From General to Specialized: The Need for Foundational Models in Agriculture
- URL: http://arxiv.org/abs/2507.05390v2
- Date: Sat, 26 Jul 2025 04:58:45 GMT
- Title: From General to Specialized: The Need for Foundational Models in Agriculture
- Authors: Vishal Nedungadi, Xingguo Xiong, Aike Potze, Ron Van Bree, Tao Lin, Marc RuĆwurm, Ioannis N. Athanasiadis,
- Abstract summary: Food security remains a global concern as population grows and climate change intensifies.<n>Recent advances in foundation models have demonstrated remarkable performance in remote sensing and climate sciences.<n>Their application in challenges related to agriculture-such as crop type mapping, crop phenology estimation, and crop yield estimation-remains under-explored.
- Score: 4.34470351323835
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Food security remains a global concern as population grows and climate change intensifies, demanding innovative solutions for sustainable agricultural productivity. Recent advances in foundation models have demonstrated remarkable performance in remote sensing and climate sciences, and therefore offer new opportunities for agricultural monitoring. However, their application in challenges related to agriculture-such as crop type mapping, crop phenology estimation, and crop yield estimation-remains under-explored. In this work, we quantitatively evaluate existing foundational models to assess their effectivity for a representative set of agricultural tasks. From an agricultural domain perspective, we describe a requirements framework for an ideal agricultural foundation model (CropFM). We then survey and compare existing general-purpose foundational models in this framework and empirically evaluate two exemplary of them in three representative agriculture specific tasks. Finally, we highlight the need for a dedicated foundational model tailored specifically to agriculture.
Related papers
- AI in Agriculture: A Survey of Deep Learning Techniques for Crops, Fisheries and Livestock [77.95897723270453]
Crops, fisheries and livestock form the backbone of global food production, essential to feed the ever-growing global population.<n> Addressing these issues requires efficient, accurate, and scalable technological solutions, highlighting the importance of artificial intelligence (AI)<n>This survey presents a systematic and thorough review of more than 200 research works covering conventional machine learning approaches, advanced deep learning techniques, and recent vision-language foundation models.
arXiv Detail & Related papers (2025-07-29T17:59:48Z) - A Comprehensive Review of Diffusion Models in Smart Agriculture: Progress, Applications, and Challenges [19.536255277516005]
Diffusion models have demonstrated potential in agricultural image processing, data augmentation, and remote sensing analysis.<n>Compared to traditional generative adversarial networks (GANs), diffusion models exhibit greater training stability and superior image generation quality.<n>This paper reviews recent advancements in the application of diffusion models within agriculture, focusing on their roles in crop disease and pest detection, remote sensing image enhancement, crop growth prediction, and agricultural resource management.
arXiv Detail & Related papers (2025-07-24T12:52:32Z) - KG-FGNN: Knowledge-guided GNN Foundation Model for Fertilisation-oriented Soil GHG Flux Prediction [8.025242423352509]
Precision soil greenhouse gas (GHG) flux prediction is essential in agricultural systems for assessing environmental impacts, developing emission mitigation strategies and promoting sustainable agriculture.<n>Due to the lack of advanced sensor and network technologies on majority of farms, there are challenges in obtaining comprehensive and diverse agricultural data.<n>This research proposes a knowledge-guided graph neural network framework that addresses the above challenges by integrating knowledge embedded in an agricultural process-based model and graph neural network techniques.
arXiv Detail & Related papers (2025-06-18T21:40:24Z) - Bridging Domain Gaps in Agricultural Image Analysis: A Comprehensive Review From Shallow Adaptation to Deep Learning [17.455138644418618]
This paper investigates how Domain Adaptation techniques can address challenges by improving cross-domain transferability in agricultural image analysis.<n>The review systematically summarizes recent advances in DA for agricultural imagery, focusing on applications such as crop health monitoring, pest detection, and fruit recognition.
arXiv Detail & Related papers (2025-06-06T10:52:10Z) - Multimodal Agricultural Agent Architecture (MA3): A New Paradigm for Intelligent Agricultural Decision-Making [32.62816270192696]
Modern agriculture faces dual challenges: optimizing production efficiency and achieving sustainable development.<n>To address these challenges, this study proposes an innovative textbfMultimodal textbfAgricultural textbfAgent textbfArchitecture (textbfMA3)<n>This study constructs a multimodal agricultural agent dataset encompassing five major tasks: classification, detection, Visual Question Answering (VQA), tool selection, and agent evaluation.
arXiv Detail & Related papers (2025-04-07T07:32:41Z) - Agri-LLaVA: Knowledge-Infused Large Multimodal Assistant on Agricultural Pests and Diseases [49.782064512495495]
We construct the first multimodal instruction-following dataset in the agricultural domain.<n>This dataset covers over 221 types of pests and diseases with approximately 400,000 data entries.<n>We propose a knowledge-infused training method to develop Agri-LLaVA, an agricultural multimodal conversation system.
arXiv Detail & Related papers (2024-12-03T04:34:23Z) - Anticipatory Understanding of Resilient Agriculture to Climate [66.008020515555]
We present a framework to better identify food security hotspots using a combination of remote sensing, deep learning, crop yield modeling, and causal modeling of the food distribution system.
We focus our analysis on the wheat breadbasket of northern India, which supplies a large percentage of the world's population.
arXiv Detail & Related papers (2024-11-07T22:29:05Z) - Generating Diverse Agricultural Data for Vision-Based Farming Applications [74.79409721178489]
This model is capable of simulating distinct growth stages of plants, diverse soil conditions, and randomized field arrangements under varying lighting conditions.
Our dataset includes 12,000 images with semantic labels, offering a comprehensive resource for computer vision tasks in precision agriculture.
arXiv Detail & Related papers (2024-03-27T08:42:47Z) - Domain Generalization for Crop Segmentation with Standardized Ensemble Knowledge Distillation [42.39035033967183]
Service robots need a real-time perception system that understands their surroundings and identifies their targets in the wild.
Existing methods, however, often fall short in generalizing to new crops and environmental conditions.
We propose a novel approach to enhance domain generalization using knowledge distillation.
arXiv Detail & Related papers (2023-04-03T14:28:29Z) - Estimating Crop Primary Productivity with Sentinel-2 and Landsat 8 using
Machine Learning Methods Trained with Radiative Transfer Simulations [58.17039841385472]
We take advantage of all parallel developments in mechanistic modeling and satellite data availability for advanced monitoring of crop productivity.
Our model successfully estimates gross primary productivity across a variety of C3 crop types and environmental conditions even though it does not use any local information from the corresponding sites.
This highlights its potential to map crop productivity from new satellite sensors at a global scale with the help of current Earth observation cloud computing platforms.
arXiv Detail & Related papers (2020-12-07T16:23:13Z) - Agriculture-Vision: A Large Aerial Image Database for Agricultural
Pattern Analysis [110.30849704592592]
We present Agriculture-Vision: a large-scale aerial farmland image dataset for semantic segmentation of agricultural patterns.
Each image consists of RGB and Near-infrared (NIR) channels with resolution as high as 10 cm per pixel.
We annotate nine types of field anomaly patterns that are most important to farmers.
arXiv Detail & Related papers (2020-01-05T20:19:33Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.