Nikitha, Kumari creator Himanshu, Sushil Kumar Chairperson Datta, Avishek Examination Committee Yaseen, Muhammad Examination Committee AIT Scholarship Scholarship Donor text series technical report th Pathum Thani, Thailand Asian Institute of Technology 2025 continuing eng

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87 leaves : ill. + 1online resource For crop management, quality assurance, and yield optimisation, accurate coffee plant variety identification is essential, especially in smallholder farming systems with limited access to expert knowledge. This study presents a low-cost, image-based system for identifying six coffee varieties, namely SL-28, Java, Geisha, Catimor, Syrina, and Bourbon, using smartphone-captured leaf images in small-scale Polyhouse environments. A lightweight convolutional neural network (MobileNetV2) is used to classify the images after they have been pre-processed using RGB normalisation, resizing to 224{u00D7}224 pixels, and data augmentation methods like flipping, rotation, zoom, and brightness adjustments. The dataset is then split using an 80/20 organized train-test approach, and performance is evaluated via the accuracy, precision, recall, F1-score, and confusion matrix metrics. This trained model is converted to TensorFlow Lite (TFLite) for low-cost edge-device compatibility. Although full Android deployment was not completed, the evaluated TFLite model produces both a predicted class and the corresponding leaf image (e.g., 2Predicted Class: Geisha3) and achieves a validation accuracy of 90%, demonstrating that compression preserves performance. The system remains affordable, portable, and practical as a prototype for resource-limited farming, while challenges such as misclassification of morphologically similar varieties under inconsistent lighting persist. By addressing the underexplored area of variety-level coffee identification and leveraging accessible mobile-based imaging, this work provides a scalable AI-driven foundation for enhancing precision agriculture in smallholder coffee farms. 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, 2025 Coffee Data processing Image processing Precision farming Asian Institute of Technology. http://203.159.5.9/ait-thesis/detail.php?q=B23285 http://203.159.5.9/ait-thesis/detail.php?q=B23285 260107 20260818153750.0