Results 161 to 170 of about 130,770 (249)

A Hierarchy of Crossmodal Odor Associations: Exploring the Role of Pleasantness and Edibility in Odor Crossmodal Associations Across the Senses

Topics in Cognitive Science, EarlyView.
Abstract Crossmodal associations with odors have the potential to aid in odor perception and language. In addition, identifying the dimensions that drive odor crossmodal associations may help understand the critical dimensions underlying odor perception more generally.
Laura J. Speed, Tabitha L. James, Duncan A. Carmichael   +2 more
wiley   +1 more source

Crystal structure of diaphorin methanol monosolvate isolated from Diaphorina citri Kuwayama, the insect vector of citrus greening disease. [PDF]

Acta Crystallogr E Crystallogr Commun, 2018
Szebenyi DM, Kriksunov I, Howe KJ, Ramsey JS, Hall DG, Heck ML, Krasnoff SB.   +6 more
europepmc   +1 more source

Recent Advances in Boron Removal From Geothermal Waters: A Review

Water and Environment Journal, EarlyView.
This review provides a comprehensive overview of boron removal technologies from geothermal fluids and highlights the limitations of conventional treatment methods. Ion exchange, hybrid processes and membrane filtration are identified as the most dominant approaches in recent studies.
Saadet Acar Can, Hasan Koseoglu
wiley   +1 more source

A multi-scale parallel weighted fusion dynamic attention method for citrus leaf disease recognitions. [PDF]

Front Plant Sci
Wu B, Gao K, Gu J, Yan G.
europepmc   +1 more source

Improved annotation of the insect vector of citrus greening disease: biocuration by a diverse genomics community. [PDF]

Database (Oxford), 2017
Saha S, Hosmani PS, Villalobos-Ayala K, Miller S, Shippy T, Flores M, Rosendale A, Cordola C, Bell T, Mann H, DeAvila G, DeAvila D, Moore Z, Buller K, Ciolkevich K, Nandyal S, Mahoney R, Van Voorhis J, Dunlevy M, Farrow D, Hunter D, Morgan T, Shore K, Guzman V, Izsak A, Dixon DE, Cridge A, Cano L, Cao X, Jiang H, Leng N, Johnson S, Cantarel BL, Richards S, English A, Shatters RG, Childers C, Chen MJ, Hunter W, Cilia M, Mueller LA, Munoz-Torres M, Nelson D, Poelchau MF, Benoit JB, Wiersma-Koch H, D'Elia T, Brown SJ.   +47 more
europepmc   +1 more source

Multi‐Model Fruit Quality Assessment With Coarse YOLOv11 Presence Filtering, Vision Transformers and the Segment Anything Model

Applied AI Letters, Volume 8, Issue 1, February 2027.
This paper presents a multi‐model deep learning pipeline for coarse fruit‐presence filtering, ripeness classification and pixel‐level spoilage segmentation. YOLOv11n is used only as an image‐level presence filter, four classifiers predict one of nine ripeness classes across three fruit types and the Segment Anything Model (SAM ViT‐H) generates fine ...
Md. Fazlay Rabbi, Al Hasib Mahamud, Md. Redwan Hossen, Md. Jakaria Mahmud, Mustofa Ahmed   +4 more
wiley   +1 more source

A hybrid approach for citrus disease detection using convolutional neural networks and fuzzy inference systems for enhanced accuracy and interpretability. [PDF]

Sci Rep
Kaur B, Middha S, Singh G, Mittal N, Doshi H, Khafaga DS, Aldakheel EA, Abouhawwash M.   +7 more
europepmc   +1 more source

From Food Waste to Phytochemicals: Green Recovery, Food‐Matrix Applications, and Metabolic‐Health Translation of Residue‐Derived Polyphenols

Phytochemicals in Food and Medicine, Volume 1, Issue 4, December 2026.
Decision rule: The lowest‐scoring gate determines overall readiness. Suggested concise take‐home message: Residue‐derived polyphenols should advance from chemically defined sources through food‐grade recovery and real‐matrix validation to realistic exposure‐based health evidence.
Chandan Kumar Sahu, Syed Irshaan, S. Karthikeyan   +2 more
wiley   +1 more source

A lightweight LBM detection transformer with multi-scale feature fusion for citrus-picking robots. [PDF]

Front Plant Sci
Gao K, Wu B, Gu J, Yan G.
europepmc   +1 more source

Artificial intelligence‐powered plant phenomics: Progress, challenges, and opportunities

The Plant Phenome Journal, Volume 9, Issue 1, December 2026.
Abstract Artificial intelligence (AI), a key driver of the Fourth Industrial Revolution, is being rapidly integrated into plant phenomics to automate sensing, accelerate data analysis, and support decision‐making in phenomic prediction and genomic selection.
Xu Wang, Daeun Choi, Damian Adams, Jaehyun Ahn, Kelly Balmant, Raquel Dias, Carlos D. Messina, Rafael Muñoz‐Carpena, Vance M. Whitaker, Haipeng Yu, Ziwen Yu, Chang Zhao, Changying Li   +12 more
wiley   +1 more source