A DIGITAL QUALITY ASSESSMENT OF BAMBOOSHOOTS.AI FOR HARVEST AND PEST DETECTION
This research offered an in-depth evaluation of "BambooShoots.AI," a platform aiding bamboo cultivators in harvest timing and pest detection. Using a quantitative method, it incorporated Lund A.M.'s USE Questionnaire (2001) and usage data analysis to gauge user satisfaction and effect...
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| Format: | Article |
| Language: | English |
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University of Kragujevac
2024-12-01
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| Series: | Proceedings on Engineering Sciences |
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| Online Access: | https://pesjournal.net/journal/v6-n4/8.pdf |
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| author | Charlot L. Maramag Thelma D. Palaoag |
| author_facet | Charlot L. Maramag Thelma D. Palaoag |
| author_sort | Charlot L. Maramag |
| collection | DOAJ |
| description | This research offered an in-depth evaluation of "BambooShoots.AI," a platform aiding bamboo cultivators in harvest timing and pest detection. Using a quantitative method, it incorporated Lund A.M.'s USE Questionnaire (2001) and usage data analysis to gauge user satisfaction and effectiveness. The majority of participants were mid-aged cultivators, providing insights on system usefulness, ease of use, learning, and overall satisfaction. Demographics showed a primary user base of 35-44-year-olds with balanced gender representation and a high rate of Bachelor’s degree holders, highlighting the platform's broad appeal. BambooShoots.AI was noted for its significant usability, scoring well in all evaluated aspects. The study suggested enhancing the interface for older users, continuous feedback integration for improvement, and specialized training programs. It emphasized the need for accessible and inclusive design, aligning with evolving user needs. BambooShoots.AI emerged as a potent, user-focused tool in agricultural technology, pointing to its potential for wider adoption and development in the farming community, and affirming its role as a critical asset in modern agriculture. |
| format | Article |
| id | doaj-art-cddcece3a1fa47f3b4c8a42596be019c |
| institution | Kabale University |
| issn | 2620-2832 2683-4111 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | University of Kragujevac |
| record_format | Article |
| series | Proceedings on Engineering Sciences |
| spelling | doaj-art-cddcece3a1fa47f3b4c8a42596be019c2024-12-09T13:11:53ZengUniversity of KragujevacProceedings on Engineering Sciences2620-28322683-41112024-12-01641489149610.24874/PES06.04.008A DIGITAL QUALITY ASSESSMENT OF BAMBOOSHOOTS.AI FOR HARVEST AND PEST DETECTIONCharlot L. Maramag 0https://orcid.org/0000-0002-6128-2078Thelma D. Palaoag 1https://orcid.org/0000-0002-5474-7260Cagayan State University-Gonzaga, Cagayan Valley, Philippines University of the Cordilleras, Baguio City, Philippines This research offered an in-depth evaluation of "BambooShoots.AI," a platform aiding bamboo cultivators in harvest timing and pest detection. Using a quantitative method, it incorporated Lund A.M.'s USE Questionnaire (2001) and usage data analysis to gauge user satisfaction and effectiveness. The majority of participants were mid-aged cultivators, providing insights on system usefulness, ease of use, learning, and overall satisfaction. Demographics showed a primary user base of 35-44-year-olds with balanced gender representation and a high rate of Bachelor’s degree holders, highlighting the platform's broad appeal. BambooShoots.AI was noted for its significant usability, scoring well in all evaluated aspects. The study suggested enhancing the interface for older users, continuous feedback integration for improvement, and specialized training programs. It emphasized the need for accessible and inclusive design, aligning with evolving user needs. BambooShoots.AI emerged as a potent, user-focused tool in agricultural technology, pointing to its potential for wider adoption and development in the farming community, and affirming its role as a critical asset in modern agriculture.https://pesjournal.net/journal/v6-n4/8.pdfbambooshoots.aidisease predictionweb applicationusabilitysoftware quality |
| spellingShingle | Charlot L. Maramag Thelma D. Palaoag A DIGITAL QUALITY ASSESSMENT OF BAMBOOSHOOTS.AI FOR HARVEST AND PEST DETECTION Proceedings on Engineering Sciences bambooshoots.ai disease prediction web application usability software quality |
| title | A DIGITAL QUALITY ASSESSMENT OF BAMBOOSHOOTS.AI FOR HARVEST AND PEST DETECTION |
| title_full | A DIGITAL QUALITY ASSESSMENT OF BAMBOOSHOOTS.AI FOR HARVEST AND PEST DETECTION |
| title_fullStr | A DIGITAL QUALITY ASSESSMENT OF BAMBOOSHOOTS.AI FOR HARVEST AND PEST DETECTION |
| title_full_unstemmed | A DIGITAL QUALITY ASSESSMENT OF BAMBOOSHOOTS.AI FOR HARVEST AND PEST DETECTION |
| title_short | A DIGITAL QUALITY ASSESSMENT OF BAMBOOSHOOTS.AI FOR HARVEST AND PEST DETECTION |
| title_sort | digital quality assessment of bambooshoots ai for harvest and pest detection |
| topic | bambooshoots.ai disease prediction web application usability software quality |
| url | https://pesjournal.net/journal/v6-n4/8.pdf |
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