PyriCane: A Large-Scale Benchmark Image Dataset for Smart Agriculture-Based Pyrilla Pest Object Detection, Infestation Identification, and Developmental Stage Classification in Sugarcane Crops

The proposed dataset is a large-scale annotated agricultural image collection designed to support hierarchical disease diagnosis and pest life-stage classification in sugarcane crops. The dataset contains 13,000 high-resolution field images acquired under real-world cultivation conditions, capturing variations in illumination, background complexity, leaf orientation, and infestation severity. These characteristics ensure robustness and practical applicability for computer vision and deep learning–based agricultural analysis. The dataset is structured to enable a two-stage hierarchical classification framework. In the first stage, images are categorized into Healthy and Diseased sugarcane classes, facilitating rapid crop health screening. Images identified as diseased are subsequently processed in the second stage, where the underlying cause of disease is determined by identifying the Pyrilla (Pyrilla perpusilla) infestation level, categorized into Egg, Nymph, and Adult stages. This hierarchical annotation strategy reflects real agricultural diagnostic workflows and reduces inter-class ambiguity commonly observed in flat multi-class classification schemes. All images are manually annotated by domain experts and undergo a quality-control process to ensure labeling accuracy and consistency. The dataset captures the complete life cycle of Pyrilla, enabling fine-grained pest severity assessment and supporting precision pest management strategies. The inclusion of multiple infestation stages allows researchers to investigate early, moderate, and severe disease progression scenarios. Owing to its scale and diversity, the dataset supports big data analytics and deep learning model development, including hierarchical classification, ensemble learning, and explainable AI techniques. The dataset is publicly available and intended to serve as a benchmark resource for research in plant disease detection, pest management, computer vision, and precision agriculture. 🔹 Optional Short Version (If Journal Has Word Limit) This study presents a large-scale annotated sugarcane image dataset comprising 13,000 field images, structured for hierarchical classification. The dataset supports two-stage analysis, where sugarcane plants are first classified as healthy or diseased, followed by identification of Pyrilla perpusilla infestation stages (egg, nymph, and adult). The dataset is designed to facilitate big data–driven deep learning research in agricultural disease diagnosis and pest management.