Publications by authors named "Vaibhav Kumar Singh"
The global wheat production faces significant challenges due to major rust-causing fungi, namely f. sp. , , and f.
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- This paper explores using Deep Learning and Visual Question Answering systems to improve the detection of wheat rust, a serious disease affecting wheat crops worldwide.
- It introduces the WheatRustDL2024 dataset, consisting of nearly 8,000 images of healthy and infected wheat leaves, designed to train models for accurate and quick disease diagnosis.
- The researchers achieved a high accuracy of 97.69% with a fine-tuned ResNet model and utilized techniques like BLIP to enhance the model’s ability to process images and text, resulting in more relevant diagnostic answers.
This study evaluates the biocontrol efficacy of three bacterial strains DTPF-3, DTBA-11, and DTBS-5) and two fungal strains ( Pusa-5SD and An-27) antagonists, along with their combinations at varying doses (5.0, 7.5, and 10.
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- * The study found that isolate P. lilacinum 6887 effectively inhibited up to 97.55% of M. incognita egg hatching at higher concentrations, with several other isolates also showing strong results.
- * Gas chromatography-mass spectrometry revealed seven nematicidal compounds in these fungi, with P. lilacinum 6553 containing potent fatty acids that caused significant juvenile mortality in nematodes, indicating potential for natural pest control strategies
Background: The coronavirus disease 2019 is a serious and highly contagious disease caused by infection with a newly discovered virus, named severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2).
Objective: A Computer Aided Diagnosis (CAD) system to assist physicians to diagnose Covid-19 from chest Computed Tomography (CT) slices is modelled and experimented.
Methods: The lung tissues are segmented using Otsu's thresholding method.