AI SearchDeep learning and drone imagery-based automated recognition of coffee plant varieties (Record no. 26185)
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| fixed length control field | 03737nas a22003855u 4500 |
| 005 - DATE AND TIME OF LATEST TRANSACTION | |
| control field | 20260818085915.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
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| 035 ## - SYSTEM CONTROL NUMBER | |
| System control number | .b12293416 |
| 099 #9 - LOCAL FREE-TEXT CALL NUMBER (OCLC) | |
| Classification number | AIT Thesis no.AE-24-03 |
| 100 1# - MAIN ENTRY--PERSONAL NAME | |
| Personal name | Wangmo, Chime |
| 245 10 - TITLE STATEMENT | |
| Title | Deep learning and drone imagery-based automated recognition of coffee plant varieties |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. | |
| Place of publication, distribution, etc. | Pathum Thani, Thailand : |
| Name of publisher, distributor, etc. | Asian Institute of Technology, |
| Date of publication, distribution, etc. | 2024 |
| 300 ## - PHYSICAL DESCRIPTION | |
| Extent | 85leaves : |
| Other physical details | ill. + |
| Accompanying material | 1online resource |
| 490 1# - SERIES STATEMENT | |
| Series statement | Thesis ; |
| Volume/sequential designation | no. AE-24-03 |
| 500 ## - GENERAL NOTE | |
| General note | A Thesis submitted in partial fulfillment of the requirements for the degree of Master of Engineering in Agricultural Systems and Engineering |
| 502 ## - DISSERTATION NOTE | |
| Dissertation note | Thesis (M. Eng.) - Asian Institute of Technology, 2024 |
| 520 ## - SUMMARY, ETC. | |
| Summary, etc. | Coffee, a globally traded agricultural commodity and one of the most consumed beverages worldwide, plays a significant role in generating millions of jobs and income. The expansion of the coffee industry driven by increased consumption and demand for specialty coffee necessitates innovative methods for accurately identifying and classifying coffee plant varieties. Traditional approaches based on physical characteristics and chemometric techniques face challenges as the number of coffee varieties grows. The study aims to investigate the viability of utilizing images captured by unmanned aerial vehicles (UAVs) with high-resolution sensors for images processing techniques. This involves the collection, processing, and analysis of real-time drone imagery to identify different coffee plant varieties. Moreover, the research focuses on the development and optimization of deep learning algorithms, particularly Convolutional Neural Networks (CNNs), tailored specifically for discerning unique features of various coffee plant varieties. CNNs belong to deep neural networks that have proven highly effective in various computer vision tasks, such as object detection, image classification and image segmentation. By integrating advanced technologies such as UAVs and CNNs, the study seeks to enhance the efficiency and accuracy of coffee variety classification, offering potential advancements for the coffee cultivation sector. The CNNs model achieved an 89.1% training accuracy, 67.8% validation accuracy, and an overall predictive accuracy of 67.06%. These results demonstrate the effectiveness of employing advanced computer vision techniques for coffee variety classification using drone-captured imagery. This study offers a promising decision support algorithm for the classification of coffee plant varieties, contributing to the enhancement of the coffee cultivation sector. By revolutionizing the coffee industry through technologically advanced and efficient solutions, this research addresses the challenges associated with the growing diversity of coffee varieties. |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name entry element | Coffee |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name entry element | Agricultural innovations |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name entry element | Drone aircraft |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name entry element | Neural networks (Neurobiology) |
| 700 1# - ADDED ENTRY--PERSONAL NAME | |
| Personal name | Himanshu, Sushil Kumar, |
| Relator term | Chairperson |
| 700 1# - ADDED ENTRY--PERSONAL NAME | |
| Personal name | Datta, Avishek, |
| Relator term | Examination Committee |
| 700 1# - ADDED ENTRY--PERSONAL NAME | |
| Personal name | Pramanik, Malay, |
| Relator term | Examination Committee |
| 710 2# - ADDED ENTRY--CORPORATE NAME | |
| Corporate name or jurisdiction name as entry element | Asian Development Bank-Japan Scholarship Program (ADB-JSP), |
| Relator term | Scholarship Donor |
| 810 2# - SERIES ADDED ENTRY--CORPORATE NAME | |
| Corporate name or jurisdiction name as entry element | Asian Institute of Technology. |
| Title of a work | Thesis ; |
| Volume/sequential designation | no. AE-24-03 |
| 856 40 - ELECTRONIC LOCATION AND ACCESS | |
| Materials specified | Full-Text |
| Uniform Resource Identifier | <a href="http://203.159.5.9/ait-thesis/detail.php?q=B21433">http://203.159.5.9/ait-thesis/detail.php?q=B21433</a> |
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| a | 241021 |
| 998 ## - LOCAL CONTROL INFORMATION (RLIN) | |
| Operator's initials, OID (RLIN) | 0 |
| Cataloger's initials, CIN (RLIN) | 241011 |
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| 945 ## - LOCAL PROCESSING INFORMATION (OCLC) | |
| l | mnarc |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
| Koha item type | 71-e-Theses |
| 909 ## - LOCAL ITEMS USED | |
| Barcode | Barcode : - |
| CREATED | CREATED : 2024-10-16 |
| RECORD Id | RECORD # : i13516917 |
| LPATRON | LPATRON : 0 |
| LCHKIN | LCHKIN : - |
| RENEWALS | # RENEWALS : 0 |
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| Withdrawn status | Lost status | Damaged status | Not for loan | Home library | Current library | Shelving location | Date acquired | Total checkouts | Full call number | Date last seen | Price effective from | Koha item type |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Available for Loans | Asian Institute of Technology Library | Asian Institute of Technology Library | Archives | 18/08/2026 | AIT Thesis no.AE-24-03 | 18/08/2026 | 18/08/2026 | 71-e-Theses |