# ICPR 2026 Competition on “Beyond Visible Spectrum: AI for Agriculture”


Thank you all for your incredible efforts and contributions to the Beyond Visible Spectrum: AI for Agriculture 2026 competition. We were truly impressed by the innovative approaches and hard work demonstrated by the community throughout this challenge!

Now that the private leaderboard has been finalized, we would like to officially congratulate our Top winners:

Action Required for Top 10 Teams:
To proceed with the reward distribution, we request that the top teams send a detailed write-up of your final solution and methodology to our official email: robeson1010@gmail.com.

@xav0xgb0lgbm , @jasonkarpeles , @sasmitshashwat , @shresthno1  @swordsing , @phuctuha , @came86 , @wqi876 , @heymanbd , @jasonpp10 , @dreamcatcher886 @xdhehe21 @newnguyen @ahmedberat19003 @ahmedzerr @notnguyen @tolinh 

Deadline: Please submit your solutions within 20 days (by March 20, 2026).

Please ensure your email includes the following:

Methodology: A detailed write-up explaining your approach and final solution.

Code & Weights: Your source code and the final model weights (providing a link to a GitHub repository or Google Drive is perfectly fine).

Personal Details: Your full real name and preferred contact information. (Note: This information is required for creating your certificates and for listing you as a co-author on the official Competition Report/Paper).

Next Steps: Our team will carefully review your methodologies to verify the results and begin drafting the official Competition Reports.

Thank you once again to everyone who participated and helped make this competition a massive success. We look forward to reading your solutions!


## Abstract

The ICPR 2026 Competition on “Beyond Visible Spectrum: AI for Agriculture” offers a unique opportunity for researchers to advance computer vision techniques in agricultural crop disease monitoring. Building on a successful series of competitions from 2022 to 2025, which attracted over 1000 participants from over 10 countries across Europe, Asia, and North America.

The challenge focuses on developing innovative deep learning algorithms using extensive multi/hyperspectral and satellite remote sensing datasets across two main tasks:

*   **Task 1: Automated Multimodal Crop Disease Diagnosis from Multimodal Remote Sensing Imagery**: Participants will be provided with RGB, multispectral (G/R/RE/NIR), and hyperspectral imagery. They will be encouraged to develop multimodal models capable of adapting to remote sensing data of varying spectral and spatial information to classify the data into three categories: 'rust,' 'healthy,' and 'other.'
*   **Task 2: Boosting Automatic Crop Diseases Classification using Sentinel Satellite Data and Self-Supervised Learning (SSL)**: Addressing the critical issue of limited labelled data in agriculture, this task requires participants to employ self-supervised learning (SSL) to learn meaningful representations from large, unlabelled Sentinel satellite datasets before fine-tuning on limited labelled data.

The primary goal is to enhance the accuracy and efficiency of identifying crop diseases, contributing to significant advancements in precision farming and sustainable agricultural practices to support global food security.


### Background and Task
Remote sensing technology offers continuous global monitoring of vast agricultural areas in real-time, enabling early identification and mitigation of threats. Advances in digital imaging have led to various image-based methods for automated plant management, showing great potential for crop disease diagnosis using RGB, thermal, multi-spectral, and hyperspectral sensors. Hyperspectral imagery (HSI), with its numerous narrow spectral bands, provides detailed spectral–spatial information, offering enhanced diagnostic accuracy for disease infestation. However, the high data dimensionality of HSI can also present challenges, leading to research into new methods like super-resolution and automated detection models.

Deep learning methods have shown promising results in remote sensing imagery analysis. Identifying relevant spectral features and extracting meaningful representations from remote sensing data are crucial for effective deep learning. However, the high number of spectral bands and complex relationships in remote sensing images pose significant challenges for feature selection and extraction, necessitating the development of new deep learning algorithms.

Building on our research in precision agriculture and extensive experience, we are hosting The ICPR 2026 Competition on “Beyond Visible Spectrum: AI for Agriculture.” This is our fifth consecutive year running this competition. We have seen a remarkable and sustained increase in community engagement, demonstrating the critical importance of this research area.


## Competition Schedule
*   **Launch Date**: December 15, 2025
*   **Submission Opening**: December 30, 2025
*   **Deadline for Competition Participants**: March 1st, 2026
*   **Winner Announcement**: March 15, 2026
*   **Initial Submission of Competition Reports Deadline**: April 1st, 2026
*   **Presentation at ICPR Conference**: August 17–21, 2026