A Study of the Application Domain of a Large Language Models in the Agricultural Sector
Journal Title: International Journal of Innovative Research in Computer Science and Technology - Year 2024, Vol 12, Issue 5
Abstract
Given the expanding global population and the increasing need for food, employing effective agricultural techniques to enhance productivity on finite land resources is imperative. Artificial Intelligence is increasingly widespread in agriculture, and Artificial Intelligence driven solutions enhance the existing farming system. Agricultural productivity relies on soil nutrient composition, moisture levels, crop rotation, precipitation, temperature, etc. Artificial intelligence-based products can utilize these characteristics to monitor agrarian productivity. Industries are increasingly adopting Artificial Intelligence technologies to enhance and streamline agricultural activities across the whole food supply chain. Agricultural applications and solutions utilizing artificial intelligence have been developed to support farmers in precise and controlled farming practices. These applications provide accurate guidance on water management, changing crops, timely harvesting, crop selection, optimal planting, pest control, and nutrition management. Artificial Intelligence enabled systems utilize data such as precipitation, wind speed, temperature, and sun radiation, together with images captured by satellites and drones, to compute weather forecasts, monitor the sustainability of agriculture, and evaluate farms for the existence of infectious illnesses, pests, or undernourished plants. A large language model is a form of artificial intelligence that employs deep learning techniques to analyse and comprehend natural language. It is trained on extensive text datasets to discern statistical correlations between words and phrases. Subsequently, it may produce text, translate material, and execute other natural language processing operations. This research demonstrates how large language models emphasize the agricultural industry.
Authors and Affiliations
Saikat Banerjee, Soumitra Das, Abhoy Chand Mondal
Keywords
Related Articles
- EP ID EP749873
- DOI 10.55524/ijircst.2024.12.5.10
- Views 30
- Downloads 0
Privacy Risk Against Composition Attack
Privacy in multiple independent data publishing has attracted considerable research interest in recent years. Although each published data set poses a small privacy risk to individuals, recent studies show that this risk...
Functional Evaluation for Wireless Multimedia Networks by MAC Protocols
A high capacity energy efficient dynamic MAC Protocol was developed for WCDMA wireless multimedia networks. It used multiple slots per frame allowing a multiple users to transmit simultaneously using their own CDMA codes...
Optimizing Real-Time Object Detection- A Comparison of YOLO Models
Gun and weapon détection plays a crucial role in security, surveillance, and law enforcement. This study conducts a comprehensive comparison of all available YOLO (You Only Look Once) models for their effectiveness in we...
Crop Yield Prediction Using Random Forest Algorithm for Major Cities in Maharashtra State
Maharashtra is a leading State in agriculture. Agriculture is the one that plays important role in the economy of India. India is an agricultural country and its economy largely based upon crop production. Hence one must...
Time in Sport Pedagogy: A Review of the Evidence
This article offers a comprehensive overview of the existing literature in sport pedagogy on time-related studies. The research is organized and analyzed into four categories: how time is conceived, how time is measured,...