Causal machine learning for sustainable agroecosystems

• URL: http://arxiv.org/abs/2408.13155v1
• Date: Fri, 23 Aug 2024 15:25:50 GMT
• Title: Causal machine learning for sustainable agroecosystems
• Authors: Vasileios Sitokonstantinou, Emiliano Díaz Salas Porras, Jordi Cerdà Bautista, Maria Piles, Ioannis Athanasiadis, Hannah Kerner, Giulia Martini, Lily-belle Sweet, Ilias Tsoumas, Jakob Zscheischler, Gustau Camps-Valls,
• Abstract summary: Predictive machine learning (ML) is leveraged in sustainable agriculture for applications like yield prediction and weather forecasting. We propose causal ML, which merges ML's data processing with causality's ability to reason about change. We showcase causal ML through eight diverse applications that benefit stakeholders across the agri-food chain, including farmers, policymakers, and researchers.
• Score: 10.052864559362254
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In a changing climate, sustainable agriculture is essential for food security and environmental health. However, it is challenging to understand the complex interactions among its biophysical, social, and economic components. Predictive machine learning (ML), with its capacity to learn from data, is leveraged in sustainable agriculture for applications like yield prediction and weather forecasting. Nevertheless, it cannot explain causal mechanisms and remains descriptive rather than prescriptive. To address this gap, we propose causal ML, which merges ML's data processing with causality's ability to reason about change. This facilitates quantifying intervention impacts for evidence-based decision-making and enhances predictive model robustness. We showcase causal ML through eight diverse applications that benefit stakeholders across the agri-food chain, including farmers, policymakers, and researchers.

Related papers

• Application of Machine Learning in Agriculture: Recent Trends and Future Research Avenues [6.0460261046732455]
  Food production is a vital global concern and the potential for an agritech revolution through artificial intelligence (AI) remains largely unexplored. This paper presents a comprehensive review focused on the application of machine learning (ML) in agriculture, aiming to explore its transformative potential in farming practices and efficiency enhancement.
  arXiv  Detail & Related papers  (2024-05-23T17:53:31Z)
• Overcoming LLM Challenges using RAG-Driven Precision in Coffee Leaf Disease Remediation [0.0]
  This research introduces an innovative AI-driven precision agriculture system, leveraging YOLOv8 for disease identification and Retrieval Augmented Generation (RAG) for context-aware diagnosis. The system integrates sophisticated object detection techniques with language models to address the inherent constraints associated with Large Language Models (LLMs)
  arXiv  Detail & Related papers  (2024-05-02T14:19:25Z)
• Intelligent Agricultural Greenhouse Control System Based on Internet of Things and Machine Learning [0.0]
  This study endeavors to conceptualize and execute a sophisticated agricultural greenhouse control system grounded in the amalgamation of the Internet of Things (IoT) and machine learning. The envisaged outcome is an enhancement in crop growth efficiency and yield, accompanied by a reduction in resource wastage.
  arXiv  Detail & Related papers  (2024-02-14T09:07:00Z)
• Climate Change Impact on Agricultural Land Suitability: An Interpretable Machine Learning-Based Eurasia Case Study [94.07737890568644]
  As of 2021, approximately 828 million people worldwide are experiencing hunger and malnutrition. Climate change significantly impacts agricultural land suitability, potentially leading to severe food shortages. Our study focuses on Central Eurasia, a region burdened with economic and social challenges.
  arXiv  Detail & Related papers  (2023-10-24T15:15:28Z)
• Identifiable Latent Polynomial Causal Models Through the Lens of Change [82.14087963690561]
  Causal representation learning aims to unveil latent high-level causal representations from observed low-level data. One of its primary tasks is to provide reliable assurance of identifying these latent causal models, known as identifiability.
  arXiv  Detail & Related papers  (2023-10-24T07:46:10Z)
• Causal machine learning for single-cell genomics [94.28105176231739]
  We discuss the application of machine learning techniques to single-cell genomics and their challenges. We first present the model that underlies most of current causal approaches to single-cell biology. We then identify open problems in the application of causal approaches to single-cell data.
  arXiv  Detail & Related papers  (2023-10-23T13:35:24Z)
• A Causal Framework for Decomposing Spurious Variations [68.12191782657437]
  We develop tools for decomposing spurious variations in Markovian and Semi-Markovian models. We prove the first results that allow a non-parametric decomposition of spurious effects. The described approach has several applications, ranging from explainable and fair AI to questions in epidemiology and medicine.
  arXiv  Detail & Related papers  (2023-06-08T09:40:28Z)
• Towards a methodology to consider the environmental impacts of digital agriculture [0.0]
  Agriculture affects global warming, while its yields are threatened by it. Information and communication technology (ICT) is often considered as a potential lever to mitigate this tension. This research aims at defining a methodology taking into account the environmental footprint of agricultural ICT systems and their required infrastructures.
  arXiv  Detail & Related papers  (2023-05-16T07:58:34Z)
• Empowering Agrifood System with Artificial Intelligence: A Survey of the Progress, Challenges and Opportunities [86.89427012495457]
  We review how AI techniques can transform agrifood systems and contribute to the modern agrifood industry. We present a progress review of AI methods in agrifood systems, specifically in agriculture, animal husbandry, and fishery. We highlight potential challenges and promising research opportunities for transforming modern agrifood systems with AI.
  arXiv  Detail & Related papers  (2023-05-03T05:16:54Z)
• DeepAg: Deep Learning Approach for Measuring the Effects of Outlier Events on Agricultural Production and Policy [4.800161917503703]
  We propose a novel framework, namely: DeepAg, that employs econometrics and measures the effects of outlier events detection using Deep Learning (DL) We employ a DL technique called Long Short-Term Memory (LSTM) networks successfully to predict commodity production with high accuracy. We present the implications of DeepAg on public policy, provide insights for policymakers and farmers, and for operational decisions in the agricultural ecosystem.
  arXiv  Detail & Related papers  (2021-10-22T20:55:33Z)
• Latent Causal Invariant Model [128.7508609492542]
  Current supervised learning can learn spurious correlation during the data-fitting process. We propose a Latent Causal Invariance Model (LaCIM) which pursues causal prediction.
  arXiv  Detail & Related papers  (2020-11-04T10:00:27Z)

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.