Gangadevi E, Soufiane Ben Othman, Balusamy Balamurugan, Khan Firoz, Getahun Masresha
Department of Computer Science, Loyola College, Chennai, India.
Applied College, King Faisal University, Al-Ahsa, 31982, Saudi Arabia.
Sci Rep. 2025 May 13;15(1):16571. doi: 10.1038/s41598-025-01466-5.
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.
现代农业越来越依赖于提高农民效率和经济增长的技术。一个挑战是准确识别受病害影响的植物，其结构、大小、质地和颜色等特征可能有很大差异。虽然现有的方法可以检测和分类这些病害，但图像噪声、超参数选择和过拟合等挑战会阻碍预测准确性。本文介绍了一种混合果蝇和模拟退火优化的更快区域卷积神经网络（FS-FRNet），用于改进植物叶片病害的识别和分类。我们新颖的FS-FRNet方法集成了用于去噪的维纳滤波器和用于提高图像质量的超分辨率方法。通过将果蝇优化算法和模拟退火相结合，解决了更快区域卷积神经网络的超参数问题，并提高了收敛速度。我们应用FS-FRNet来识别和分类番茄植株病害，如早疫病、黄叶卷曲病、叶斑病、花叶病毒病和晚疫病。在植物村数据集上的实验结果表明，我们的方法优于现有技术，准确率达到98.3%，精确率达到98.04%，召回率达到98.11%，从而证实了其在可靠检测番茄植株叶片病害方面的有效性。
