International Journal of Innovative Research in Computer and Communication Engineering

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TITLE PranaDhanya: An AI-Powered Portal for Crop Disease Diagnosis, Livestock Health Screening and Farm Decision Support
ABSTRACT Small and marginal farmers usually have to decide about crop disease, livestock illness, fertilizer and selling time without quick access to an expert. This paper presents PranaDhanya, a web portal that puts six machine-learning models behind one role-based interface for farmers, veterinary officers and administrators. Two ResNet9 convolutional networks screen leaf images (38 plant classes) and animal images (11 cattle, dog and goat classes); an XGBoost classifier recommends crops from soil and weather values; Random Forest models estimate crop yield, wholesale price and a suitable fertilizer. The models are served by a FastAPI microservice and consumed by a Spring Boot 3 backend with JWT security and a PostgreSQL database. On our held-out data the plant and animal image models reach 96.00% test accuracy, crop recommendation reaches 98.64–99.86% accuracy (F1 = 0.99), fertilizer prediction reaches 98.99–100% accuracy (F1 = 0.99), and the yield and price regressors reach R2 of 0.74–0.85 (MAE 1.12 t/ha) and 0.77–0.85 (MAE Rs. 71 per quintal) respectively. We also discuss where the system still falls short, especially for field photographs taken against cluttered backgrounds.
AUTHOR DINESH KUMAR M, DHANUSH M, SHASHANK M ARADHYA, ADITHYA S, KUSHAL R Professor, Department of Information Science and Engineering, Rajarajeswari College of Engineering, Bengaluru, Karnataka, India Student, Department of Information Science and Engineering, Rajarajeswari College of Engineering, Bengaluru, Karnataka, India
VOLUME 190
DOI DOI: 10.15680/IJIRCCE.2026.1411006
PDF pdf/6_PranaDhanya An AI-Powered Portal for Crop Disease Diagnosis, Livestock Health Screening and Farm Decision Support.pdf
KEYWORDS
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