Integrating LLM for Cotton Soil Analysis in Smart Agriculture System
Journal Title: International Journal of Innovations in Science and Technology - Year 2024, Vol 6, Issue 7
Abstract
Cotton is a critical crop for the agricultural economy, with its productivity closely tied to soil quality, particularly soil nutrient levels and pH. Monitoring and optimizing these properties is essential for sustainable cotton cultivation. This study proposes using finetuned Large Language Models (LLMs)—specifically GPT-2 and LLaMA-2—to automate soil analysis and produce detailed soil reports with actionable recommendations, addressing the limitations of traditional machine learning models in this context. A custom dataset was created by extracting key information from cotton-specific resources, focusing on soil nutrient interpretation and recommendations across different growth stages. Fine-tuning was applied to GPT-2 and LLaMA-2 models (specifically, the Nous Research version LLaMA2-7b-hf from Hugging Face), enabling them to generate data-driven reports on cotton soil health. The finetuned GPT-2 model achieved a training loss of 0.093 and an evaluation loss of 0.086, outperforming LLaMA-2, which had a training loss of 0.033 and an evaluation loss of 0.25. Evaluation with BERT Score showed that GPT-2 scored average Precision, Recall, and F1 scores of 0.9284, 0.9308, and 0.9296, respectively, highlighting its superior report accuracy and contextual relevance compared to LLaMA-2. The generated reports included soil properties and actionable nutrient management recommendations, effectively supporting optimized cotton growth. Implementing fine-tuned LLMs for soil report generation enhances nutrient management practices, contributing to higher yields and more sustainable cotton farming.
Authors and Affiliations
Syed Hassan Ali, Muhammad Farrukh Shahid, M. Hassan Tanveer, Abdul Rauf
Keywords
Related Articles
- EP ID EP761769
- DOI -
- Views 33
- Downloads 0
Osteochondroma Identification Through Transfer Learning and Convolutional Neural Networks
Accurate and timely diagnosis of musculoskeletal conditions like osteochondroma is pivotal in ensuring effective treatment and improved patient outcomes. However, traditional diagnostic methods relying on manual interp...
Comparative Evaluation of Machine Learning and Deep Learning Models for Real Estate Price Prediction
Accurate real estate price prediction is vital in informed decisionmaking for investors, policymakers, and stakeholders. This study evaluates various machine learning and deep learning models for predicting real estate...
Analyzing Privacy in Frank Lloyd Wright's Prairie Style Homes Through Syntactic Methodsusing “A Graph”and Depth Map XSoftwares
Frank Lloyd Wright's Prairie Style homes, designed across the United States, showcase his unique architectural approach. This study examines how Wright's designs interact with environmental conditions, focusing on priv...
Barriers to AI Adoption in Education: Insights from Teacher's Perspectives
Artificial intelligence in education is capable of offering significant benefits in the form of content generation, personalized learning, assistance in administration, and analytical reports. Despite the benefits, the...
Catalytic PerformanceofElectro-Oxidative Natural Manganese Sand forAmmonium Nitrogen Removal
The environmental risks associated with ammonium nitrogen (NH₄⁺-N) pollution have led to a growing focus on prevention. Electrochemical advanced oxidation is an effective and ecofriendly method that only requires electr...