• Abhijeeth Chandra
• Prathvij
• Puneeth K
• Varun B
• Madhusudhan S,Daya Naik

Arecanut cultivation is a significant aspect of agriculture in many tropical regions. However, it is often threatened by various plant and seed diseases that affect yield and crop quality. This research introduces a deep learning-based framework, utilising a Residual Neural Network (ResNet) architecture, to enable early and accurate detection of arecanut diseases. The system analyses images of arecanut plants to identify infections and offers recommended treatments, helping farmers mitigate potential crop losses. This approach integrates conventional farming practices with advanced AI technologies, fostering sustainable agriculture.

Arecanut, Deep Learning, Convolutional Neural Networks, ResNet, Agriculture AI, Early Diagnosis.

"EARLY DETECTION AND MANAGEMENT OF ARECANUT PLANT AND SEED DISEASES", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.12, Issue 7, page no.649-653, July-2025, Available :http://www.jetir.org/papers/JETIRGX06124.pdf

"EARLY DETECTION AND MANAGEMENT OF ARECANUT PLANT AND SEED DISEASES", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.12, Issue 7, page no. pp649-653, July-2025, Available at : http://www.jetir.org/papers/JETIRGX06124.pdf