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.