Differentiation of deciduous-calyx and persistent-calyx pears using hyperspectral reflectance imaging and multivariate analysis

Hu, Haijiang; Pan, Leiqing; Sun, Ke; Tu, Sicong; Sun, Yi; Wei, Yingying; Tu, Kang

doi:10.1016/j.compag.2017.04.002

Cited by 23 publications

(12 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The aim of the study was quality improvement while the minimising fiber damage. Another study [ 95 ] regards pears production and, more specifically, a method was presented for the identification and differentiation of Korla fragrant pears into deciduous-calyx or persistent-calyx categories. The approach applied ML methods with hyperspectral reflectance imaging.…”

Section: Reviewmentioning

confidence: 99%

Machine Learning in Agriculture: A Review

Λιάκος

Busato

Moshou

et al. 2018

Sensors

1,887

827

View full text Add to dashboard Cite

Machine learning has emerged with big data technologies and high-performance computing to create new opportunities for data intensive science in the multi-disciplinary agri-technologies domain. In this paper, we present a comprehensive review of research dedicated to applications of machine learning in agricultural production systems. The works analyzed were categorized in (a) crop management, including applications on yield prediction, disease detection, weed detection crop quality, and species recognition; (b) livestock management, including applications on animal welfare and livestock production; (c) water management; and (d) soil management. The filtering and classification of the presented articles demonstrate how agriculture will benefit from machine learning technologies. By applying machine learning to sensor data, farm management systems are evolving into real time artificial intelligence enabled programs that provide rich recommendations and insights for farmer decision support and action.

show abstract

Section: Reviewmentioning

confidence: 99%

Machine Learning in Agriculture: A Review

Λιάκος

Busato

Moshou

et al. 2018

Sensors

1,887

827

View full text Add to dashboard Cite

show abstract

“…Discrimination analysis was then carried out by linear discriminant analysis (LDA) and partial least square discriminant analysis (PLS‐DA) to discriminate samples into two classes: Class A (acceptable, DON < 1000 μg kg −1 ) and Class B (rejective, DON ≥ 1000 μg kg −1 ) according to China regulatory level. Quantitative analysis of DON levels in samples was performed by linear and nonlinear algorithms, including partial least squares regression (PLSR), multiple linear regression (MLR) and support vector machine (SVM) (Hu et al ., 2017).…”

Section: Methodsmentioning

confidence: 99%

Rapid screening of DON contamination in whole wheat meals by Vis/NIR spectroscopy and computer vision coupling technology

Zhang

Jiang

Shen

et al. 2020

Int J of Food Sci Tech

View full text Add to dashboard Cite

show abstract

“… Fan et al., 2016a , Fan et al., 2016b and Tian (2017) combined spectra and textural features of hyperspectral reflectance imaging, respectively. Hyperspectral imaging was proposed as a tool to assess the quality of pear, strawberry, apricot, and pomegranate ( Hu et al., 2017 ; Liu et al., 2014 ; Büyükcan et al., 2016 ; Khodabakhshian et al., 2016 ). Zhang (2015) adopted hyperspectral imaging and chemometrics combined method to detect egg freshness, egg internal bubbles, and egg scattered yolk.…”

Section: Machine Vision System (Mvs)mentioning

confidence: 99%

“…(2017) Grading An improved watershed segmentation algorithm accuracy = 96.5% (for bruised), accuracy = 97.5% (for sound) Li et al. (2018) Pear Grading SPA-SVM accuracy = 93.3%, 96.7% Hu et al. (2017) Pomegranate Grading PLS r = 0 .…”

Section: Machine Learning Approachesmentioning

confidence: 99%

Deep learning and machine vision for food processing: A survey

Zhu

Spachos

Pensini

et al. 2021

Current Research in Food Science

184

View full text Add to dashboard Cite

The quality and safety of food is an important issue to the whole society, since it is at the basis of human health, social development and stability. Ensuring food quality and safety is a complex process, and all stages of food processing must be considered, from cultivating, harvesting and storage to preparation and consumption. However, these processes are often labour-intensive. Nowadays, the development of machine vision can greatly assist researchers and industries in improving the efficiency of food processing. As a result, machine vision has been widely used in all aspects of food processing. At the same time, image processing is an important component of machine vision. Image processing can take advantage of machine learning and deep learning models to effectively identify the type and quality of food. Subsequently, follow-up design in the machine vision system can address tasks such as food grading, detecting locations of defective spots or foreign objects, and removing impurities. In this paper, we provide an overview on the traditional machine learning and deep learning methods, as well as the machine vision techniques that can be applied to the field of food processing. We present the current approaches and challenges, and the future trends.

show abstract

Differentiation of deciduous-calyx and persistent-calyx pears using hyperspectral reflectance imaging and multivariate analysis

Cited by 23 publications

References 26 publications

Machine Learning in Agriculture: A Review

Machine Learning in Agriculture: A Review

Rapid screening of DON contamination in whole wheat meals by Vis/NIR spectroscopy and computer vision coupling technology

Deep learning and machine vision for food processing: A survey

Contact Info

Product

Resources

About