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Hyperparameter optimization of YoLov8 for smoke and wildfire detection:Implications for agricultural and environmental safety

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Artificial Intelligence in Agriculture 2024年第2期12卷 109-126页

作者： Leo Ramos Edmundo Casas Eduardo Bendek cristian Romero Francklin Rivas-Echeverría Kauel Inc. Menlo Park(Silicon Valley)CA 94025USA Computer Vision Center Universitat Autònoma de BarcelonaBellaterra 08193Spain Faculty of Engineering University of DeustoBilbao 48007Spain Jet Propulsion Laboratory NASAPasadenaCA 91109USA

In this study,we extensively evaluated the viability of the state-of-the-art YoLov8 architecture for object detec-tion tasks,specifically tailored for smoke and wildfire identification with a focus on agricultural and environmen-tal *** available versions of YoLov8 were initially fine-tuned on a domain-specific dataset that included a variety of scenarios,crucial for comprehensive agricultural ***‘large’version(YoLov8l)was se-lected for further hyperparameter tuning based on its performance *** model underwent a detailed hyperparameter optimization using the one Factor At a Time(oFAT)methodology,concentrating on key param-eters such as learning rate,batch size,weight decay,epochs,and *** from the oFAT study were used to define search spaces for a subsequent Random search(RS).The final model derived from RS demon-strated significant improvements over the initial fine-tuned model,increasing overall precision by 1.39%,recall by 1.48%,F1-score by 1.44%,mAP@0.50 by 0.70%,and mAP@0.50:0.95 by 5.09%.We validated the enhanced model's efficacy on a diverse set of real-world images,reflecting various agricultural settings,to confirm its ro-bustness in detecting smoke and *** results underscore the model's reliability and effectiveness in scenar-ios critical to agricultural safety and environmental *** work,representing a significant advancement in the field of fire and smoke detection through machine learning,lays a strong foundation for fu-ture research and solutions aimed at safeguarding agricultural areas and natural environments.

关键词： Agricultural safety Wildfire detection Smoke detection object detection Computer vision YoL

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The glutamate metabotropic receptor 5(GRM5)gene is associated with beef cattle home range and movement tortuosity

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Journal of Animal Science and Biotechnology 2023年第1期14卷 72-87页

作者： cristian A.Moreno Garcia Huitong Zhou David Altimira Robyn Dynes Pablo Gregorini Sadeepa Jayathunga Thomas M.R.Maxwell Jonathan Hickford Department of Agricultural Sciences Faculty of Agriculture and Life SciencesLincoln UniversityPO Box 85084Lincoln 7647New Zealand. Exertion Games Lab 8 Scenic BoulevardClaytonVictoria 3800Australia. AgResearch Limited Lincoln Research CentreCnr Springs Road and Gerald StreetLincoln 7674New Zealand. Scion Tītokorangi Drive(formerly Long Mile Rd)Rotorua 3010New Zealand.

Background:The grazing behaviour of herbivores and their grazing personalities might in part be determined genetically,but there are few studies in beef cattle illustrating *** this study,we investigated for first time the genetic variation within a candidate‘grazing gene',the glutamate metabotropic receptor 5 gene(GRM5),and tested associations between variation in that gene and variation in grazing personality behaviours(GP-behaviours)displayed by free-ranging cows during winter grazing in the steep and rugged rangelands of New *** beef cows(n=303,from 3 to 10 years of age)were tracked with global positioning system(GPS)and,with 5-minutes(min)relocation frequency,various GP-behaviours were *** included horizontal and vertical distances travelled,mean elevation,elevation range,elevation gain,slope,home range and movement tortuosity,variously calculated using daily relocation trajectories with repeated measurements(i.e.,7 to 24 days(d))and satellite-derived digital elevation models(DEM).The different GP-behaviours were fitted into mixed models to ascertain their associations with variant sequences and genotypes of ***:We discovered three GRM5 variants(A,B and C)and identified the six possible genotypes in the cattle *** mixed models revealed that A was significantly associated with elevation range,home range and movement ***,GRM5 genotypes were associated(Po home range and movement tortuosity,while trends suggesting association(Po revealed for elevation range and horizontal distance *** GP-behaviour models were improved by correcting for cow age-class as a fixed *** analysis of GP-behaviours averaged per cow age-class suggests that grazing personality is fully established as beef cows reached 4 years of *** range and movement tortuosity were not only associated with GRM5 variation,but also negatively correlated with each other(r=-0.27,Ponclusions:There se

关键词： Animal personality Breeding programmes Genetic associations Grazing distribution Grazing patterns Steep and rugged terrain

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Challenges Analyzing RNA-seq Gene Expression Data

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open Journal of Statistics 2016年第4期6卷 628-636页

作者： Liliana López-Kleine cristian González-Prieto Department of Statistics Universidad Nacional de Colombia—Sede Bogotá Bogotá Colombia

The analysis of messenger Ribonucleic acid obtained through sequencing techniques (RNA-se- quencing) data is very challenging. once technical difficulties have been sorted, an important choice has to be made during pre-processing: Two different paths can be chosen: Transform RNA- sequencing count data to a continuous variable or continue to work with count data. For each data type, analysis tools have been developed and seem appropriate at first sight, but a deeper analysis of data distribution and structure, are a discussion worth. In this review, open questions regarding RNA-sequencing data nature are discussed and highlighted, indicating important future research topics in statistics that should be addressed for a better analysis of already available and new appearing gene expression data. Moreover, a comparative analysis of RNAseq count and transformed data is presented. This comparison indicates that transforming RNA-seq count data seems appropriate, at least for differential expression detection.

关键词： RNA-seq Analysis Count Data Preprocessing Differential Expression Gene Co-Expression Network

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