Images and CNN applications in smart agriculture

ABSTRACT

In recent years, the agricultural sector has undergone a revolutionary shift toward “smart farming”, integrating advanced technologies to strengthen crop health and productivity significantly. This paradigm shift holds profound implications for food safety and the broader economy. At the forefront of this transformation is deep learning, a subset of artificial intelligence based on artificial neural networks, has emerged as a powerful tool in detection and classification tasks. Specifically, Convolutional Neural Networks (CNNs), a specialized type of deep learning and computer vision models, demonstrated remarkable proficiency in analyzing crop imagery, whether sourced from satellites, aircraft, or terrestrial cameras. These networks often leverage vegetation indices and multispectral imagery to enhance their analytical capabilities. Such model contribute to the development of systems that could enhance agricultural. This review encapsulates the current state of the art in using CNNs in agriculture. It details the image types utilized within this context, including, but not limited to, multispectral images and vegetation indices. Furthermore, it catalogs accessible online datasets pertinent to this field. Collectively, this paper underscores the pivotal role of CNNs in agriculture and highlights the transformative impact of multispectral images in this rapidly evolving domain.

KEYWORDS:

• Deep learning
• multispectral images
• vegetation index
• agricultural datasets
• smart farming
• smart agriculture

Acknowledgments

We are grateful to Professor Jian-Yun Nie for his valuable feedback and comments.

Disclosure statement

The authors declare that they have no financial or non-financial competing interest. Funded by the European Union. Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union. Neither the European Union nor the granting authority can be held responsible for them.

Additional information

Funding

The work was funded by the European Union. The AI4AGRI project entitled “Romanian Excellence Center on Artificial Intelligence on Earth Observation Data for Agriculture” received funding from the European Union’s Horizon Europe research and innovation programme under the grant agreement no. 101079136. The LabEx CIMI (International Center of Mathematics and Computer Science in Toulouse) supported the visit of Professor Jian-Yun Nie. The Défi Région Occitanie “Observation de la Terre et Territoire en Transition” also supported this work.