PLDD: a field-collected pineapple leaf disease dataset for deep learning-based plant health analysis

Pineapple (Ananas comosus) is a tropical fruit of great economic value. It is grown in several areas of India and makes a good contribution to horticultural production and farmer's income. Unfortunately, pineapples are very vulnerable to a variety of leaf diseases that reduce crop yield. Due to this, there is a pressing need for innovative ways to monitor plant health, such as deep learning-based disease detection using well-prepared datasets. The main aim of the dataset is to support research in the areas of condition analysis and early disease detection including machine learning models that reduce manual inspections, enable early detection, facilitate timely treatments. The dataset consists of 4476 original images collected from 200 unique pineapple leaf specimens, representing four classes. These specimens were collected in the field from Vazhakulam, Kerala, India, during June to July 2026. Fifty unique specimens were included for each class, with at least 20 distinct original images acquired per specimen. They were then pre-processed and augmented with image modifications, while the independent test images were retained without augmentation. The images were used to evaluate five transfer-learning-based deep learning architectures- Xception, InceptionV3, MobileNetV2, ResNet50, and EfficientNetB0, using a specimen-level 80:20 training-testing strategy. Researchers and experts can make great use of this dataset because of its enormity and quality. It can be used to develop and test techniques for leaf classification, disease detection, and severity estimation that will aid to the development of deep learning-enabled plant health assessment and precision farming systems. Such systems allow for more precise treatment of the affected area, reduce chemical use, and overall crop management.