Amirkabir University of Technology AUT Journal of Modeling and Simulation 2588-2953 58 2 2026 10 01 Data-Driven Modeling and UAV-Based Identification of Diseases in Solanum lycopersicum Leaves Using Machine Learning and Deep Learning Algorithms 6163 10.22060/miscj.2026.25104.5454 EN Lordwin Cecil Prabhaker CP Micheal Avadi Srinivasa Rao C U Department of Electronics and Communication Engineering, Bapatla Engineering College, Bapatla, Andrapradesh Nadana Ravishankar T Department of Data Science and Business Systems, School of Computing SRM Institute of Science and Technology, KTR, Chennai-603203. Mathan Kumar A VelTech MultiTech Dr. Rangarajan Dr. Sakunthala Engineering College, Chennai Journal Article 2025 11 13 Unmanned Aerial Vehicles (UAVs) equipped with environmental sensors and imaging systems provide an efficient platform for precision agriculture and early crop disease detection. This study presents a UAV-based intelligent framework for the identification of Solanum lycopersicum (tomato) leaf diseases by integrating RGB image analysis with real-time environmental sensing. A lightweight six-arm UAV was developed using a Raspberry Pi 3 controller, GPS, radio telemetry, RGB camera, and onboard sensors for temperature, humidity, rainfall, and gas concentration monitoring. The acquired RGB images and environmental sensor data were used to construct a multimodal dataset for disease classification. Three classification models, namely 2D Convolutional Neural Network (2D-CNN), Support Vector Machine (SVM), and Random Forest (RF), were trained and evaluated using a common testing dataset. The 2D-CNN achieved an accuracy of 69.10%, whereas the SVM improved the classification performance to 93.40% with a 35.17% relative improvement in accuracy over the 2D-CNN. The Random Forest classifier achieved the highest performance with an accuracy of 98.61%, precision of 99.40%, recall of 98.24%, and F1-score of 98.82%, representing a 42.71% improvement in accuracy compared with the 2D-CNN and a 5.58% improvement over the SVM. The results demonstrate that integrating UAV-based RGB imaging with environmental sensing significantly improves disease identification under field conditions. The proposed framework provides a reliable and cost-effective solution for early crop health assessment, reduces manual inspection effort, and supports precision agriculture through data-driven decision making.