Aquatic Plant Disease Detection Using Deep Learning

• Main Authors: Aishwarya, R., Froila Stephanie, P.A, Yogitha, R., Srinivas, Chegoni Dhanusha
• Format: Conference Proceeding
• Language: English
• Subjects: Agriculture and Prediction; Aquatic; Crops; Deep learning; Detection; Distortion; Feature extraction; Features; Imaging; Plant disease; Plant diseases; Predictive models
• ISSN: 2575-7288
• DOI: 10.1109/ICACCS57279.2023.10113002

A subset of artificial intelligence is deep learning. With the advantages of feature extraction and automatic learning, it has received a lot of attention in recent years from both academic and professional circles. Natural language processing, voice processing, and image and video processing all make extensive use of it. In addition, it has developed into a hub for research in the area of aquatic plant protection, involving the study of pest ranges and the diagnosis of illnesses affecting aquatic plants. Deep learning may be used to recognise aquatic plant diseases in order to minimise the limitations associated with artificially selecting disease spot features, increase objectivity in the extraction of aquatic plant disease features, and quicken the pace of scientific and technical advancement. This review details the development of deep learning technology in recent years for identifying leaf diseases. Using deep learning and cutting-edge imaging techniques, In this work, we discuss the current patterns and challenges in the identification of aquatic plant leaf disease. We anticipate that this work will be a useful tool for scientists looking into the identification of aquatic plant diseases. We also talked about some of the current difficulties and issues that need to be fixed at the same time.