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    ICPR 2024 Competition on Beyond Visible Spectrum: AI for Agriculture

    Han, L ORCID logoORCID: https://orcid.org/0000-0003-2491-7473, Huang, W, Zhang, X ORCID logoORCID: https://orcid.org/0000-0001-7844-593X, Dong, Y, Sobeih, T and Lin, Y (2025) ICPR 2024 Competition on Beyond Visible Spectrum: AI for Agriculture. In: Pattern Recognition. Competitions: 27th International Conference, ICPR 2024, Kolkata, India, December 1–5, 2024, Proceedings, Part XXXIV, pp. 46-61. Presented at 27th International Conference, ICPR 2024, 1 December 2024 –5 December 2024, Kolkata, India.

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    Abstract

    The 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 datasets, participants are encouraged to develop innovative machines/deep learning algorithms. The primary objective is to improve the accuracy and efficiency of crop disease identification, thereby fostering sustainable agricultural practices and facilitating global knowledge generation for enhanced food security. This challenge presents participants with the opportunity to make significant contributions to the advancement of precision farming and crop monitoring techniques. By improving the machines/deep learning models, the competition aims to drive forward the capabilities of agricultural technology, making it more efficient, accurate, and sustainable. A total of 190 people from more than 10 countries in Europe, Asia and North America entered the competition by the deadline date, with a total of 126 submissions of results. The rank1 team obtained an accuracy of 73.3% in crop disease classification task with hyperspectral dataset. In the self-supervised learning task, the rank1 team obtained 79.2% accuracy. We report the leader-boards of the competition in this report and present three innovative approaches of the participants. 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 analyze and utilized, paving the way for smarter, more sustainable farming practices that can benefit communities worldwide.

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