Tag: Anoop C. Patil

Beyond the black box: towards an open and citable software ecosystem in photonics published in Journal of Physics: Photonics

A centralized, peer-reviewed ecosystem in J. Phys. Photonics where human agents and/or AI agents co-develop validated, documented software. (Image from the manuscript.)
Beyond the black box: towards an open and citable software ecosystem in photonics
Anoop C Patil, Maciej Trusiak, Fei Xia, Liangcai Cao, Carlo Manzo and Giovanni Volpe
Journal of Physics: Photonics 8, 020201 (2026)
doi: 10.1088/2515-7647/ae68cd

This editorial draws attention to a major issue in photonics: important research software is often hidden, undocumented, and lost over time, making results hard to reproduce. The rise of AI-generated code increases this problem by adding more ‘black box’ systems. To address this, J. Phys. Photonics is introducing Software Articles, a new article type allowing researchers to publish, validate, and share their code as formal scientific outputs. This initiative aims to promote transparency, reproducibility, and proper credit for developers. Open and peer-reviewed software helps the community verify results, reduce duplication of effort, and build lasting tools, ensuring that computational methods become reliable, accessible, and integral to scientific progress.

Soft Matter Lab members present at SPIE Optics+Photonics conference in San Diego, 3-7 August 2025

The Soft Matter Lab participates to the SPIE Optics+Photonics conference in San Diego, CA, USA, 3-7 August 2025, with the presentations listed below.

Giovanni Volpe, who serves as Symposium Chair for the SPIE Optics+Photonics Congress in 2025, is a coauthor of the following invited presentations:

Giovanni Volpe will also be the reference presenter of the following Poster contributions:

Presentation by Anoop C. Patil at SPIE-ETAI, San Diego, 6 August 2025

In this work, we present an unsupervised deep learning framework using Variational Autoencoders (VAEs) to decode stress-specific biomolecular fingerprints directly from Raman spectral data across multiple plant species and genotypes. (Image by the Authors of the manuscript. A part of the image was designed using Biorender.com.)
From Spectra to Stress: Unsupervised Deep Learning for Plant Health Monitoring
Anoop C. Patil, Benny Jian Rong Sng, Yu-Wei Chang, Joana B. Pereira, Chua Nam-Hai, Rajani Sarojam, Gajendra Pratap Singh, In-Cheol Jang, and Giovanni Volpe
Date: 6 August 2025
Time: 10:30 AM – 11:00 AM
Place: Conv. Ctr. Room 4

Plants experience a wide variety of stresses, from light and temperature fluctuations to bacterial infections. Each stress has a biomolecular fingerprint, but detecting and interpreting these signatures across species can be challenging. This work presents a deep learning-based approach using Variational Autoencoders (VAEs) to uncover how plants respond to light stress, shade avoidance, temperature stress, and bacterial infection — all without requiring any human intervention in spectral processing. By encoding Raman spectral data into an intuitive latent space, this method automatically categorizes and visualizes stress-specific biomolecular shifts, offering a powerful, unsupervised tool for stress phenotyping in crops.

Reference:
Patil, A.C. et al. Deep-Learning Investigation of Vibrational Raman Spectra for Plant-Stress Analysis. arXiv preprint arXiv:2507.15772v1 (2025). URL https://arxiv.org/abs/2507.15772

Deep-Learning Investigation of Vibrational Raman Spectra for Plant-Stress Analysis on ArXiv

In this work, we present an unsupervised deep learning framework using Variational Autoencoders (VAEs) to decode stress-specific biomolecular fingerprints directly from Raman spectral data across multiple plant species and genotypes. (Image by the Authors of the manuscript. A part of the image was designed using Biorender.com.)
From Spectra to Stress: Unsupervised Deep Learning for Plant Health Monitoring
Anoop C. Patil, Benny Jian Rong Sng, Yu-Wei Chang, Joana B. Pereira, Chua Nam-Hai, Rajani Sarojam, Gajendra Pratap Singh, In-Cheol Jang, and Giovanni Volpe
ArXiv: 2507.15772

Detecting stress in plants is crucial for both open-farm and controlled-environment agriculture. Biomolecules within plants serve as key stress indicators, offering vital markers for continuous health monitoring and early disease detection. Raman spectroscopy provides a powerful, non-invasive means to quantify these biomolecules through their molecular vibrational signatures. However, traditional Raman analysis relies on customized data-processing workflows that require fluorescence background removal and prior identification of Raman peaks of interest-introducing potential biases and inconsistencies. Here, we introduce DIVA (Deep-learning-based Investigation of Vibrational Raman spectra for plant-stress Analysis), a fully automated workflow based on a variational autoencoder. Unlike conventional approaches, DIVA processes native Raman spectra-including fluorescence backgrounds-without manual preprocessing, identifying and quantifying significant spectral features in an unbiased manner. We applied DIVA to detect a range of plant stresses, including abiotic (shading, high light intensity, high temperature) and biotic stressors (bacterial infections). By integrating deep learning with vibrational spectroscopy, DIVA paves the way for AI-driven plant health assessment, fostering more resilient and sustainable agricultural practices.

Anoop C. Patil joins the Soft Matter Lab

(Photo by Rashmi Anoop Patil.)
Anoop C. Patil joined the Soft Matter lab on March 1, 2024.

Anoop is a Senior Fellow at the Singapore-MIT Alliance for Research & Technology (SMART) center, based in the National University Singapore campus, Singapore.

He is working on computational analysis for precision agriculture at Disruptive & Sustainable Technologies for Agricultural Precision (DiSTAP), SMART, Singapore. As a part of this work, he is also working on the BRAPH-2 platform for spectral analysis applications at DiSTAP, SMART, and Temasek Life Sciences Laboratory (TLL), Singapore.