#Important Information


Please contact me (robeson1010@gmail.com) to verify the your results on our private dataset. We will award and certification after evaluation at August 18, 2024.

@Roberto Chang S
@VietNamese
@early_bird
@maimimib
@DieNeuronaleCrew
@NCKU_ACVLAB
@Khao Cha
@Jason Z88
@AgMod
@Roshan Rateria


# Overview

The International Conference on Pattern Recognition (ICPR) 2024 Competition on “**Beyond Visible Spectrum: AI for Agriculture**” presents  an exciting opportunity for researchers and practitioners to advance computer vision techniques in agricultural crop disease monitoring. By leveraging the vast multi/hyper spectral remote sensing image datasets, participants are encouraged to develop innovative deep learning algorithms. The primary aim is to enhance the accuracy and efficiency of identifying crop diseases, thereby contributing to sustainable agricultural practices and global food security.

Participants in this challenge will have the chance to contribute to significant advancements in precision farming and crop monitoring techniques. By improving the deep learning vision models, the competition aims to drive forward the capabilities of agricultural technology, making it more efficient, accurate, and sustainable. The ICPR 2024 Competition on “Beyond Visible Spectrum” is not just a competition; it is a call to action for the computer research community to bring about transformative changes in how agricultural data is analyzed and utilized, paving the way for smarter, more sustainable farming practices that can benefit communities worldwide.


Early accurate diagnosis and quantification of crop diseases are crucial for precision management by allowing targeted interventions at the field level. Effective diagnosis and control measures are dependent on symptom recognition and severity assessment (in the vast majority of cases, diseases exhibit a range of visual symptoms, e.g. coloured spots or streaks). At present, these are mainly achieved by visual observation and estimation.  

Recent advancements in digital imaging have facilitated the development of a variety of image-based methods for plant management that are capable of performing automated image acquisition and analysis. These methods have shown great potential for automated crop disease diagnosis based on images from RGB (red, green and blue), thermal, multispectral, and hyperspectral sensors.

Particularly, hyperspectral imagery (HSI), which contains more narrow spectral bands over a contiguous spectral range, provides detailed spectral–spatial information of the disease infestation and offers the potential to provide better diagnostic accuracy.

In this challenge, we will mainly focus on the accurate diagnosis of crop diseases from hyperspectral imagery (in this case fusarium head blight, FHB). The benchmark model for this competition is based on our published paper on remote sensing [1]. 

[1. A Deep Learning-Based Approach for Automated Yellow Rust Disease Detection from High-Resolution Hyperspectral UAV Images](https://www.mdpi.com/2072-4292/11/13/1554)

# Competition Schedule
 
**Launch Date:** April 15, 2024

**Submission Opening:** April 15, 2024

**Submission Deadline:** July 21, 2024

**Submission of Competition Reports Deadline:**  August 18, 2024

# Awards

All submissions before the deadline will receive a participation certificate

The top three places of each task will be awarded prizes to the value of:


**1st Place:**	$300

**2nd Place:**	$200

**3rd Place:**	$100




# Challenge keywords
Remote sensing; Hyperspectral; Crop disease; Deep learning ; Fusarium Head Blight


# Workshop/conference

This challenge is part of the conference on "The International Conference on Pattern Recognition (ICPR) (2024)".