[LGPR] Find a Funder Success Story- Dr Bobbinpreet Kaur

#findafunder #openaccess #Nature #ScientificReports #Q1 #SCI

Dr Bobbinpreet Kaur, Received full open access fee support under find the funder programme of worth 2590 USD.

We are delighted to share the first success story from our newly launched Find a Funder initiative. Through the collaborative efforts of LUCM, CREP, and Vibrasphere Technologies, we have successfully secured open access fee funding of USD 36,000 per candidate under the LGPR Programme.

Dr Bobbinpreet Kaur, LGPR candidate has received full open access fee support worth 2590 USD for her publication in the prestigious Scientific Reports Journal.

Scientific Reports, 15, 32296 (2025) | https://www.nature.com/srep/

Impact Factor: 3.9

Cite Score: 6.7

Scientific Reports is an open access journal publishing original research from across all areas of the natural sciences, psychology, medicine and engineering.

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Layout for citrus disease detection using deep learning.

Precision diagnosis of citrus leaf diseases using image enhancement and nonlinear fuzzy ranking ensemble approach NLFuRBe

Cite This Article:

Kaur, B., Gupta, S.K., Janarthan, M. et al. Precision diagnosis of citrus leaf diseases using image enhancement and nonlinear fuzzy ranking ensemble approach NLFuRBe. Sci Rep 15, 32296 (2025). https://doi.org/10.1038/s41598-025-16923-4

Bobbinpreet Kaur, Shashi Kant Gupta & Midhunchakkaravarthy Janarthan

Lincoln University College, Petaling Jaya-47301, Selangor, Malaysia

Deema Mohammed Alsekait

Department of Information Technology, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, 11671, Riyadh, Saudi Arabia.

Diaa Salama AbdElminaam

Faculty of Computers and Artificial Intelligence, Benha University, Benha, Egypt

Jadara Research Center, Jadara University, 21110, Irbid, Jordan

Abstract

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Citrus fruits, especially lemons, play a vital economic and nutritional role worldwide but are increasingly threatened by a wide range of diseases that diminish yield quality and quantity. Traditional manual and automated methods for disease detection requires domain expert, ample observation time, and is often ineffective during early infection stages. This paper presents a novel automated approach for the symptom based detection and classification of citrus leaf diseases using a nonlinear Fuzzy Rank-Based Ensemble (NL-FuRBE) methodology, enhanced by image quality improvement techniques. The study emphasizes the significance of timely disease diagnosis in citrus crops, which are vital for global food security and economic stability. The methodology begins with image quality enhancement through Vector-Valued Anisotropic Diffusion (VAD) and morphological filtering, evaluated using PSNR, SSIM, and NIQE metrics to ensure optimal visual clarity for classifier input. The core ensemble integrates three deep learning (DL) architectures–VGG19, AlexNet, and Xception–using a fuzzy rank-based scoring mechanism built on nonlinear transformations (exponential, tanh, and sigmoid functions) to address prediction uncertainty and model bias. A comprehensive dataset of lemon leaf diseases, consisting of 1354 images across nine classes, was utilized for training and evaluation. Experimental results using five-fold cross-validation demonstrate that the proposed model achieves superior performance with an average accuracy of 96.51%, outperforming conventional ensemble and state-of-the-art approaches. The results validate the proposed NL-FuRBE as an effective, automated, and cost-efficient tool for precision agriculture and early disease diagnosis in citrus farming.

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