Tomato Plant Leaf Disease Classification Using Deep Learning

Plant diseases pose a significant threat to global food production and require early identification to ensure global food security and safeguard the economic interests of agriculture stakeholders. Practical implementation in agriculture requires fast, lightweight and accurate models for edge devices. This research presents a tomato leaf disease classification approach using a fast and lightweight pre-trained CNN architecture designed for efficiency. The method incorporates resampling techniques to address the data class imbalance, while pre-processing steps such as resizing, and augmentation enhance model performance. The transfer learning-based NASNetMobile model is trained and evaluated using the plant village dataset, containing nine disease classes and a healthy class. Performance assessment focuses on lightweight characteristics and classification accuracy of the model. Results demonstrate the effectiveness of the approach in balancing lightweight characteristics and accuracy, offering the potential for extending disease classification to other plant species. This research contributes to the development of low-end devices for easy disease identification, benefiting global food security and agricultural stakeholders’ economic well-being.