Modern agriculture increasingly relies on technologies that enhance farmers' efficiency and economic growth. One challenge is the accurate identification of disease-affected plants, whose characteristics like structure, size, texture, and color can vary significantly. While there are existing methods to detect and classify these diseases, challenges such as image noise, hyper-parameter selection, and over-fitting can impede prediction accuracy. This paper introduces a hybrid fruit fly and simulated annealing-optimized Faster R-CNN (FS-FRNet) for improved plant leaf disease identification and classification. Our novel FS-FRNet method integrates a Wiener filter for de-noising and a super-resolution method to enhance image quality. By hybridizing the fruit fly optimization algorithm and simulated annealing, the Faster R-CNN's hyper-parameter issues are addressed, and the convergence rate is improved. We applied the FS-FRNet to identify and classify tomato plant diseases like early blight, yellow leaf curl, Septoria leaf, mosaic virus, and late blight. Experimental outcomes on the Plant Village dataset show that our method outperforms existing techniques, achieving 98.3% accuracy, 98.04% precision, and 98.11% recall, thus confirming its efficacy for reliable detection of tomato plant leaf diseases.