Related papers: Agricultural Recommendation System based on Deep Learning: A Multivariate Weather Forecasting Approach

Agricultural Recommendation System based on Deep Learning: A Multivariate Weather Forecasting Approach

URL: http://arxiv.org/abs/2401.11410v3
Date: Fri, 12 Jul 2024 02:02:45 GMT
Title: Agricultural Recommendation System based on Deep Learning: A Multivariate Weather Forecasting Approach
Authors: Md Zubair, Md. Shahidul Salim, Mehrab Mustafy Rahman, Mohammad Jahid Ibna Basher, Shahin Imran, Iqbal H. Sarker,
Abstract summary: This paper proposes a context-based crop recommendation system powered by a weather forecast model. The proposed weather model can forecast Rainfall, Temperature, Humidity, and Sunshine for any given location in Bangladesh with an average R-Squared value of 0.9824. The system is also adept at making knowledge-based crop suggestions for flood and drought-prone regions.
Score: 1.756503402823037
License: http://creativecommons.org/licenses/by/4.0/
Abstract: Agriculture plays a fundamental role in driving economic growth and ensuring food security for populations around the world. Although labor-intensive agriculture has led to steady increases in food grain production in many developing countries, it is frequently challenged by adverse weather conditions, including heavy rainfall, low temperatures, and drought. These factors substantially hinder food production, posing significant risks to global food security. In order to have a profitable, sustainable, and farmer-friendly agricultural practice, this paper proposes a context-based crop recommendation system powered by a weather forecast model. For implementation purposes, we have considered the whole territory of Bangladesh. With extensive evaluation, the multivariate Stacked Bi-LSTM (three Bi-LSTM layers with a time Distributed layer) Network is employed as the weather forecasting model. The proposed weather model can forecast Rainfall, Temperature, Humidity, and Sunshine for any given location in Bangladesh with an average R-Squared value of 0.9824, and the model outperforms other state-of-the-art LSTM models. These predictions guide our system in generating viable farming decisions. Additionally, our full-fledged system is capable of alerting the farmers about extreme weather conditions so that preventive measures can be undertaken to protect the crops. Finally, the system is also adept at making knowledge-based crop suggestions for flood and drought-prone regions.

Related papers

Enabling Adoption of Regenerative Agriculture through Soil Carbon Copilots [11.63518622433838]
We introduce an AI-driven Soil Organic Carbon Copilot to provide insights into soil health and regenerative practices. Our data includes extreme weather event data, farm management data, and SOC predictions. In comparisons of agricultural practices across California counties, we find evidence that diverse agricultural activity may mitigate the negative effects of tillage.
arXiv Detail & Related papers (2024-11-25T19:11:41Z)
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)
The unrealized potential of agroforestry for an emissions-intensive agricultural commodity [48.652015514785546]
We use machine learning to generate estimates of shade-tree cover and carbon stocks across a West African region. We find that existing shade-tree cover is low, and not spatially aligned with climate threat. But we also find enormous unrealized potential for the sector to counterbalance a large proportion of their high carbon footprint annually.
arXiv Detail & Related papers (2024-10-28T10:02:32Z)
Efficient Localized Adaptation of Neural Weather Forecasting: A Case Study in the MENA Region [62.09891513612252]
We focus on limited-area modeling and train our model specifically for localized region-level downstream tasks. We consider the MENA region due to its unique climatic challenges, where accurate localized weather forecasting is crucial for managing water resources, agriculture and mitigating the impacts of extreme weather events. Our study aims to validate the effectiveness of integrating parameter-efficient fine-tuning (PEFT) methodologies, specifically Low-Rank Adaptation (LoRA) and its variants, to enhance forecast accuracy, as well as training speed, computational resource utilization, and memory efficiency in weather and climate modeling for specific regions.
arXiv Detail & Related papers (2024-09-11T19:31:56Z)
A Machine Learning Approach for Crop Yield and Disease Prediction Integrating Soil Nutrition and Weather Factors [0.0]
The development of an intelligent agricultural decision-supporting system for crop selection and disease forecasting in Bangladesh is the main objective of this work. The recommended approach uses a variety of datasets on the production of crops, soil conditions, agro-meteorological regions, crop disease, and meteorological factors.
arXiv Detail & Related papers (2024-03-28T09:57:50Z)
Forecasting trends in food security with real time data [0.0]
We present a quantitative methodology to forecast levels of food consumption for 60 consecutive days, at the sub-national level, in four countries: Mali, Nigeria, Syria, and Yemen. The methodology is built on publicly available data from the World Food Programme's global hunger monitoring system.
arXiv Detail & Related papers (2023-12-01T14:42:37Z)
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)
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)
Multi-scale Digital Twin: Developing a fast and physics-informed surrogate model for groundwater contamination with uncertain climate models [53.44486283038738]
Climate change exacerbates the long-term soil management problem of groundwater contamination. We develop a physics-informed machine learning surrogate model using U-Net enhanced Fourier Neural Contaminated (PDENO) In parallel, we develop a convolutional autoencoder combined with climate data to reduce the dimensionality of climatic region similarities across the United States.
arXiv Detail & Related papers (2022-11-20T06:46:35Z)
Remote Sensing and Machine Learning for Food Crop Production Data in Africa Post-COVID-19 [0.0]
Travel bans and border closures, the late reception and the use of agricultural inputs could lead to poor food crop production performances. This chapter assesses food crop production levels in 2020 in all African regions and four staples such as maize, cassava, rice, and wheat. The production levels are predicted using the combination of biogeophysical remote sensing data retrieved from satellite images and machine learning artificial neural networks (ANNs) technique.
arXiv Detail & Related papers (2021-07-14T13:14:46Z)

This list is automatically generated from the titles and abstracts of the papers in this site.