AI-Driven Image Annotation for Plant Disease Detection Using Google Cloud Vision Platform

Sabeetha Saraswathi S; Raju V; Dhanamathi A; Chitra J; Chandrasekar V; Rekha M; Thiruppathy Kesavan V

doi:10.52756/ijerr.2024.v46.008

Authors

Sabeetha Saraswathi S Faculty of Computer Science & Engineering, SRM TRP Engineering College, Trichy – Chennai Highway, Irungalur - 621105, Tamil Nadu, India https://orcid.org/0000-0002-8231-653X
Raju V Faculty of Computer Science & Engineering, Dhanalakshmi Srinivasan University, Samayapuram, Tiruchirappalli-621112, Tamil Nadu, India
Dhanamathi A Faculty of Computer Science & Engineering, Roever Engineering College, Perambalur- 621212, Tamil Nadu, India https://orcid.org/0009-0002-1133-7437
Chitra J Faculty of Artificial Intelligence and Data Science, K.Ramakrishnan College of Engineering, Samayapuram - Kariyamanickam Rd, Tamil Nadu 621112, India https://orcid.org/0009-0008-4705-6227
Chandrasekar V Faculty of Computer Science and Engineering, Presidency University, Bangalore-560064, Karnataka, India
Rekha M Faculty of Electronics and Communication Engineering, Dhanalakshmi Srinivasan Engineering College, Perambalur-621212, Tamil Nadu, India https://orcid.org/0000-0001-9998-983X
Thiruppathy Kesavan V Faculty of Information Technology, Dhanalakshmi Srinivasan Engineering College, Perambalur– 621212, Tamil Nadu, India https://orcid.org/0000-0001-7763-3373

DOI:

https://doi.org/10.52756/ijerr.2024.v46.008

Keywords:

Plant disease, Deep Learning, Google Cloud, Annotation, Detection

Abstract

Enabling visual plant disease diagnosis through deep learning that analyses big data is essential to diagnose diseases quickly. It helps the farmers and enables them to treat early, reducing the crop losses needed for a sustainable increase in agriculture. Farmers’ losses were also reduced using these technologies. However, deep learning still has great potential for plant disease diagnosis, though many challenges are associated with it. For example, it requires large, annotated data sets of symptoms and processing resources. This study proposes a novel Cloud-based Image Annotation Plant Disease Detection (C-IAPDD), which employs cloud platforms such as Google Cloud Vision API for image annotation and plant disease detection. Instead of creating such datasets manually or using those non-annotated ones saved by farmers onto their mobile phones since sensors in the device can detect disease on a particular leaf whenever placed close to it. The proposed solution provides a connection to the Internet and offline as well. The ability of C-IAPDD to simplify large-scale envision dataset collection and annotation enables powerful deep-learning models. Using cloud infrastructure’s processing power and scalability makes this a highly efficient method of identifying plant diseases without compromising accuracy. Several simulation experiments have proved that C-IAPDD could recognize a wide range of plant diseases across different types of crops. This simulation shows that C-IAPDD performs better than other methods in precision, swiftness, and expandability. The results indicate that C-IAPDD may improve plant disease detection and control, leading to healthier harvests. These findings endorse I-CIAPDD for artificial intelligence in agriculture.

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