Comparison of Tea Leaf Disease Classification Using SVM with MobileNetV2 and MobileNetV3-Small Feature Extractors

Abstrak

Tea is a strategic plantation commodity that serves as a major source of income for millions of rural families. However, its production is often threatened by devastating pests and diseases. Accurate and timely classification of diseases such as brown blight, gray blight, and tea algal leaf spot is crucial for maintaining crop quality. Traditional identification methods often involve observer subjectivity and require significant time. Although Convolutional Neural Networks (CNNs) have demonstrated effectiveness in automatic recognition, their application on mobile devices is often limited by high computational demands. Previous studies in the tea domain that use MobileNet as a feature extractor combined with an SVM classifier are still limited. Therefore, this study evaluates the implementation of this hybrid model for tea leaf disease classification. This study compares two models: MobileNetV2-SVM and MobileNetV3-Small-SVM, using the TeaLeafBD dataset. Empirical testing shows that both architectures achieve very comparable classification performance, with accuracy rates of 75.3% for MobileNetV2 and 75.1% for MobileNetV3-Small. Despite marginal differences in accuracy, the MobileNetV3-Small-SVM hybrid offers a lower computational footprint, reducing computational load by approximately fivefold and model size by more than half. These findings indicate that the MobileNetV3-Small-SVM architecture provides a favorable balance between recognition stability and resource efficiency. Consequently, this hybrid approach is a viable candidate for the development of on-site tea leaf disease diagnostic tools on resource-constrained mobile devices.