Low-cost image-based coffee plant variety identification system using mobile photography under controlled environment | |
| Author | Nikitha, Kumari |
| Call Number | AIT Thesis no.AS-25-06 |
| Subject(s) | Coffee--Data processing Image processing Precision farming |
| Note | A Thesis submitted in partial fulfillment of the requirements for the degree of Master of Engineering in Agricultural Systems and Engineering |
| Publisher | Asian Institute of Technology |
| Abstract | 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 224×224 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., “Predicted Class: Geisha”) 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. |
| Year | 2025 |
| Type | Thesis |
| School | School of Environment, Resources, and Development |
| Department | Department of Food, Agriculture and Natural Resources (Former title: Department of Food Agriculture, and BioResources (DFAB)) |
| Academic Program/FoS | Agricultural Systems and Engineering (ASE) |
| Chairperson(s) | Himanshu, Sushil Kumar |
| Examination Committee(s) | Datta, Avishek;Yaseen, Muhammad |
| Scholarship Donor(s) | AIT Scholarship |
| Degree | Thesis (M. Eng.) - Asian Institute of Technology, 2025 |