TY  - SER
AU  - Nikitha,Kumari
AU  - Himanshu,Sushil Kumar
AU  - Datta,Avishek
AU  - Yaseen,Muhammad
ED  - AIT Scholarship,
TI  - Low-cost image-based coffee plant variety identification system using mobile photography under controlled environment
T2  - Thesis
PY  - 2025///
CY  - Pathum Thani, Thailand
PB  - Asian Institute of Technology
KW  - Coffee
KW  - Data processing
KW  - Image processing
KW  - Precision farming
N1  - 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
N2  - 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.
UR  - http://203.159.5.9/ait-thesis/detail.php?q=B23285
ER  -