Related papers: Evaluating Digital Tools for Sustainable Agricultu…
A data science task can be deemed as making sense of the data or testing a hypothesis about it. The conclusions inferred from data can greatly guide us to make informative decisions. Big data has enabled us to carry out countless prediction…
California is a global leader in agricultural production, contributing 12.5% of the United States total output and ranking as the fifth-largest food and cotton supplier in the world. Despite the availability of extensive historical yield…
Remote sensing (RS) technique, enabling the non-contact acquisition of extensive ground observations, is a valuable tool for crop yield predictions. Traditional process-based models struggle to incorporate large volumes of RS data, and most…
An accurate and precise understanding of global irrigation usage is crucial for a variety of climate science efforts. Irrigation is highly energy-intensive, and as population growth continues at its current pace, increases in crop need and…
We report promising results for high-throughput on-field soybean pod count with small mobile robots and machine-vision algorithms. Our results show that the machine-vision based soybean pod counts are strongly correlated with soybean yield.…
The current paper investigates the influences of AI innovation, GDP growth, renewable energy utilization, the digital economy, and industrialization on CO2 emissions in the USA from 1990 to 2022, incorporating the ARDL methodology. The…
Background: Recent innovations in generative artificial intelligence (AI) have transformed how programmers develop and maintain software. The advanced capabilities of generative AI tools in supporting development tasks have led to a rise in…
Forecasting agricultural markets remains challenging due to nonlinear dynamics, structural breaks, and sparse data. A long-standing belief holds that simple time-series methods outperform more advanced alternatives. This paper provides the…
This paper explores the transformative potential of artificial intelligence (AI) in the context of sustainable agricultural development across diverse regions in Africa. Delving into opportunities, challenges, and impact, the study…
In the face of climate change-induced droughts, vulnerable regions encounter severe threats to food security, demanding urgent humanitarian assistance. This paper introduces a causal inference framework for the Horn of Africa, aiming to…
The widespread use of AI services has raised concerns for its environmental sustainability, towards which recent studies have identified carbon emissions of AI inference as the major contributor. This paper introduces a framework for…
The rapid growth of IoT devices has led to an enormous amount of sensor data that requires transmission to cloud servers for processing, resulting in excessive network congestion, increased latency and high energy consumption. This is…
The conventional fishing industry has several difficulties: water contamination, temperature instability, nutrition, area, expense, etc. In fish farming, Biofloc technology turns traditional farming into a sophisticated infrastructure that…
Crop yield is a highly complex trait determined by multiple factors such as genotype, environment, and their interactions. Accurate yield prediction requires fundamental understanding of the functional relationship between yield and these…
Solar irrigation systems are increasingly deployed in rural regions, yet their distributed and remote deployment makes maintenance challenging for farmers. While formal monitoring processes and applications exist, they often fall short in…
In this paper, we present a data-driven model for estimating optimal rework policies in manufacturing systems. We consider a single production stage within a multistage, lot-based system that allows for optional rework steps. While the…
Most major retailers today have multiple divisions focused on various aspects, such as marketing, supply chain, online customer experience, store customer experience, employee productivity, and vendor fulfillment. They also regularly…
Evaluating the causal effect of recommendations is an important objective because the causal effect on user interactions can directly leads to an increase in sales and user engagement. To select an optimal recommendation model, it is common…
The effectiveness of farmer loan policies is crucial for the high-quality development of agriculture and the orderly advancement of the rural revitalization strategy. Exploring the impact of farmers' borrowing behavior on agricultural…
Instrumental variable methods are among the most commonly used causal inference approaches to deal with unmeasured confounders in observational studies. The presence of invalid instruments is the primary concern for practical applications,…