Precision Agriculture for Improving Water Quality under Changing Climate

Lal, Rattan; Stewart, B.A.

doi:10.1201/b18759-15

Cited by 3 publications

(1 citation statement)

References 1 publication

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Conventional yield prediction treats the field uniformly despite inherent variability. Uniform management may result in over-or under-application of resources in specific locations within a field, which may have a negative impact on the environment and profitability [8]. However, consistent and accurate withinfield yield prediction is challenging due to the high accuracy requirement under the complex interactions between yield-influencing factors, such as soil, weather, water, and spatial correlations.…”

Section: Introductionmentioning

confidence: 99%

Spatial-Temporal Multi-Task Learning for Within-Field Cotton Yield Prediction

Nguyen

Zhu

Lin

et al. 2019

Lecture Notes in Computer Science

View full text Add to dashboard Cite

Understanding and accurately predicting within-field spatial variability of crop yield play a key role in site-specific management of crop inputs such as irrigation water and fertilizer for optimized crop production. However, such a task is challenged by the complex interaction between crop growth and environmental and managerial factors, such as climate, soil conditions, tillage, and irrigation. In this paper, we present a novel Spatial-temporal Multi-Task Learning algorithms for within-field crop yield prediction in west Texas from 2001 to 2003. This algorithm integrates multiple heterogeneous data sources to learn different features simultaneously, and to aggregate spatial-temporal features by introducing a weighted regularizer to the loss functions. Our comprehensive experimental results consistently outperform the results of other conventional methods, and suggest a promising approach, which improves the landscape of crop prediction research fields.

show abstract

Section: Introductionmentioning

confidence: 99%

Spatial-Temporal Multi-Task Learning for Within-Field Cotton Yield Prediction

Nguyen

Zhu

Lin

et al. 2019

Lecture Notes in Computer Science

View full text Add to dashboard Cite

show abstract

Priorities for science to overcome hurdles thwarting the full promise of the ‘digital agriculture’ revolution

et al. 2018

View full text Add to dashboard Cite

The world needs to produce more food, more sustainably, on a planet with scarce resources and under changing climate. The advancement of technologies, computing power and analytics offers the possibility that 'digitalisation of agriculture' can provide new solutions to these complex challenges. The role of science is to evidence and support the design and use of digital technologies to realise these beneficial outcomes and avoid unintended consequences. This requires consideration of data governance design to enable the benefits of digital agriculture to be shared equitably and how digital agriculture could change agricultural business models; that is, farm structures, the value chain and stakeholder roles, networks and power relations, and governance. We argue that this requires transdisciplinary research (at pace), including explicit consideration of the aforementioned socio-ethical issues, data governance and business models, alongside addressing technical issues, as we now have to simultaneously deal with multiple interacting outcomes in complex technical, social, economic and governance systems. The exciting prospect is that digitalisation of science can enable this new, and more effective, way of working. The question then becomes: how can we effectively accelerate this shift to a new way of working in agricultural science? As well as identifying key research areas, we suggest organisational changes will be required: new research business models, agile project management; new skills and capabilities; and collaborations with new partners to develop 'technology ecosystems'. © 2018 The Authors. Journal of The Science of Food and Agriculture published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.

show abstract

A Temporal Learning Framework: From Experience of Artificial Cultivation to Knowledge

Sun

Zheng

et al. 2019

Green, Pervasive, and Cloud Computing

View full text Add to dashboard Cite

Precision Agriculture for Improving Water Quality under Changing Climate

Cited by 3 publications

References 1 publication

Spatial-Temporal Multi-Task Learning for Within-Field Cotton Yield Prediction

Spatial-Temporal Multi-Task Learning for Within-Field Cotton Yield Prediction

Priorities for science to overcome hurdles thwarting the full promise of the ‘digital agriculture’ revolution

A Temporal Learning Framework: From Experience of Artificial Cultivation to Knowledge

Contact Info

Product

Resources

About