Title: Internet of things-based remote monitoring and classification of Spinacia oleracea leaf disease using deep learning approach

Authors: Swarna Prabha Jena; Sujata Chakravarty; Bijay Kumar Paikaray

Addresses: Department of ECE, Centurion University of Technology and Management, Odisha, India ' Department of CSE, Centurion University of Technology and Management, Odisha, India ' Centre for Data Science, Department of Computer Science and Engineering, Siksha 'O' Anusandhan (Deemed to be) University, Odisha, India

Abstract: Due to the change in the climatical conditions, there is a considerable impact on the plant's growth. Hence, a system with a model has been developed for monitoring Spinacia oleracea plant which has many health benefits. It will control, monitors and protect it from different disease-causing agents. Here the leafy plant was grown and quality has compared in both fields. The environmental sensors installed in the field continuously capture and stores in the database. The image data in the database are analysed using transfer learning methods, i.e., MobileNetV2, ResNet152V2, InceptionV3, DenseNet201, and VGG16. From experimental results, it has been found that MobileNetV2 has reached the highest accuracy of 95% compared to other models. Finally, web app was developed which will quickly identify and classify the occurrence of the diseases. It has been seen that Spinacia oleracea is better in growth, nutrient content, and disease-free when grown inside the polyhouse.

Keywords: spinach; internet of things; growth parameters; polyhouse; edge device; leaf disease.

DOI: 10.1504/IJWGS.2024.138597

International Journal of Web and Grid Services, 2024 Vol.20 No.2, pp.159 - 187

Received: 25 Jun 2023
Accepted: 23 Sep 2023
Published online: 14 May 2024 *

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