Build Deep Neural Network Models to Detect Common Edible Nuts from Photos and Estimate Nutrient Portfolio.

Publisher: MDPI Publishing Country of Publication: Switzerland NLM ID: 101521595 Publication Model: Electronic Cited Medium: Internet ISSN: 2072-6643 (Electronic) Linking ISSN: 20726643 NLM ISO Abbreviation: Nutrients Subsets: MEDLINE

Original Publication: Basel, Switzerland : MDPI Publishing

Nuts* ; Photography* ; Neural Networks, Computer* ; Nutritive Value*; Humans ; Deep Learning ; Nutrients/analysis

Nuts are nutrient-dense foods and can be incorporated into a healthy diet. Artificial intelligence-powered diet-tracking apps may promote nut consumption by providing real-time, accurate nutrition information but depend on data and model availability. Our team developed a dataset comprising 1380 photographs, each in RGB color format and with a resolution of 4032 × 3024 pixels. These images feature 11 types of nuts that are commonly consumed. Each photo includes three nut types; each type consists of 2-4 nuts, so 6-9 nuts are in each image. Rectangular bounding boxes were drawn using a visual geometry group (VGG) image annotator to facilitate the identification of each nut, delineating their locations within the images. This approach renders the dataset an excellent resource for training models capable of multi-label classification and object detection, as it was meticulously divided into training, validation, and test subsets. Utilizing transfer learning in Python with the IceVision framework, deep neural network models were adeptly trained to recognize and pinpoint the nuts depicted in the photographs. The ultimate model exhibited a mean average precision of 0.7596 in identifying various nut types within the validation subset and demonstrated a 97.9% accuracy rate in determining the number and kinds of nuts present in the test subset. By integrating specific nutritional data for each type of nut, the model can precisely (with error margins ranging from 0.8 to 2.6%) calculate the combined nutritional content-encompassing total energy, proteins, carbohydrates, fats (total and saturated), fiber, vitamin E, and essential minerals like magnesium, phosphorus, copper, manganese, and selenium-of the nuts shown in a photograph. Both the dataset and the model have been made publicly available to foster data exchange and the spread of knowledge. Our research underscores the potential of leveraging photographs for automated nut calorie and nutritional content estimation, paving the way for the creation of dietary tracking applications that offer real-time, precise nutritional insights to encourage nut consumption.

IEEE Trans Pattern Anal Mach Intell. 2017 Jun;39(6):1137-1149. (PMID: 27295650)
Res Nurs Health. 2013 Jun;36(3):242-56. (PMID: 23408456)
Appl Physiol Nutr Metab. 2021 Sep 15;:1-8. (PMID: 34525321)
IEEE Trans Pattern Anal Mach Intell. 2020 Feb;42(2):318-327. (PMID: 30040631)
JMIR Mhealth Uhealth. 2019 May 17;7(5):e9232. (PMID: 31102369)
J Big Data. 2021;8(1):101. (PMID: 34306963)
Annu Rev Nutr. 1999;19:17-40. (PMID: 10448515)
JMIR Mhealth Uhealth. 2017 Jul 10;5(7):e95. (PMID: 28694241)
Int J Behav Nutr Phys Act. 2009 Aug 12;6:54. (PMID: 19674467)
Nutrients. 2017 Nov 22;9(11):. (PMID: 29165404)
BMC Med. 2016 Dec 5;14(1):207. (PMID: 27916000)
Nature. 2015 May 28;521(7553):436-44. (PMID: 26017442)
Nutr Metab (Lond). 2018 Jun 22;15:46. (PMID: 29977320)
Nutrients. 2010 Jul;2(7):652-682. (PMID: 22254047)
J Obes. 2012;2012:852672. (PMID: 22811890)
J Cardiovasc Nurs. 2010 Nov-Dec;25(6):450-60. (PMID: 20938248)
J Acad Nutr Diet. 2015 Jan;115(1):64-77. (PMID: 25441955)
Curr Surg Rep. 2021;9(7):20. (PMID: 34123579)
Adv Nutr. 2011 May;2(3):293-4. (PMID: 22332062)
Nutrients. 2017 Dec 02;9(12):. (PMID: 29207471)
Nutr Health. 2022 Jul 21;:2601060221113928. (PMID: 35861193)
Heliyon. 2018 Nov 23;4(11):e00938. (PMID: 30519653)
J Med Internet Res. 2021 Apr 22;23(4):e25160. (PMID: 33885375)

Keywords: computer vision; deep learning; diet; neural network; nut; object detection

Date Created: 20240511 Date Completed: 20240511 Latest Revision: 20240513

20240513

PMC11085677

10.3390/nu16091294

38732541