Indian Journal of Agricultural Research
SCOPUSWeb of Science
  • Year: 2025
  • Volume: 59
  • Issue: 9

Transfer Learning-based Areca Nut (Areca catechu) Disease Detection using CNN and SVM Approaches with ResNet-50 for Improved Deep Learning Performance

  • Author:
  • N.S. Vidhya Shree1,*, Rajarajeswari Subramanian2, G.N. Basavaraj3
  • Total Page Count: 10
  • Page Number: 1385 to 1394

1Siddaganga Institute of Technology, Visvesvaraya Technological University, Belagavi-590 018, Karnataka, India

2MS Ramaiah Institute of Technology, Visvesvaraya Technological University, Belagavi-590 018, Karnataka, India

3BMS Institute of Technology, Visvesvaraya Technological University, Belagavi-590 018, Karnataka, India

*Corresponding Author: N.S. Vidhya Shree, Siddaganga Institute of Technology, Visvesvaraya Technological University, Belagavi-590 018, Karnataka, India, Email: vidhyashree014@gmail.com

Online published on 20 February, 2026.

Abstract

Fruit rot among Arecanut (Areca catechu) cultivation presents severe risks to agricultural yield together with product quality and agricultural profit margins. The timely detection of diseases along with accurate detection needs to be performed for minimizing economic losses and enabling sustainable agriculture systems.

The study implements deep learning through transfer learning of the pre-trained ResNet-50 model. A method flows from ICAR Hirehalli proprietary data processing through python software coding to Keras and TensorFlow implementation for model training. In addition to the assessment the study evaluated CNN along with SVM for traditional benchmarking purposes.

The efficacy of ResNet-50 during training reached 98% accuracy while validation accuracy settled at 92.76% surpassing both CNN with 90.83% accuracy and SVM with 89.95% accuracy. After training the model reached a substantial loss level of 0.2 which proved learning efficiency as validation loss settled at 0.3 indicating robust generalization.

Keywords

Agricultural automation disease classification, Convolutional neural networks (CNN), Crop disease detection, Deep learning, ICAR hirehalli dataset, Image classification, Machine learning, ResNet-50, Support vector machines (SVM), Transfer learning