The prevalence of tomato leaf diseases should be diagnosed in early-stage to prevent spoilage of the entire field. Manually checking tomato diseases consumes more time and is labor-intensive. In modern agriculture, machine and deep learning-based disease identification techniques have been developed to effectively classify diseases.Most of the existing methods are inappropriate for horticulture due to their incompetence in handling the complex backgrounds of the image. In this article, a novel segmentation and classification algorithm is proposed for detecting tomato leaf diseases with complex background interference based on leaf segmentation fuzzy CNN (LSFCNN) and ant colony-based mask RCNN (AC-MRCNN). Foremostly the collected images are annotated and enhanced for further processing. Then the novel LSFCNN is implemented to separate the tomato leaf in a complex background. For classification, AC-MRCNN is developed, which masks the disease spot and recognizes the diseases. Herein ant colony optimization algorithm is utilized to optimize the mask RCNN to increase the flow of information and gradients of the network. Over 14,817 uniform and complex background images are collected to train the model. The proposed method is profoundly effective for quite challenging background leaf disease classification, with an accuracy of 97.66% of eight diseases and one healthy class.
Achieving increase in spectral efficiency has always been a major aspect in communication systems. Our work emphasises the application of dual polarized modulation in a wireless environment. Our main goal is to increase the throughput of the system. We aim to increase the spectral efficiency and throughput without an increment of radiated energy without any Channel State Information at Transmitter (CSIT) and feedback at the transmitter The proposed dual Polarized Modulation (DPMOD) scheme exploits the polarization diversity reducing the required SNR (Eb/N0) and adding an extra bit and achieves the same Bit Error Rate (BER) carrying extra information.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.