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Liu X, Li N, Huang Y, Lin X, Ren Z. A comprehensive review on acquisition of phenotypic information of Prunoideae fruits: Image technology. FRONTIERS IN PLANT SCIENCE 2023; 13:1084847. [PMID: 36777535 PMCID: PMC9909479 DOI: 10.3389/fpls.2022.1084847] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/31/2022] [Accepted: 12/21/2022] [Indexed: 06/18/2023]
Abstract
Fruit phenotypic information reflects all the physical, physiological, biochemical characteristics and traits of fruit. Accurate access to phenotypic information is very necessary and meaningful for post-harvest storage, sales and deep processing. The methods of obtaining phenotypic information include traditional manual measurement and damage detection, which are inefficient and destructive. In the field of fruit phenotype research, image technology is increasingly mature, which greatly improves the efficiency of fruit phenotype information acquisition. This review paper mainly reviews the research on phenotypic information of Prunoideae fruit based on three imaging techniques (RGB imaging, hyperspectral imaging, multispectral imaging). Firstly, the classification was carried out according to the image type. On this basis, the review and summary of previous studies were completed from the perspectives of fruit maturity detection, fruit quality classification and fruit disease damage identification. Analysis of the advantages and disadvantages of various types of images in the study, and try to give the next research direction for improvement.
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Affiliation(s)
- Xuan Liu
- College of Mechanical and Electrical Engineering, Hebei Agricultural University, Baoding, China
| | - Na Li
- College of Mechanical and Electrical Engineering, Hebei Agricultural University, Baoding, China
| | - Yirui Huang
- College of Information Engineering, Hebei GEO University, Shijiazhuang, China
| | - Xiujun Lin
- College of Mechanical and Electrical Engineering, Hebei Agricultural University, Baoding, China
| | - Zhenhui Ren
- College of Mechanical and Electrical Engineering, Hebei Agricultural University, Baoding, China
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Hao Y, Lu Y, Li X. Study on robust model construction method of multi-batch fruit online sorting by near-infrared spectroscopy. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2022; 280:121478. [PMID: 35724593 DOI: 10.1016/j.saa.2022.121478] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/17/2022] [Revised: 05/19/2022] [Accepted: 06/03/2022] [Indexed: 06/15/2023]
Abstract
In the online detection of fruit samples by near-infrared spectroscopy (NIRS), the natural change of sample states or the variations of instruments will often cause a large error in predicting different batches of samples. In this study, a total of 440 tomato samples were collected in four batches with each batch of 110 samples. The Spectral and soluble solids content (SSC) of single batch were collected every other day in batch order. The multivariate statistical process control (MSPC) method was adopted to establish a stability monitor model. The robustness regression (Rob-Reg) and partial least squares regression (PLSR) were used for mixed modeling of multiple batches of samples to eliminate the variability influence of sample and instrument states. The results show that MSPC can effectively monitor the consistency of the same batch samples measured at different times or different batches. The variation of sample attributes with spectral acquisition time has dramatically damaged the adaptation of PLSR models. The Rob-Reg method can predict the SSC of the different batches of samples at different collection times. Compared with the PLSR method, the correlation coefficient of prediction (Rp) was improved from 0.61 to 0.66, and the root mean square error of prediction (RMSEP) was decreased from 0.55 to 0.44 for Rob-Reg method. The RPD of 3.85 indicated that the model is excellent. The Robust modeling method can be well applied to fruit near-infrared online detection system.
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Affiliation(s)
- Yong Hao
- School of Mechatronics and Vehicle Engineering, East China Jiaotong University, Nanchang 330013, China.
| | - Yuanhang Lu
- School of Mechatronics and Vehicle Engineering, East China Jiaotong University, Nanchang 330013, China
| | - Xiyan Li
- School of Mechatronics and Vehicle Engineering, East China Jiaotong University, Nanchang 330013, China
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Broadacre Crop Yield Estimation Using Imaging Spectroscopy from Unmanned Aerial Systems (UAS): A Field-Based Case Study with Snap Bean. REMOTE SENSING 2021. [DOI: 10.3390/rs13163241] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Accurate, precise, and timely estimation of crop yield is key to a grower’s ability to proactively manage crop growth and predict harvest logistics. Such yield predictions typically are based on multi-parametric models and in-situ sampling. Here we investigate the extension of a greenhouse study, to low-altitude unmanned aerial systems (UAS). Our principal objective was to investigate snap bean crop (Phaseolus vulgaris) yield using imaging spectroscopy (hyperspectral imaging) in the visible to near-infrared (VNIR; 400–1000 nm) region via UAS. We aimed to solve the problem of crop yield modelling by identifying spectral features explaining yield and evaluating the best time period for accurate yield prediction, early in time. We introduced a Python library, named Jostar, for spectral feature selection. Embedded in Jostar, we proposed a new ranking method for selected features that reaches an agreement between multiple optimization models. Moreover, we implemented a well-known denoising algorithm for the spectral data used in this study. This study benefited from two years of remotely sensed data, captured at multiple instances over the summers of 2019 and 2020, with 24 plots and 18 plots, respectively. Two harvest stage models, early and late harvest, were assessed at two different locations in upstate New York, USA. Six varieties of snap bean were quantified using two components of yield, pod weight and seed length. We used two different vegetation detection algorithms. the Red-Edge Normalized Difference Vegetation Index (RENDVI) and Spectral Angle Mapper (SAM), to subset the fields into vegetation vs. non-vegetation pixels. Partial least squares regression (PLSR) was used as the regression model. Among nine different optimization models embedded in Jostar, we selected the Genetic Algorithm (GA), Ant Colony Optimization (ACO), Simulated Annealing (SA), and Particle Swarm Optimization (PSO) and their resulting joint ranking. The findings show that pod weight can be explained with a high coefficient of determination (R2 = 0.78–0.93) and low root-mean-square error (RMSE = 940–1369 kg/ha) for two years of data. Seed length yield assessment resulted in higher accuracies (R2 = 0.83–0.98) and lower errors (RMSE = 4.245–6.018 mm). Among optimization models used, ACO and SA outperformed others and the SAM vegetation detection approach showed improved results when compared to the RENDVI approach when dense canopies were being examined. Wavelengths at 450, 500, 520, 650, 700, and 760 nm, were identified in almost all data sets and harvest stage models used. The period between 44–55 days after planting (DAP) the optimal time period for yield assessment. Future work should involve transferring the learned concepts to a multispectral system, for eventual operational use; further attention should also be paid to seed length as a ground truth data collection technique, since this yield indicator is far more rapid and straightforward.
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Fahey T, Pham H, Gardi A, Sabatini R, Stefanelli D, Goodwin I, Lamb DW. Active and Passive Electro-Optical Sensors for Health Assessment in Food Crops. SENSORS (BASEL, SWITZERLAND) 2020; 21:E171. [PMID: 33383831 PMCID: PMC7795220 DOI: 10.3390/s21010171] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/06/2020] [Revised: 12/23/2020] [Accepted: 12/24/2020] [Indexed: 11/26/2022]
Abstract
In agriculture, early detection of plant stresses is advantageous in preventing crop yield losses. Remote sensors are increasingly being utilized for crop health monitoring, offering non-destructive, spatialized detection and the quantification of plant diseases at various levels of measurement. Advances in sensor technologies have promoted the development of novel techniques for precision agriculture. As in situ techniques are surpassed by multispectral imaging, refinement of hyperspectral imaging and the promising emergence of light detection and ranging (LIDAR), remote sensing will define the future of biotic and abiotic plant stress detection, crop yield estimation and product quality. The added value of LIDAR-based systems stems from their greater flexibility in capturing data, high rate of data delivery and suitability for a high level of automation while overcoming the shortcomings of passive systems limited by atmospheric conditions, changes in light, viewing angle and canopy structure. In particular, a multi-sensor systems approach and associated data fusion techniques (i.e., blending LIDAR with existing electro-optical sensors) offer increased accuracy in plant disease detection by focusing on traditional optimal estimation and the adoption of artificial intelligence techniques for spatially and temporally distributed big data. When applied across different platforms (handheld, ground-based, airborne, ground/aerial robotic vehicles or satellites), these electro-optical sensors offer new avenues to predict and react to plant stress and disease. This review examines the key sensor characteristics, platform integration options and data analysis techniques recently proposed in the field of precision agriculture and highlights the key challenges and benefits of each concept towards informing future research in this very important and rapidly growing field.
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Affiliation(s)
- Thomas Fahey
- School of Engineering, RMIT University, Melbourne, VIC 3000, Australia; (T.F.); (H.P.); (A.G.)
- Food Agility CRC Ltd., 81 Broadway, Melbourne, NSW 2007, Australia; (D.S.); (I.G.); (D.W.L.)
| | - Hai Pham
- School of Engineering, RMIT University, Melbourne, VIC 3000, Australia; (T.F.); (H.P.); (A.G.)
| | - Alessandro Gardi
- School of Engineering, RMIT University, Melbourne, VIC 3000, Australia; (T.F.); (H.P.); (A.G.)
- Food Agility CRC Ltd., 81 Broadway, Melbourne, NSW 2007, Australia; (D.S.); (I.G.); (D.W.L.)
| | - Roberto Sabatini
- School of Engineering, RMIT University, Melbourne, VIC 3000, Australia; (T.F.); (H.P.); (A.G.)
- Food Agility CRC Ltd., 81 Broadway, Melbourne, NSW 2007, Australia; (D.S.); (I.G.); (D.W.L.)
| | - Dario Stefanelli
- Food Agility CRC Ltd., 81 Broadway, Melbourne, NSW 2007, Australia; (D.S.); (I.G.); (D.W.L.)
- Manjimup Centre, Department of Primary Industries and Regional Development, Western Australia, Private Bag 7, Manjimup, WA 6258, Australia
| | - Ian Goodwin
- Food Agility CRC Ltd., 81 Broadway, Melbourne, NSW 2007, Australia; (D.S.); (I.G.); (D.W.L.)
- Agriculture Victoria, Tatura, VIC 3616, Australia
| | - David William Lamb
- Food Agility CRC Ltd., 81 Broadway, Melbourne, NSW 2007, Australia; (D.S.); (I.G.); (D.W.L.)
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Growth Stage Classification and Harvest Scheduling of Snap Bean Using Hyperspectral Sensing: A Greenhouse Study. REMOTE SENSING 2020. [DOI: 10.3390/rs12223809] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
The agricultural industry suffers from a significant amount of food waste, some of which originates from an inability to apply site-specific management at the farm-level. Snap bean, a broad-acre crop that covers hundreds of thousands of acres across the USA, is not exempt from this need for informed, within-field, and spatially-explicit management approaches. This study aimed to assess the utility of machine learning algorithms for growth stage and pod maturity classification of snap bean (cv. Huntington), as well as detecting and discriminating spectral and biophysical features that lead to accurate classification results. Four major growth stages and six main sieve size pod maturity levels were evaluated for growth stage and pod maturity classification, respectively. A point-based in situ spectroradiometer in the visible-near-infrared and shortwave-infrared domains (VNIR-SWIR; 400–2500 nm) was used and the radiance values were converted to reflectance to normalize for any illumination change between samples. After preprocessing the raw data, we approached pod maturity assessment with multi-class classification and growth stage determination with binary and multi-class classification methods. Results from the growth stage assessment via the binary method exhibited accuracies ranging from 90–98%, with the best mathematical enhancement method being the continuum-removal approach. The growth stage multi-class classification method used raw reflectance data and identified a pair of wavelengths, 493 nm and 640 nm, in two basic transforms (ratio and normalized difference), yielding high accuracies (~79%). Pod maturity assessment detected narrow-band wavelengths in the VIS and SWIR region, separating between not ready-to-harvest and ready-to-harvest scenarios with classification measures at the ~78% level by using continuum-removed spectra. Our work is a best-case scenario, i.e., we consider it a stepping-stone to understanding snap bean harvest maturity assessment via hyperspectral sensing at a scalable level (i.e., airborne systems). Future work involves transferring the concepts to unmanned aerial system (UAS) field experiments and validating whether or not a simple multispectral camera, mounted on a UAS, could incorporate < 10 spectral bands to meet the need of both growth stage and pod maturity classification in snap bean production.
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Li M, Huang M, Zhu Q, Zhang M, Guo Y, Qin J. Pickled and dried mustard foreign matter detection using multispectral imaging system based on single shot method. J FOOD ENG 2020. [DOI: 10.1016/j.jfoodeng.2020.110106] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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Cardim Ferreira Lima M, Krus A, Valero C, Barrientos A, del Cerro J, Roldán-Gómez JJ. Monitoring Plant Status and Fertilization Strategy through Multispectral Images. SENSORS 2020; 20:s20020435. [PMID: 31941027 PMCID: PMC7014396 DOI: 10.3390/s20020435] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/17/2019] [Revised: 01/09/2020] [Accepted: 01/09/2020] [Indexed: 11/25/2022]
Abstract
A crop monitoring system was developed for the supervision of organic fertilization status on tomato plants at early stages. An automatic and nondestructive approach was used to analyze tomato plants with different levels of water-soluble organic fertilizer (3 + 5 NK) and vermicompost. The evaluation system was composed by a multispectral camera with five lenses: green (550 nm), red (660 nm), red edge (735 nm), near infrared (790 nm), RGB, and a computational image processing system. The water-soluble fertilizer was applied weekly in four different treatments: (T0: 0 mL, T1: 6.25 mL, T2: 12.5 mL and T3: 25 mL) and the vermicomposting was added in Weeks 1 and 5. The trial was conducted in a greenhouse and 192 images were taken with each lens. A plant segmentation algorithm was developed and several vegetation indices were calculated. On top of calculating indices, multiple morphological features were obtained through image processing techniques. The morphological features were revealed to be more feasible to distinguish between the control and the organic fertilized plants than the vegetation indices. The system was developed in order to be assembled in a precision organic fertilization robotic platform.
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Affiliation(s)
- Matheus Cardim Ferreira Lima
- Department of Agroforest Ecosystems, ETSI Agrónomos, Universidad Politécnica de Valencia, 46022 Valencia, Spain
- Research and Extension Unit (AGDR), Food and Agriculture Organization of the United Nations (FAO), 00153 Rome, Italy
- Correspondence:
| | - Anne Krus
- Department of Agroforest Engineering, ETSI Agronómica, Alimentaria y de Biosistemas, Universidad Politécnica de Madrid, 28040 Madrid, Spain; (A.K.); (C.V.)
| | - Constantino Valero
- Department of Agroforest Engineering, ETSI Agronómica, Alimentaria y de Biosistemas, Universidad Politécnica de Madrid, 28040 Madrid, Spain; (A.K.); (C.V.)
| | - Antonio Barrientos
- Centre for Automation and Robotics (CSIC-UPM), 28006 Madrid, Spain; (A.B.); (J.d.C.); or (J.J.R.-G.)
| | - Jaime del Cerro
- Centre for Automation and Robotics (CSIC-UPM), 28006 Madrid, Spain; (A.B.); (J.d.C.); or (J.J.R.-G.)
| | - Juan Jesús Roldán-Gómez
- Centre for Automation and Robotics (CSIC-UPM), 28006 Madrid, Spain; (A.B.); (J.d.C.); or (J.J.R.-G.)
- Department of Computer Engineering, Higher Polytechnic School, Autonomous University of Madrid (UAM), 28049 Madrid, Spain
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Oviposition Suitability of Drosophila Suzukii (Diptera: Drosophilidae) for Nectarine Varieties and Its Correlation with the Physiological Indexes. INSECTS 2019; 10:insects10080221. [PMID: 31344965 PMCID: PMC6723238 DOI: 10.3390/insects10080221] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 04/12/2019] [Revised: 07/09/2019] [Accepted: 07/20/2019] [Indexed: 11/16/2022]
Abstract
The nectarine is an important fruit, which is attacked by Drosophila suzukii in Europe and the United States but there are no reports of it attacking nectarines in China. Here, we determined the oviposition preference of D. suzukii six on intact and sliced nectarine varieties in China and how physical and physiological indexes of the fruit correlate with these preferences. D. suzukii were allowed to oviposit on two early–, two middle– and two late–maturing varieties of nectarine—Shuguang and Chunguang, Fengguang and Zhong you 4, Zhong you 7 and Zhong you 8, respectively and the number of larvae also followed the order. The firmness, soluble solids content and the nutritional components of the amino acid, protein, soluble sugar and pectin contents of each variety were measured. D. suzukii preferred the early Shuguang variety, followed by the early Chunguang variety and then the middle Zhong you 4 and Fengguang varieties. Taken together, results show that D. suzukii shows preferences for earlier rather than later varieties of nectarines in China and that these preferences are related to the fruit’s physical and physiological traits. Results suggest that mixed cultivation of early–, middle– and late–maturing nectarine varieties should be avoided in order to prevent fly dispersal and infestation by D. suzukii.
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Yeong TJ, Pin Jern K, Yao LK, Hannan MA, Hoon STG. Applications of Photonics in Agriculture Sector: A Review. Molecules 2019; 24:E2025. [PMID: 31137897 PMCID: PMC6571790 DOI: 10.3390/molecules24102025] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2019] [Revised: 05/10/2019] [Accepted: 05/12/2019] [Indexed: 11/17/2022] Open
Abstract
The agricultural industry has made a tremendous contribution to the foundations of civilization. Basic essentials such as food, beverages, clothes and domestic materials are enriched by the agricultural industry. However, the traditional method in agriculture cultivation is labor-intensive and inadequate to meet the accelerating nature of human demands. This scenario raises the need to explore state-of-the-art crop cultivation and harvesting technologies. In this regard, optics and photonics technologies have proven to be effective solutions. This paper aims to present a comprehensive review of three photonic techniques, namely imaging, spectroscopy and spectral imaging, in a comparative manner for agriculture applications. Essentially, the spectral imaging technique is a robust solution which combines the benefits of both imaging and spectroscopy but faces the risk of underutilization. This review also comprehends the practicality of all three techniques by presenting existing examples in agricultural applications. Furthermore, the potential of these techniques is reviewed and critiqued by looking into agricultural activities involving palm oil, rubber, and agro-food crops. All the possible issues and challenges in implementing the photonic techniques in agriculture are given prominence with a few selective recommendations. The highlighted insights in this review will hopefully lead to an increased effort in the development of photonics applications for the future agricultural industry.
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Affiliation(s)
- Tan Jin Yeong
- Institute of Power Engineering, College of Engineering, Universiti Tenaga Nasional, Kajang 43000, Selangor, Malaysia.
| | - Ker Pin Jern
- Institute of Power Engineering, College of Engineering, Universiti Tenaga Nasional, Kajang 43000, Selangor, Malaysia.
| | - Lau Kuen Yao
- Institute of Power Engineering, College of Engineering, Universiti Tenaga Nasional, Kajang 43000, Selangor, Malaysia.
| | - M A Hannan
- Institute of Power Engineering, College of Engineering, Universiti Tenaga Nasional, Kajang 43000, Selangor, Malaysia.
| | - Shirley Tang Gee Hoon
- Microbiology Unit, Department of Pre-clinical, International Medical School, Management and Science University, University Drive, Off Persiaran Olahraga, Seksyen 13, Shah Alam 40100, Selangor, Malaysia.
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The Optimal Local Model Selection for Robust and Fast Evaluation of Soluble Solid Content in Melon with Thick Peel and Large Size by Vis-NIR Spectroscopy. FOOD ANAL METHOD 2018. [DOI: 10.1007/s12161-018-1346-3] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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Srivastava S, Sadistap S. Data processing approaches and strategies for non-destructive fruits quality inspection and authentication: a review. JOURNAL OF FOOD MEASUREMENT AND CHARACTERIZATION 2018. [DOI: 10.1007/s11694-018-9893-2] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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Comparison and Optimization of Models for Determination of Sugar Content in Pear by Portable Vis-NIR Spectroscopy Coupled with Wavelength Selection Algorithm. FOOD ANAL METHOD 2018. [DOI: 10.1007/s12161-018-1326-7] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/28/2022]
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13
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Hashim N, Onwude DI, Osman MS. Evaluation of Chilling Injury in Mangoes Using Multispectral Imaging. J Food Sci 2018; 83:1271-1279. [PMID: 29660789 DOI: 10.1111/1750-3841.14127] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2017] [Revised: 02/04/2018] [Accepted: 02/22/2018] [Indexed: 11/27/2022]
Abstract
Commodities originating from tropical and subtropical climes are prone to chilling injury (CI). This injury could affect the quality and marketing potential of mango after harvest. This will later affect the quality of the produce and subsequent consumer acceptance. In this study, the appearance of CI symptoms in mango was evaluated non-destructively using multispectral imaging. The fruit were stored at 4 °C to induce CI and 12 °C to preserve the quality of the control samples for 4 days before they were taken out and stored at ambient temperature for 24 hr. Measurements using multispectral imaging and standard reference methods were conducted before and after storage. The performance of multispectral imaging was compared using standard reference properties including moisture content (MC), total soluble solids (TSS) content, firmness, pH, and color. Least square support vector machine (LS-SVM) combined with principal component analysis (PCA) were used to discriminate CI samples with those of control and before storage, respectively. The statistical results demonstrated significant changes in the reference quality properties of samples before and after storage. The results also revealed that multispectral parameters have a strong correlation with the reference parameters of L* , a* , TSS, and MC. The MC and L* were found to be the best reference parameters in identifying the severity of CI in mangoes. PCA and LS-SVM analysis indicated that the fruit were successfully classified into their categories, that is, before storage, control, and CI. This indicated that the multispectral imaging technique is feasible for detecting CI in mangoes during postharvest storage and processing. PRACTICAL APPLICATION This paper demonstrates a fast, easy, and accurate method of identifying the effect of cold storage on mango, nondestructively. The method presented in this paper can be used industrially to efficiently differentiate different fruits from each other after low temperature storage.
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Affiliation(s)
- Norhashila Hashim
- Dept. of Biological and Agricultural Engineering, Faculty of Engineering, Univ. Putra Malaysia, 43400, Serdang, Selangor, Malaysia.,SMART Farming Technology Research Center (SFTRC), Faculty of Engineering, Univ. Putra Malaysia, 43400, UPM Serdang, Selangor, Malaysia
| | - Daniel I Onwude
- Dept. of Biological and Agricultural Engineering, Faculty of Engineering, Univ. Putra Malaysia, 43400, Serdang, Selangor, Malaysia.,Dept. of Agricultural and Food Engineering, Faculty of Engineering, Univ. of Uyo, 52101 Uyo, Nigeria
| | - Muhamad Syafiq Osman
- Dept. of Biological and Agricultural Engineering, Faculty of Engineering, Univ. Putra Malaysia, 43400, Serdang, Selangor, Malaysia
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Li B, Lecourt J, Bishop G. Advances in Non-Destructive Early Assessment of Fruit Ripeness towards Defining Optimal Time of Harvest and Yield Prediction-A Review. PLANTS (BASEL, SWITZERLAND) 2018; 7:E3. [PMID: 29320410 PMCID: PMC5874592 DOI: 10.3390/plants7010003] [Citation(s) in RCA: 72] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/07/2017] [Revised: 01/05/2018] [Accepted: 01/08/2018] [Indexed: 11/17/2022]
Abstract
Global food security for the increasing world population not only requires increased sustainable production of food but a significant reduction in pre- and post-harvest waste. The timing of when a fruit is harvested is critical for reducing waste along the supply chain and increasing fruit quality for consumers. The early in-field assessment of fruit ripeness and prediction of the harvest date and yield by non-destructive technologies have the potential to revolutionize farming practices and enable the consumer to eat the tastiest and freshest fruit possible. A variety of non-destructive techniques have been applied to estimate the ripeness or maturity but not all of them are applicable for in situ (field or glasshouse) assessment. This review focuses on the non-destructive methods which are promising for, or have already been applied to, the pre-harvest in-field measurements including colorimetry, visible imaging, spectroscopy and spectroscopic imaging. Machine learning and regression models used in assessing ripeness are also discussed.
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Affiliation(s)
- Bo Li
- NIAB EMR, East Malling, Kent ME19 6BJ, UK.
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15
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Potential of multispectral imaging combined with chemometric methods for rapid detection of sucrose adulteration in tomato paste. J FOOD ENG 2017. [DOI: 10.1016/j.jfoodeng.2017.07.026] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
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16
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Non-destructive sensing methods for quality assessment of on-tree fruits: a review. JOURNAL OF FOOD MEASUREMENT AND CHARACTERIZATION 2017. [DOI: 10.1007/s11694-017-9663-6] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
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17
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Liu C, Liu W, Yang J, Chen Y, Zheng L. Non-destructive detection of dicyandiamide in infant formula powder using multi-spectral imaging coupled with chemometrics. JOURNAL OF THE SCIENCE OF FOOD AND AGRICULTURE 2017; 97:2094-2099. [PMID: 27570201 DOI: 10.1002/jsfa.8014] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/29/2015] [Revised: 06/06/2016] [Accepted: 08/24/2016] [Indexed: 06/06/2023]
Abstract
BACKGROUND Dicyandiamide (DCD) contamination of milk and milk products has become an urgent and broadly recognised topic as a result of several food safety scares. This study investigated the potential of using multi-spectral imaging (405-970 nm) coupled with chemometrics for detection of DCD in infant formula powder. Partial least squares (PLS), least squares-support vector machines (LS-SVM), and back-propagation neural network (BPNN) were applied to develop quantitative models. RESULTS Compared with PLS and LS-SVM, BPNN considerably improved the prediction performance with coefficient of determination in prediction (RP2) = 0.935 and 0.873, residual predictive deviation (RPD) = 3.777 and 3.060 for brand 1 and brand 2 of infant formula powders, respectively. Besides, multi-spectral imaging was able to differentiate unadulterated infant formula powder from samples containing 0.01% DCD with no misclassification using BPNN model. CONCLUSION The study demonstrated that multi-spectral imaging combined with chemometrics enables rapid and non-destructive detection of DCD in infant formula powder. © 2016 Society of Chemical Industry.
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Affiliation(s)
- Changhong Liu
- College of Food Science and Engineering, Hefei University of Technology, Hefei, 230009, China
| | - Wei Liu
- Intelligent Control and Compute Vision Lab, Hefei University, Hefei, 230601, China
| | - Jianbo Yang
- Rice Research Institute, Anhui Academy of Agricultural Sciences, Hefei, 230031, China
| | - Ying Chen
- Agro-product Safety Research Centre, Chinese Academy of Inspection and Quarantine, Beijing, 100123, China
| | - Lei Zheng
- College of Food Science and Engineering, Hefei University of Technology, Hefei, 230009, China
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Xiong C, Liu C, Liu W, Pan W, Ma F, Chen W, Chen F, Yang J, Zheng L. Noninvasive discrimination and textural properties of E-beam irradiated shrimp. J FOOD ENG 2016. [DOI: 10.1016/j.jfoodeng.2015.12.008] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
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19
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Xia Q, Liu C, Liu J, Pan W, Lu X, Yang J, Chen W, Zheng L. Rapid and non-destructive determination of rancidity levels in butter cookies by multi-spectral imaging. JOURNAL OF THE SCIENCE OF FOOD AND AGRICULTURE 2016; 96:1821-1827. [PMID: 26041533 DOI: 10.1002/jsfa.7292] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/15/2015] [Revised: 05/22/2015] [Accepted: 05/29/2015] [Indexed: 06/04/2023]
Abstract
BACKGROUND Rancidity is an important attribute for quality assessment of butter cookies, while traditional methods for rancidity measurement are usually laborious, destructive and prone to operational error. In the present paper, the potential of applying multi-spectral imaging (MSI) technology with 19 wavelengths in the range of 405-970 nm to evaluate the rancidity in butter cookies was investigated. RESULTS Moisture content, acid value and peroxide value were determined by traditional methods and then related with the spectral information by partial least squares regression (PLSR) and back-propagation artificial neural network (BP-ANN). The optimal models for predicting moisture content, acid value and peroxide value were obtained by PLSR. The correlation coefficient (r) obtained by PLSR models revealed that MSI had a perfect ability to predict moisture content (r = 0.909), acid value (r = 0.944) and peroxide value (r = 0.971). CONCLUSION The study demonstrated that the rancidity level of butter cookies can be continuously monitored and evaluated in real-time by the multi-spectral imaging, which is of great significance for developing online food safety monitoring solutions.
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Affiliation(s)
- Qing Xia
- School of Biotechnology and Food Engineering, Hefei University of Technology, Hefei, 230009, China
| | - Changhong Liu
- School of Biotechnology and Food Engineering, Hefei University of Technology, Hefei, 230009, China
| | - Jinxia Liu
- School of Biotechnology and Food Engineering, Hefei University of Technology, Hefei, 230009, China
| | - Wenjuan Pan
- School of Biotechnology and Food Engineering, Hefei University of Technology, Hefei, 230009, China
| | - Xuzhong Lu
- Rice Research Institute, Anhui Academy of Agricultural Sciences, Hefei, 230031, China
| | - Jianbo Yang
- Rice Research Institute, Anhui Academy of Agricultural Sciences, Hefei, 230031, China
| | - Wei Chen
- School of Biotechnology and Food Engineering, Hefei University of Technology, Hefei, 230009, China
| | - Lei Zheng
- School of Biotechnology and Food Engineering, Hefei University of Technology, Hefei, 230009, China
- School of Medical Engineering, Hefei University of Technology, Hefei, 230009, China
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Wang X, Wu C, Hirafuji M. Visible Light Image-Based Method for Sugar Content Classification of Citrus. PLoS One 2016; 11:e0147419. [PMID: 26811935 PMCID: PMC4727814 DOI: 10.1371/journal.pone.0147419] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2015] [Accepted: 01/04/2016] [Indexed: 12/05/2022] Open
Abstract
Visible light imaging of citrus fruit from Mie Prefecture of Japan was performed to determine whether an algorithm could be developed to predict the sugar content. This nondestructive classification showed that the accurate segmentation of different images can be realized by a correlation analysis based on the threshold value of the coefficient of determination. There is an obvious correlation between the sugar content of citrus fruit and certain parameters of the color images. The selected image parameters were connected by addition algorithm. The sugar content of citrus fruit can be predicted by the dummy variable method. The results showed that the small but orange citrus fruits often have a high sugar content. The study shows that it is possible to predict the sugar content of citrus fruit and to perform a classification of the sugar content using light in the visible spectrum and without the need for an additional light source.
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Affiliation(s)
- Xuefeng Wang
- Research Institute of Forest Resource Information Techniques, Chinese Academy of Forestry, Beijing, China
| | - Chunyan Wu
- Research Institute of Forest Resource Information Techniques, Chinese Academy of Forestry, Beijing, China
| | - Masayuki Hirafuji
- Hokkaido Agricultural Research Center, Memuro-cho, Kasai-gun, Japan
- * E-mail:
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21
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Adebayo SE, Hashim N, Abdan K, Hanafi M. Application and potential of backscattering imaging techniques in agricultural and food processing – A review. J FOOD ENG 2016. [DOI: 10.1016/j.jfoodeng.2015.08.006] [Citation(s) in RCA: 46] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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22
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Giovenzana V, Beghi R, Civelli R, Guidetti R. Optical techniques for rapid quality monitoring along minimally processed fruit and vegetable chain. Trends Food Sci Technol 2015. [DOI: 10.1016/j.tifs.2015.10.006] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
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23
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Evaluation of Techniques for Automatic Classification of Lettuce Based on Spectral Reflectance. FOOD ANAL METHOD 2015. [DOI: 10.1007/s12161-015-0366-5] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/17/2023]
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24
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Xiong C, Liu C, Pan W, Ma F, Xiong C, Qi L, Chen F, Lu X, Yang J, Zheng L. Non-destructive determination of total polyphenols content and classification of storage periods of Iron Buddha tea using multispectral imaging system. Food Chem 2015; 176:130-6. [DOI: 10.1016/j.foodchem.2014.12.057] [Citation(s) in RCA: 45] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2014] [Revised: 12/03/2014] [Accepted: 12/13/2014] [Indexed: 11/25/2022]
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25
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Fruit quality evaluation using spectroscopy technology: a review. SENSORS 2015; 15:11889-927. [PMID: 26007736 PMCID: PMC4481958 DOI: 10.3390/s150511889] [Citation(s) in RCA: 105] [Impact Index Per Article: 11.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 03/17/2015] [Revised: 05/14/2015] [Accepted: 05/18/2015] [Indexed: 11/26/2022]
Abstract
An overview is presented with regard to applications of visible and near infrared (Vis/NIR) spectroscopy, multispectral imaging and hyperspectral imaging techniques for quality attributes measurement and variety discrimination of various fruit species, i.e., apple, orange, kiwifruit, peach, grape, strawberry, grape, jujube, banana, mango and others. Some commonly utilized chemometrics including pretreatment methods, variable selection methods, discriminant methods and calibration methods are briefly introduced. The comprehensive review of applications, which concentrates primarily on Vis/NIR spectroscopy, are arranged according to fruit species. Most of the applications are focused on variety discrimination or the measurement of soluble solids content (SSC), acidity and firmness, but also some measurements involving dry matter, vitamin C, polyphenols and pigments have been reported. The feasibility of different spectral modes, i.e., reflectance, interactance and transmittance, are discussed. Optimal variable selection methods and calibration methods for measuring different attributes of different fruit species are addressed. Special attention is paid to sample preparation and the influence of the environment. Areas where further investigation is needed and problems concerning model robustness and model transfer are identified.
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26
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Feasibility in multispectral imaging for predicting the content of bioactive compounds in intact tomato fruit. Food Chem 2015; 173:482-8. [DOI: 10.1016/j.foodchem.2014.10.052] [Citation(s) in RCA: 62] [Impact Index Per Article: 6.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2014] [Revised: 10/07/2014] [Accepted: 10/09/2014] [Indexed: 11/23/2022]
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Kapetanakou A, Stragkas I, Skandamis P. Developing an antimicrobial packaging of ready-to-eat pomegranate arils based on vapors of brandy or distillery ethanol. Food Res Int 2015. [DOI: 10.1016/j.foodres.2014.12.006] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/17/2023]
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28
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Discrimination of Kernel Quality Characteristics for Sunflower Seeds Based on Multispectral Imaging Approach. FOOD ANAL METHOD 2014. [DOI: 10.1007/s12161-014-0038-x] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/24/2022]
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29
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Ma F, Yao J, Xie T, Liu C, Chen W, Chen C, Zheng L. Multispectral imaging for rapid and non-destructive determination of aerobic plate count (APC) in cooked pork sausages. Food Res Int 2014. [DOI: 10.1016/j.foodres.2014.05.010] [Citation(s) in RCA: 34] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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30
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Nondestructive determination of transgenic Bacillus thuringiensis rice seeds (Oryza sativa L.) using multispectral imaging and chemometric methods. Food Chem 2014; 153:87-93. [DOI: 10.1016/j.foodchem.2013.11.166] [Citation(s) in RCA: 69] [Impact Index Per Article: 6.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2013] [Revised: 11/07/2013] [Accepted: 11/24/2013] [Indexed: 11/19/2022]
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31
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Liu C, Liu W, Lu X, Ma F, Chen W, Yang J, Zheng L. Application of multispectral imaging to determine quality attributes and ripeness stage in strawberry fruit. PLoS One 2014; 9:e87818. [PMID: 24505317 PMCID: PMC3913704 DOI: 10.1371/journal.pone.0087818] [Citation(s) in RCA: 31] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2013] [Accepted: 01/02/2014] [Indexed: 12/04/2022] Open
Abstract
Multispectral imaging with 19 wavelengths in the range of 405–970 nm has been evaluated for nondestructive determination of firmness, total soluble solids (TSS) content and ripeness stage in strawberry fruit. Several analysis approaches, including partial least squares (PLS), support vector machine (SVM) and back propagation neural network (BPNN), were applied to develop theoretical models for predicting the firmness and TSS of intact strawberry fruit. Compared with PLS and SVM, BPNN considerably improved the performance of multispectral imaging for predicting firmness and total soluble solids content with the correlation coefficient (r) of 0.94 and 0.83, SEP of 0.375 and 0.573, and bias of 0.035 and 0.056, respectively. Subsequently, the ability of multispectral imaging technology to classify fruit based on ripeness stage was tested using SVM and principal component analysis-back propagation neural network (PCA-BPNN) models. The higher classification accuracy of 100% was achieved using SVM model. Moreover, the results of all these models demonstrated that the VIS parts of the spectra were the main contributor to the determination of firmness, TSS content estimation and classification of ripeness stage in strawberry fruit. These results suggest that multispectral imaging, together with suitable analysis model, is a promising technology for rapid estimation of quality attributes and classification of ripeness stage in strawberry fruit.
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Affiliation(s)
- Changhong Liu
- School of Biotechnology and Food Engineering, Hefei University of Technology, Hefei, China
| | - Wei Liu
- Intelligent Control and Compute Vision Lab, Hefei University, Hefei, China
| | - Xuzhong Lu
- Institute of Rice Research, Anhui Academy of Agricultural Sciences, Hefei, China
| | - Fei Ma
- School of Biotechnology and Food Engineering, Hefei University of Technology, Hefei, China
| | - Wei Chen
- School of Biotechnology and Food Engineering, Hefei University of Technology, Hefei, China
- * E-mail: (WC); (JY); (LZ)
| | - Jianbo Yang
- Institute of Rice Research, Anhui Academy of Agricultural Sciences, Hefei, China
- * E-mail: (WC); (JY); (LZ)
| | - Lei Zheng
- School of Biotechnology and Food Engineering, Hefei University of Technology, Hefei, China
- School of Medical Engineering, Hefei University of Technology, Hefei, China
- * E-mail: (WC); (JY); (LZ)
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Saldaña E, Siche R, Luján M, Quevedo R. Review: computer vision applied to the inspection and quality control of fruits and vegetables. BRAZILIAN JOURNAL OF FOOD TECHNOLOGY 2013. [DOI: 10.1590/s1981-67232013005000031] [Citation(s) in RCA: 29] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
Abstract
This is a review of the current existing literature concerning the inspection of fruits and vegetables with the application of computer vision, where the techniques most used to estimate various properties related to quality are analyzed. The objectives of the typical applications of such systems include the classification, quality estimation according to the internal and external characteristics, supervision of fruit processes during storage or the evaluation of experimental treatments. In general, computer vision systems do not only replace manual inspection, but can also improve their skills. In conclusion, computer vision systems are powerful tools for the automatic inspection of fruits and vegetables. In addition, the development of such systems adapted to the food industry is fundamental to achieve competitive advantages.
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Qin J, Chao K, Kim MS, Lu R, Burks TF. Hyperspectral and multispectral imaging for evaluating food safety and quality. J FOOD ENG 2013. [DOI: 10.1016/j.jfoodeng.2013.04.001] [Citation(s) in RCA: 207] [Impact Index Per Article: 18.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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34
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Combination of digital images and laser light to predict moisture content and color of bell pepper simultaneously during drying. J FOOD ENG 2012. [DOI: 10.1016/j.jfoodeng.2011.10.037] [Citation(s) in RCA: 62] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
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35
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Herrero-Langreo A, Fernández-Ahumada E, Roger JM, Palagós B, Lleó L. Combination of optical and non-destructive mechanical techniques for the measurement of maturity in peach. J FOOD ENG 2012. [DOI: 10.1016/j.jfoodeng.2011.07.004] [Citation(s) in RCA: 30] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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36
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Lorente D, Aleixos N, Gómez-Sanchis J, Cubero S, García-Navarrete OL, Blasco J. Recent Advances and Applications of Hyperspectral Imaging for Fruit and Vegetable Quality Assessment. FOOD BIOPROCESS TECH 2011. [DOI: 10.1007/s11947-011-0725-1] [Citation(s) in RCA: 253] [Impact Index Per Article: 19.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/15/2022]
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37
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Lleó L, Roger J, Herrero-Langreo A, Diezma-Iglesias B, Barreiro P. Comparison of multispectral indexes extracted from hyperspectral images for the assessment of fruit ripening. J FOOD ENG 2011. [DOI: 10.1016/j.jfoodeng.2011.01.028] [Citation(s) in RCA: 49] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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38
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Herrero-Langreo A, Lunadei L, Lleó L, Diezma B, Ruiz-Altisent M. Multispectral vision for monitoring peach ripeness. J Food Sci 2011; 76:E178-87. [PMID: 21535757 DOI: 10.1111/j.1750-3841.2010.02000.x] [Citation(s) in RCA: 12] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
UNLABELLED The main objective of this research was to develop an automatic procedure able to classify Rich Lady commercial peaches according to their ripeness stage through multispectral imaging techniques. A classification procedure was applied to the ratio images calculated as red (R, 680 nm) divided by infrared (IR, 800 nm), that is, R/IR images. Four image-based ripeness reference classes (A: unripe to D: overripe) were generated from 380 fruit images (season 1: 2006) by a nonsupervised classification method and evaluated according to reference measurements of the ripeness of the same samples: Magness-Taylor penetrometry firmness, low-mass impact firmness, reflectance at 680 nm (R680, and soluble solids content. The assignment of unknown sample images from those season 1 images (internal validation, n = 380) and of 240 images from the 2nd season (season 2: 2007) to the ripeness reference classes (external validation) was carried out by computing the minimum Euclidean distance (classification distance, C(d)) between each unknown image histogram and the average histogram of each ripeness reference class. For both validation phases, firmness values decreased and R680 increased for increasing alphabetical order of image-based class letter, reflecting the ripening process. Moreover, 70% (season 1) and 80% (season 2) of the samples below bruise susceptibility firmness were classified into class D. PRACTICAL APPLICATION This work proposes and validates a procedure for assessing peach ripeness through spectral imaging. The control of ripeness in this fruit is crucial for ensuring its quality and the measurement of optimum peach ripeness at harvest and postharvest is a controversial issue, which needs to be balanced between a minimum ripeness, acceptable for the consumer, and a maximum ripeness, to minimize fruit losses during the postharvest process. The proposed method is nondestructive and quick, showing thus, a good perspective for its application in fresh fruit packing lines, either for peach ripeness assessment or for other fruits (providing adequate calibration).
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Affiliation(s)
- Ana Herrero-Langreo
- Laboratorio de Propiedades Físicas y Tecnologías Avanzadas en Agroalimentación, ETSI Agrónomos, Univ Politécnica de Madrid, Avda Complutense s/n, Madrid 28040, Spain.
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Advances in Machine Vision Applications for Automatic Inspection and Quality Evaluation of Fruits and Vegetables. FOOD BIOPROCESS TECH 2010. [DOI: 10.1007/s11947-010-0411-8] [Citation(s) in RCA: 207] [Impact Index Per Article: 14.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
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