool to Determine Fruit Quality: Watermelon Case Study |
| title_sort | effectiveness of generative ai tool to determine fruit quality watermelon case study |
| topic | watermelon artificial intelligence ChatGPT visual assessment panel test |
| url | https://www.mdpi.com/2311-7524/11/3/308 |
| work_keys_str_mv | AT serkanozdemir effectivenessofgenerativeaitooltodeterminefruitqualitywatermeloncasestudy |