Transfer-learning CNNs for Robust plant-health classification: a tomato and rose smart-farming case study

This research introduces an advanced image classification model for the early detection and precise identification of diseases and pests affecting tomato and rose plants.The study evaluates three CNN-based architectures: a custom baseline CNN specifically developed for this investigation, and fine-tuned versions of two established pretrained models, ResNet18 and GoogLeNet.Plant image datasets sourced from AIHub provided standardized, comprehensive training and evaluation data.Performance was rigorously assessed using accuracy, macro F1-score, and weighted F1-score, offering a robust evaluation under class-imbalanced conditions typical in agricultural contexts.GoogLeNet achieved the best performance on the tomato dataset (accuracy: 0.9941, macro F1-score: 0.9615, weighted F1-score: 0.9941), while ResNet18 excelled on the rose dataset (accuracy: 0.9966, macro F1-score: 0.9726, weighted F1-score: 0.9966).The findings provide valuable guidance on selecting effective deep learning models for early detection and classification of agricultural diseases and pests, laying a foundation for intelligent diagnostic systems in smart farming environments.