[LGPR] Publication Success Series- Dr Sujatha Kesavan

Dr Sujatha Kesavan, Posdoctoral Candidate LGPR. Dean Dr. M.G.R. Educational and Research Institute

International Journal of Basic and Applied Sciences 14 (5) (2025) 327-334

Journal Indexes: CiteScore 0.2

Indexing: SCOPUS

Aims and Scope

The International Journal of Basic and Applied Sciences (IJBAS) aims to advance scientific knowledge by publishing high-quality, original research in basic and applied sciences. The journal promotes interdisciplinary collaboration, global scientific dialogue, and innovation, fostering a culture of discovery and practical application.

Scope

IJBAS welcomes submissions in physics, chemistry, biology, mathematics, engineering, environmental sciences, computer science, health sciences, and multidisciplinary studies. The journal prioritizes rigorous, impactful research that contributes to scientific understanding and addresses real-world challenges.

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A Journey on The Exploration of Village Plant Dataset Using ‎Machine Learning Models 

https://doi.org/10.14419/jk48hn96

K. Sujatha

Department of Computer Science Engineering, Lincoln University College, Petaling Jaya, Malaysia

https://orcid.org/0000-0003-2417-0760

Ganesh Khekare

School of Computer Science & Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu, India

https://orcid.org/0000-0002-1687-4699

Divya Midhunchakkaravarthy

Centre of Postgraduate Studies, Lincoln University College, Petaling Jaya, Malaysia

https://orcid.org/0000-0002-8044-1863

M. Malathi

Department of ECE, Rajalakshmi Institute of Technology, Chennai, India

V. Srividhya

Department of EEE, Meenakshi College of Engineering, Chennai, Tamil Nadu, India

N.P.G. Bhavani

Department of ECE, Saveetha Institute of Medical and Technical Sciences, Chennai, India

Abstract

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This article is coined for investigating the Village Plant dataset. Many researchers worldwide, carrying out their research in ‎the domain of agriculture, are dependent on this open source dataset. A plant is vulnerable to several infirmities during its period of growth. ‎Detection of the plant’s ill health and monitoring the environmental parameters is the most challenging task in agriculture. Plant disease epidemic may have a significant effect on crop production, reducing the country’s wealth. Early diagnosis of the occurrence of ill health in plants ‎and the remedies are feasible using Artificial Intelligence (AI). Currently, methods like Deep Learning (DL) algorithms, machine vision ‎techniques, and robotics play an important role in monitoring plant diseases and the growth status. This dataset contains multi-fold in-‎information about the plants. They include the normal and diseased images of plants like Bell Pepper, Tomato, Cucumber, and Potato. An Internet ‎of Things (IoT) based plant data collection and integration system will provide data for this research, which optimizes the feature set through ‎Ant Colony Optimization (ACO) for improving prediction in feature selection using deep learning models like DenseNet, ResNet 50, ‎VGG 19, and Long Short-Term Memory (LSTM) networks, which in turn enhances plant productivity with advances in AI-driven agricul‎tural diagnostics for plant stress prediction‎.

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Cascaded CNN For Early Detection of Plant Diseases Machine Vision Techniques in Smart Agriculture

 https://doi.org/10.14419/b9c87e95

International Journal of Basic and Applied Sciences 14 (5) (2025) 55-66

Sujatha Kesavan

Department of Computer Science Engineering, Lincoln University College, Petaling Jaya, Malaysia

Ganesh Khekare

School of Computer Science & Engineering, Vellore Institute of Technology, Vellore, Tamilnadu, India

https://orcid.org/0000-0002-1687-4699

Divya Midhunchakkaravarthy

Centre of Postgraduate Studies, Lincoln University College, Petaling Jaya, Malaysia

https://orcid.org/0000-0002-8044-1863

N.P.G. Bhavani

Department of ECE, Saveetha Institute of Medical and Technical Sciences, Chennai, India

M. Malathi

Department of ECE, Rajalakshmi Institute of Technology, Chennai, India

Abstract

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Plant disease detection is important because plants absorb the greenhouse gases emitted by industries and vehicles and liberate oxygen, ‎to extend human life in the Universe. The biodiversity chain is fully reliant on plant growth and mainly purifies the air and makes it suitable for ‎respiration. Various diseases like Bacterial Spot (BS), Early Blight (EB), Late Blight, Mold (LBM), Leaf Spot (LS), Spider Mites (SM), ‎Target Spot (TS), and Yellow Leaf Curl (YLC) attack the plants, causing retardation in plant growth, thereby offering stress to the plants. ‎Plant stress reduces crop productivity and creates a deadly impact on the economy. Several studies have exhibited that the quality of ‎agricultural products is seriously affected due to plant stress. Plants are stressed due to many reasons like extended use of synthetic fertilizers-‎ers, soil nutrients, plant diseases, and the various physiological parameters like atmospheric temperature, sunlight, soil pH, and soil moisture ‎content, which may vary seasonally. The benchmark images of the leaves under normal conditions are gathered, and pre-processing is per-‎formed. Further, the feature extraction is done by various types of Convolutional Neural Network (CNN) models like Faster Region-based CNN (FR-CNN), R-CNN, Cascaded CNN, and compared with the standardized CNN model. An optimal solution is offered using ‎Artificial Intelligence (AI) for early-stage detection of chlorophyll content and as well as plant disease prediction. The important advantage ‎of AI AI-based detection approach is to identify the leaf area turning yellow or brown at the start with optimal accuracy‎.

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