03737nas a22003855u 4500 20260818085915.0 190208s20249999th a ms tm 000 eng d .b12293416 AIT Thesis no.AE-24-03 Wangmo, Chime Deep learning and drone imagery-based automated recognition of coffee plant varieties Pathum Thani, Thailand : Asian Institute of Technology, 2024 85leaves : ill. + 1online resource Thesis ; no. AE-24-03 A Thesis submitted in partial fulfillment of the requirements for the degree of Master of Engineering in Agricultural Systems and Engineering Thesis (M. Eng.) - Asian Institute of Technology, 2024 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. Coffee Agricultural innovations Drone aircraft Neural networks (Neurobiology) Himanshu, Sushil Kumar, Chairperson Datta, Avishek, Examination Committee Pramanik, Malay, Examination Committee Asian Development Bank-Japan Scholarship Program (ADB-JSP), Scholarship Donor Asian Institute of Technology. Thesis ; no. AE-24-03 Full-Text http://203.159.5.9/ait-thesis/detail.php?q=B21433 .b12293416 mnait a 241021 0 241011 m h a 0 mnarc 71 Barcode : - CREATED : 2024-10-16 RECORD # : i13516917 LPATRON : 0 LCHKIN : - # RENEWALS : 0 # OVERDUE : 0 IUSE3 : 0 TOT CHKOUT : 0 TOT RENEW : 0 26185 26185 0 0 0 0 MAIN MAIN mnarc 2026-08-18 0 AIT Thesis no.AE-24-03 2026-08-18 08:59:15 2026-08-18 71