Background: Accurate plant disease detection is essential for maintaining agricultural productivity and sustainability. With the rise of machine learning techniques, comparing deep learning models such as MobileNetV3Small with traditional machine learning models like Support Vector Machines (SVM) has become increasingly important in improving plant disease classification accuracy. 

Objectives: This research aims to compare the performance of SVM and MobileNetV3Small models for the classification of plant diseases, focusing specifically on tomato and watermelon leaf conditions. The goal is to evaluate both models in terms of classification accuracy, interpretability and efficiency. 

Methods: The study employs two classification models which are SVM, a traditional machine learning algorithm and MobileNetV3Small, a deep learning model. A dataset comprising images of healthy and diseased tomato and watermelon leaves is used. The performance of both models is assessed using metrics such as accuracy, precision, recall and F1 score. Additionally, model interpretability is evaluated through techniques like feature importance analysis and visualization. 

Results: The MobileNetV3Small model outperforms the SVM model in terms of classification accuracy, achieving higher precision and recall. The deep learning model demonstrates better generalization on test data, while the SVM model offers greater interpretability due to its simpler architecture. 

Conclusions: While MobileNetV3Small provides superior performance in terms of accuracy, SVM remains a practical option where model interpretability is essential. This comparison highlights the trade-offs between deep learning and traditional machine learning approaches in plant disease classification, offering insights into their respective strengths and applications in agricultural settings.