Mango Leaf Image Monitoring System for Early Disease Detection
Keywords:
Monitoring, Citra, Daun, Mangga, PenyakitAbstract
This research develops a mango leaf image monitoring system for automated early disease detection in the field. The proposed method utilizes image acquisition via a mobile phone camera, contrast enhancement preprocessing, color-based leaf segmentation, and extraction of symptom-relevant texture and color features. Classification is performed using a lightweight machine learning model designed to run on edge devices; the architecture is tested against several common disease variants, including anthracnose and bacterial spot. The dataset consists of annotated images collected under various lighting conditions and angles, then split for training, validation, and testing. Evaluations demonstrate high accuracy in distinguishing healthy from infected leaves, with a sensitivity of over eighty percent in field scenarios. The system also provides a monitoring interface to visualize event trends and recommend early cultivation actions. These results highlight the potential of image-based approaches to accelerate detection, reduce pesticide overuse, and aid farmer decision-making. Future efforts are planned to improve generalizability through data augmentation, additional disease classes, and environmental sensor integration to enrich the diagnostic context. The field implementation is designed to be cost-effective, support offline modes, and be user-friendly for smallholders; initial trials demonstrate fast inference times and a simple workflow for routine, daily monitoring at a family farm scale.
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