LLMs for Enhanced Agricultural Meteorological Recommendations

• URL: http://arxiv.org/abs/2408.04640v1
• Date: Tue, 30 Jul 2024 18:10:49 GMT
• Title: LLMs for Enhanced Agricultural Meteorological Recommendations
• Authors: Ji-jun Park, Soo-joon Choi,
• Abstract summary: Agricultural meteorological recommendations are crucial for enhancing crop productivity and sustainability by providing farmers with actionable insights based on weather forecasts, soil conditions, and crop-specific data. This paper presents a novel approach that leverages large language models (LLMs) and prompt engineering to improve the accuracy and relevance of these recommendations.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Agricultural meteorological recommendations are crucial for enhancing crop productivity and sustainability by providing farmers with actionable insights based on weather forecasts, soil conditions, and crop-specific data. This paper presents a novel approach that leverages large language models (LLMs) and prompt engineering to improve the accuracy and relevance of these recommendations. We designed a multi-round prompt framework to iteratively refine recommendations using updated data and feedback, implemented on ChatGPT, Claude2, and GPT-4. Our method was evaluated against baseline models and a Chain-of-Thought (CoT) approach using manually collected datasets. The results demonstrate significant improvements in accuracy and contextual relevance, with our approach achieving up to 90\% accuracy and high GPT-4 scores. Additional validation through real-world pilot studies further confirmed the practical benefits of our method, highlighting its potential to transform agricultural practices and decision-making.

Related papers

• Precision Agriculture Revolution: Integrating Digital Twins and Advanced Crop Recommendation for Optimal Yield [0.3686808512438362]
  Agriculture 4.0 technologies like weather APIs (Application programming interface), GPS modules, and NPK (Nitrogen, Phosphorus and Potassium) soil sensors and machine learning recommendation models, we seek to revolutionize agricultural production through this concept. The combination of real-time data on soil composition, meteorological dynamics, and geographic coordinates aims to support crop recommendation models and simulate predictive scenarios for improved water and pesticide management.
  arXiv  Detail & Related papers  (2025-02-06T13:12:25Z)
• The Dual-use Dilemma in LLMs: Do Empowering Ethical Capacities Make a Degraded Utility? [54.18519360412294]
  Large Language Models (LLMs) must balance between rejecting harmful requests for safety and accommodating legitimate ones for utility. This paper presents a Direct Preference Optimization (DPO) based alignment framework that achieves better overall performance. Our resulting model, LibraChem, outperforms leading LLMs including Claude-3, GPT-4o, and LLaMA-3 by margins of 13.44%, 7.16%, and 7.10% respectively.
  arXiv  Detail & Related papers  (2025-01-20T06:35:01Z)
• Enhancing Agricultural Machinery Management through Advanced LLM Integration [0.7366405857677226]
  The integration of artificial intelligence into agricultural practices has the potential to revolutionize efficiency and sustainability in farming. This paper introduces a novel approach that leverages large language models (LLMs), particularly GPT-4, to enhance decision-making processes in agricultural machinery management.
  arXiv  Detail & Related papers  (2024-07-30T06:49:55Z)
• Monte Carlo Tree Search Boosts Reasoning via Iterative Preference Learning [55.96599486604344]
  We introduce an approach aimed at enhancing the reasoning capabilities of Large Language Models (LLMs) through an iterative preference learning process. We use Monte Carlo Tree Search (MCTS) to iteratively collect preference data, utilizing its look-ahead ability to break down instance-level rewards into more granular step-level signals. The proposed algorithm employs Direct Preference Optimization (DPO) to update the LLM policy using this newly generated step-level preference data.
  arXiv  Detail & Related papers  (2024-05-01T11:10:24Z)
• GPT-4 as Evaluator: Evaluating Large Language Models on Pest Management in Agriculture [7.458004824488893]
  The application of large language models (LLMs) in agriculture, particularly in pest management, remains nascent. We aimed to prove the feasibility by evaluating the content of the pest management advice generated by LLMs, including the Generative Pre-trained Transformer (GPT) series from OpenAI and the FLAN series from Google. We proposed an innovative approach, using GPT-4 as an evaluator, to score the generated content on Coherence, Logical Consistency, Fluency, Relevance, Comprehensibility, and Exhaustiveness.
  arXiv  Detail & Related papers  (2024-03-18T15:08:01Z)
• Aligning GPTRec with Beyond-Accuracy Goals with Reinforcement Learning [67.71952251641545]
  GPTRec is an alternative to the Top-K model for item-by-item recommendations. We show that GPTRec offers a better tradeoff between accuracy and secondary metrics than classic greedy re-ranking techniques. Our experiments on two datasets show that GPTRec's Next-K generation approach offers a better tradeoff between accuracy and secondary metrics than classic greedy re-ranking techniques.
  arXiv  Detail & Related papers  (2024-03-07T19:47:48Z)
• Silkie: Preference Distillation for Large Visual Language Models [56.10697821410489]
  This paper explores preference distillation for large vision language models (LVLMs) We first build a vision-language feedback dataset utilizing AI annotation. We adopt GPT-4V to assess the generated outputs regarding helpfulness, visual faithfulness, and ethical considerations. The resulting model Silkie, achieves 6.9% and 9.5% relative improvement on the MME benchmark regarding the perception and cognition capabilities.
  arXiv  Detail & Related papers  (2023-12-17T09:44:27Z)
• Evaluating Digital Agriculture Recommendations with Causal Inference [0.9213852038999553]
  We propose an observational causal inference framework for the empirical evaluation of the impact of digital tools on target farm performance indicators. As a case study, we designed and implemented a recommendation system for the optimal sowing time of cotton based on numerical weather predictions. Using the back-door criterion, we identify the impact of sowing recommendations on the yield and subsequently estimate it using linear regression, matching, inverse propensity score weighting and meta-learners.
  arXiv  Detail & Related papers  (2022-11-30T12:20:08Z)
• Evaluating Digital Tools for Sustainable Agriculture using Causal Inference [0.9213852038999553]
  We propose an observational causal inference framework for the empirical evaluation of the impact of digital tools on target farm performance indicators. This way, we can increase farmers' trust by enhancing the transparency of the digital agriculture market.
  arXiv  Detail & Related papers  (2022-11-06T18:22:17Z)
• 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)
• Learning from Data to Optimize Control in Precision Farming [77.34726150561087]
  Special issue presents the latest development in statistical inference, machine learning and optimum control for precision farming. Satellite positioning and navigation followed by Internet-of-Things generate vast information that can be used to optimize farming processes in real-time.
  arXiv  Detail & Related papers  (2020-07-07T12:44:17Z)

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.