Using UAV Images and Phenotypic Traits to Predict Potato Morphology and Yield in Peru DOI Creative Commons
Dennis Ccopi-Trucios, Kevin Abner Ortega Quispe, Marco Italo Castañeda-Tinco

et al.

Agriculture, Journal Year: 2024, Volume and Issue: 14(11), P. 1876 - 1876

Published: Oct. 24, 2024

Precision agriculture aims to improve crop management using advanced analytical tools. In this context, the objective of study is develop an innovative predictive model estimate yield and morphological quality, such as circularity length–width ratio potato tubers, based on phenotypic characteristics plants data captured through spectral cameras equipped UAVs. For purpose, experiment was carried out at Santa Ana Experimental Station in central Peruvian Andes, where clones were planted December 2023 under three levels fertilization. Random Forest, XGBoost, Support Vector Machine models used predict quality parameters, ratio. The results showed that Forest XGBoost achieved high accuracy prediction (R2 > 0.74). contrast, less accurate, with standing most reliable = 0.55 for circularity). Spectral significantly improved capacity compared agronomic alone. We conclude integrating indices multitemporal into estimating certain traits, offering key opportunities optimize agricultural management.

Language: Английский

Unsupervised Learning DOI

Akshay Bhuvaneswari Ramakrishnan,

S. Srijanani

Advances in computational intelligence and robotics book series, Journal Year: 2025, Volume and Issue: unknown, P. 303 - 314

Published: March 26, 2025

Unsupervised learning, an essential component of machine has a substantial impact on the advancement and implementation generative AI. Incorporating unsupervised learning into AI models potential to transform businesses by automating improving creative processes. This chapter explores fundamental principles, techniques, progress in learning. The authors delve range methods approaches, including clustering, dimensionality reduction, data mining, feature extraction, neural networks, anomaly detection, emphasizing their use models. provides detailed explanation cases demonstrate how allows produce new high-quality outputs without need for labeled data.

Language: Английский

Citations

0
A cluster-based local modeling paradigm for high spatiotemporal resolution VPD prediction using multi-source data and machine learning DOI Creative Commons

Mi Wang,

Zhuowei Hu,

Xiangping Liu

et al.

International Journal of Digital Earth, Journal Year: 2025, Volume and Issue: 18(1)

Published: April 29, 2025

Language: Английский

Citations

0
Dynamic optimization can effectively improve the accuracy of reference evapotranspiration in southern China DOI
Xiang Xiao, Ziniu Xiao, Xiaogang Liu

et al.

Computers and Electronics in Agriculture, Journal Year: 2024, Volume and Issue: 230, P. 109881 - 109881

Published: Dec. 31, 2024

Language: Английский

Citations

2
Estimating and forecasting daily reference crop evapotranspiration in China with temperature-driven deep learning models DOI Creative Commons
Jia Zhang, Yimin Ding, Lei Zhu

et al.

Agricultural Water Management, Journal Year: 2024, Volume and Issue: 307, P. 109268 - 109268

Published: Dec. 24, 2024

Language: Английский

Citations

1
The Impact of Cell Phone Dependence on College Students’ Mental Health and Adjustment Strategies in the Context of Big Data DOI Creative Commons

Zhen Qu

Applied Mathematics and Nonlinear Sciences, Journal Year: 2024, Volume and Issue: 9(1)

Published: Jan. 1, 2024

Abstract Addiction to cell phone use is prevalent in the college student population, which not only affects academic life but also often coincides with psychological problems such as anxiety and depression. Four institutions of higher education high detection rates depression other disorders previous years were setting for this paper’s one-year baseline survey two follow-up studies. Using mental health scores depressive symptoms dependent variable dependence independent variable, we explored association between among students using a partial least squares regression model that combines features principal component analysis stepwise regression. Finally, designed social treatment adjustment strategy dependence, selected six severe undergo semester-long intervention adjustment, evaluated effects. The study found regardless gender, there was significant positive students, β = 0.26, 95% CI: 0.31, 0.38 male 0.39 female effect dosage even more pronounced. We scored 15 points. paper has better impact on suffering from can reduce time by at 2 hours or more. This provides innovative ideas feasible debugging strategies managing behavior students.

Language: Английский

Citations

0
Using UAV Images and Phenotypic Traits to Predict Potato Morphology and Yield in Peru DOI Creative Commons
Dennis Ccopi-Trucios, Kevin Abner Ortega Quispe, Marco Italo Castañeda-Tinco

et al.

Agriculture, Journal Year: 2024, Volume and Issue: 14(11), P. 1876 - 1876

Published: Oct. 24, 2024

Precision agriculture aims to improve crop management using advanced analytical tools. In this context, the objective of study is develop an innovative predictive model estimate yield and morphological quality, such as circularity length–width ratio potato tubers, based on phenotypic characteristics plants data captured through spectral cameras equipped UAVs. For purpose, experiment was carried out at Santa Ana Experimental Station in central Peruvian Andes, where clones were planted December 2023 under three levels fertilization. Random Forest, XGBoost, Support Vector Machine models used predict quality parameters, ratio. The results showed that Forest XGBoost achieved high accuracy prediction (R2 > 0.74). contrast, less accurate, with standing most reliable = 0.55 for circularity). Spectral significantly improved capacity compared agronomic alone. We conclude integrating indices multitemporal into estimating certain traits, offering key opportunities optimize agricultural management.

Language: Английский

Citations

0