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12 pages, 3478 KiB

Open AccessArticle

Characterizing Hyperspectral Microscope Imagery for Classification of Blueberry Firmness with Deep Learning Methods

by Bosoon Park, Tae-Sung Shin, Jeong-Seok Cho, Jeong-Ho Lim and Ki-Jae Park

Agronomy 2022, 12(1), 85; https://doi.org/10.3390/agronomy12010085 - 30 Dec 2021

Cited by 9 | Viewed by 3164

Abstract

Firmness is an important quality indicator of blueberries. Firmness loss (or softening) of postharvest blueberries has posed a challenge in its shelf-life quality control and can be delineated with its microstructural changes. To investigate spatial and spectral characteristics of microstructures based on firmness, [...] Read more.

Firmness is an important quality indicator of blueberries. Firmness loss (or softening) of postharvest blueberries has posed a challenge in its shelf-life quality control and can be delineated with its microstructural changes. To investigate spatial and spectral characteristics of microstructures based on firmness, hyperspectral microscope imaging (HMI) was employed for this study. The mesocarp area with 20× magnification of blueberries was selectively imaged with a Fabry–Perot interferometer HMI system of 400–1000 nm wavelengths, resulting in 281 hypercubes of parenchyma cells in a resolution of 968 × 608 × 300 pixels. After properly processing each hypercube of parenchyma cells in a blueberry, the cell image with different firmness was examined based on parenchyma cell shape, cell wall segment, cell-to-cell adhesion, and size of intercellular spaces. Spectral cell characteristics of firmness were also sought based on the spectral profile of cell walls with different image preprocessing methods. The study found that softer blueberries (1.96–3.92 N) had more irregular cell shapes, lost cell-to-cell adhesion, loosened and round cell wall segments, large intercellular spaces, and cell wall colors that were more red than the firm blueberries (6.86–8.83 N). Even though berry-to-berry (or image-to-image) variations of the characteristics turned out large, the deep learning model with spatial and spectral features of blueberry cells demonstrated the potential for blueberry firmness classification with Matthew’s correlation coefficient of 73.4% and accuracy of 85% for test set. Full article

(This article belongs to the Special Issue Application of Computer Vision on Quality Monitoring of Agricultural Products)

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14 pages, 8779 KiB

Open AccessEditor’s ChoiceArticle

Development of Monitoring Robot System for Tomato Fruits in Hydroponic Greenhouses

by Dasom Seo, Byeong-Hyo Cho and Kyoung-Chul Kim

Agronomy 2021, 11(11), 2211; https://doi.org/10.3390/agronomy11112211 - 31 Oct 2021

Cited by 38 | Viewed by 5836

Abstract

Crop monitoring is highly important in terms of the efficient and stable performance of tasks such as planting, spraying, and harvesting, and for this reason, several studies are being conducted to develop and improve crop monitoring robots. In addition, the applications of deep [...] Read more.

Crop monitoring is highly important in terms of the efficient and stable performance of tasks such as planting, spraying, and harvesting, and for this reason, several studies are being conducted to develop and improve crop monitoring robots. In addition, the applications of deep learning algorithms are increasing in the development of agricultural robots since deep learning algorithms that use convolutional neural networks have been proven to show outstanding performance in image classification, segmentation, and object detection. However, most of these applications are focused on the development of harvesting robots, and thus, there are only a few studies that improve and develop monitoring robots through the use of deep learning. For this reason, we aimed to develop a real-time robot monitoring system for the generative growth of tomatoes. The presented method detects tomato fruits grown in hydroponic greenhouses using the Faster R-CNN (region-based convolutional neural network). In addition, we sought to select a color model that was robust to external light, and we used hue values to develop an image-based maturity standard for tomato fruits; furthermore, the developed maturity standard was verified through comparison with expert classification. Finally, the number of tomatoes was counted using a centroid-based tracking algorithm. We trained the detection model using an open dataset and tested the whole system in real-time in a hydroponic greenhouse. A total of 53 tomato fruits were used to verify the developed system, and the developed system achieved 88.6% detection accuracy when completely obscured fruits not captured by the camera were included. When excluding obscured fruits, the system’s accuracy was 90.2%. For the maturity classification, we conducted qualitative evaluations with the assistance of experts. Full article

(This article belongs to the Special Issue Application of Computer Vision on Quality Monitoring of Agricultural Products)

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15 pages, 3123 KiB

Open AccessArticle

Image Analysis Reveals That Lenticel Damage Does Not Result in Black Spot Development but Enhances Dehydration in Persea americana Mill. cv. Hass during Prolonged Storage

by Vicente Lindh, Virgilio Uarrota, Claudio Zulueta, Juan E. Alvaro, Monika Valdenegro, Italo F. Cuneo, Domingo Mery and Romina Pedreschi

Agronomy 2021, 11(9), 1699; https://doi.org/10.3390/agronomy11091699 - 26 Aug 2021

Cited by 8 | Viewed by 3940

Abstract

Black spot corresponds to a physiological disorder of the type of oxidative stress that occurs after the prolonged postharvest storage of Persea americana Mill. cv. Hass fruit. Industry tends to confuse this disorder with pathogen attack (Colletotrichum gloeosporioides), chilling injury, mechanical [...] Read more.

Black spot corresponds to a physiological disorder of the type of oxidative stress that occurs after the prolonged postharvest storage of Persea americana Mill. cv. Hass fruit. Industry tends to confuse this disorder with pathogen attack (Colletotrichum gloeosporioides), chilling injury, mechanical damage during harvest and transport or lenticel damage. The main objectives of this research were: (i) to develop a method to assess and differentiate lenticel damage and black spot and (ii) to study the correlation between mechanical damage and lenticel damage on the development of black spot. Avocado fruits from different orchards were evaluated at two sampling times using different harvesting systems (conventional and appropriate) and at two times of the day (a.m. or p.m.). Here, we report a method based on image analysis to differentiate and quantify lenticel damage and black spot disorder. In addition, the results show that conventional harvest increased lenticel damage and lenticel damage did not correlate with black spot development but correlated with increased weight loss during prolonged postharvest storage. These results have important commercial implications since the appropriate harvesting of avocado cv. Hass would not only control the incidence of lenticel damage, which would be an advantage in terms of external quality, but also reduce weight loss during transport to distant markets. Full article

(This article belongs to the Special Issue Application of Computer Vision on Quality Monitoring of Agricultural Products)

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17 pages, 1984 KiB

Open AccessArticle

Self-Configuring CVS to Discriminate Rocket Leaves According to Cultivation Practices and to Correctly Attribute Visual Quality Level

by Michela Palumbo, Bernardo Pace, Maria Cefola, Francesco Fabiano Montesano, Francesco Serio, Giancarlo Colelli and Giovanni Attolico

Agronomy 2021, 11(7), 1353; https://doi.org/10.3390/agronomy11071353 - 1 Jul 2021

Cited by 12 | Viewed by 2721

Abstract

Computer Vision Systems (CVS) represent a contactless and non-destructive tool to evaluate and monitor the quality of fruits and vegetables. This research paper proposes an innovative CVS, using a Random Forest model to automatically select the relevant features for classification, thereby avoiding their [...] Read more.

Computer Vision Systems (CVS) represent a contactless and non-destructive tool to evaluate and monitor the quality of fruits and vegetables. This research paper proposes an innovative CVS, using a Random Forest model to automatically select the relevant features for classification, thereby avoiding their choice through a cumbersome and error-prone work of human designers. Moreover, three color correction techniques were evaluated and compared, in terms of classification performance to identify the best solution to provide consistent color measurements. The proposed CVS was applied to fresh-cut rocket, produced under greenhouse soilless cultivation conditions differing for the irrigation management strategy and the fertilization level. The first aim of this study was to objectively estimate the quality levels (QL) occurring during storage. The second aim was to non-destructively, and in a contactless manner, identify the cultivation approach using the digital images of the obtained product. The proposed CVS achieved an accuracy of about 95% in QL assessment and about 65–70% in the discrimination of the cultivation approach. Full article

(This article belongs to the Special Issue Application of Computer Vision on Quality Monitoring of Agricultural Products)

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18 pages, 6573 KiB

Open AccessArticle

Automated Detection of Tetranychus urticae Koch in Citrus Leaves Based on Colour and VIS/NIR Hyperspectral Imaging

by María Gyomar Gonzalez-Gonzalez, Jose Blasco, Sergio Cubero and Patricia Chueca

Agronomy 2021, 11(5), 1002; https://doi.org/10.3390/agronomy11051002 - 18 May 2021

Cited by 8 | Viewed by 5321

Abstract

Tetranychus urticae Koch is an important citrus pest that produces chlorotic spots on the leaves and scars on the fruit of affected trees. It is detected by visual inspection of the leaves. This work studies the potential of colour and hyperspectral imaging (400–1000 [...] Read more.

Tetranychus urticae Koch is an important citrus pest that produces chlorotic spots on the leaves and scars on the fruit of affected trees. It is detected by visual inspection of the leaves. This work studies the potential of colour and hyperspectral imaging (400–1000 nm) under laboratory conditions as a fast and automatic method to detect the damage caused by this pest. The ability of a traditional vision system to differentiate this pest from others, such as Phyllocnistis citrella, and other leaf problems such as those caused by nutritional deficiencies, has been studied and compared with a more advanced hyperspectral system. To analyse the colour images, discriminant analysis has been used to classify the pixels as belonging to either a damaged or healthy leaves. In contrast, the hyperspectral images have been analysed using PLS DA. The rate of detection of the damage caused by T. urticae with colour images reached 92.5%, while leaves that did not present any damage were all correctly identified. Other problems such as damage by P. citrella were also correctly discriminated from T. urticae. Moreover, hyperspectral imaging allowed damage caused by T. urticae to be discriminated from healthy leaves and to distinguish between recent and mature leaves, which indicates whether it is a recent or an older infestation. Furthermore, good results were achieved in the discrimination between damage caused by T. urticae, P. citrella, and nutritional deficiencies. Full article

(This article belongs to the Special Issue Application of Computer Vision on Quality Monitoring of Agricultural Products)

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23 pages, 25421 KiB

Open AccessEditor’s ChoiceArticle

Seed Morphology in Key Spanish Grapevine Cultivars

by Emilio Cervantes, José Javier Martín-Gómez, Francisco Emmanuel Espinosa-Roldán, Gregorio Muñoz-Organero, Ángel Tocino and Félix Cabello-Sáenz de Santamaría

Agronomy 2021, 11(4), 734; https://doi.org/10.3390/agronomy11040734 - 10 Apr 2021

Cited by 17 | Viewed by 3099

Abstract

Ampelography, the botanical discipline dedicated to the identification and classification of grapevine cultivars, was grounded on the description of morphological characters and more recently is based on the application of DNA polymorphisms. New methods of image analysis may help to optimize morphological approaches [...] Read more.

Ampelography, the botanical discipline dedicated to the identification and classification of grapevine cultivars, was grounded on the description of morphological characters and more recently is based on the application of DNA polymorphisms. New methods of image analysis may help to optimize morphological approaches in ampelography. The objective of this study was the classification of representative cultivars of Vitis vinifera conserved in the Spanish collection of IMIDRA according to seed shape. Thirty eight cultivars representing the diversity of this collection were analyzed. A consensus seed silhouette was defined for each cultivar representing the geometric figure that better adjusted to their seed shape. All the cultivars tested were classified in ten morphological groups, each corresponding to a new model. The models are geometric figures defined by equations and similarity to each model is evaluated by quantification of percent of the area shared by the two figures, the seed and the model (J index). The comparison of seed images with geometric models is a rapid and convenient method to classify cultivars. A large proportion of the collection may be classified according to the new models described and the method permits to find new models according to seed shape in other cultivars. Full article

(This article belongs to the Special Issue Application of Computer Vision on Quality Monitoring of Agricultural Products)

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16 pages, 4413 KiB

Open AccessArticle

Seed Morphology in the Vitaceae Based on Geometric Models

by José Javier Martín-Gómez, Diego Gutiérrez del Pozo, Mariano Ucchesu, Gianluigi Bacchetta, Félix Cabello Sáenz de Santamaría, Ángel Tocino and Emilio Cervantes

Agronomy 2020, 10(5), 739; https://doi.org/10.3390/agronomy10050739 - 20 May 2020

Cited by 16 | Viewed by 5820

Abstract

Morphometric methods based on artificial vision algorithms provide measurements for magnitudes descriptive of seed images (i.e., the length, width, area, and surface circularity index). Nevertheless, their results frequently omit the resemblance of the images to geometric figures that may be used as models. [...] Read more.

Morphometric methods based on artificial vision algorithms provide measurements for magnitudes descriptive of seed images (i.e., the length, width, area, and surface circularity index). Nevertheless, their results frequently omit the resemblance of the images to geometric figures that may be used as models. A complementary method based on the comparison of seed images with geometric models is applied to seeds of Vitis spp. The J index gives the percentage of similarity between a seed image and the model. Seven new geometric models are described based on the heart-shaped and piriform curves. Seeds of different species, subspecies and cultivars of Vitis adjust to different models. Models 1 and 3, the heart curve and the water drop, adjust better to seeds of V. amurensis, V. labrusca and V. rupestris than to V. vinifera. Model 6, the Fibonacci’s pear, adjusts well to seeds of V. vinifera, in general, and better to V. vinifera ssp. vinifera than to V. vinifera ssp. sylvestris. Seed morphology in species of Cissus and Parthenocissus, two relatives of Vitis in the Vitaceae, is also analysed. Geometric models are a tool for the description and identification of species and lower taxonomic levels complementing the results of morphometric analysis. Full article

(This article belongs to the Special Issue Application of Computer Vision on Quality Monitoring of Agricultural Products)

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Review

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17 pages, 2505 KiB

Open AccessReview

An Ongoing Blended Long-Term Vegetation Health Product for Monitoring Global Food Security

by Wenze Yang, Felix Kogan and Wei Guo

Agronomy 2020, 10(12), 1936; https://doi.org/10.3390/agronomy10121936 - 9 Dec 2020

Cited by 8 | Viewed by 2670

Abstract

Remotely observing global vegetation from space has endured for nearly 50 years. Many datasets have been developed to monitor vegetation status. Tailored to specifically monitor global food security concerning drought and crop yield, a suite of datasets based on vegetation health concepts and [...] Read more.

Remotely observing global vegetation from space has endured for nearly 50 years. Many datasets have been developed to monitor vegetation status. Tailored to specifically monitor global food security concerning drought and crop yield, a suite of datasets based on vegetation health concepts and Advanced Very High Resolution Radiometer (AVHRR) observation was developed in the 1980s and utilized throughout the world. Nowadays, satellites based imaging radiometers have evolved into the Visible Infrared Imaging Radiometer Suite (VIIRS) era. With proper algorithm development, the blended version of the data suite, composed of the AVHRR dataset from 1981 to 2012 and VIIRS dataset from 2013 and afterwards, has bridged the long-term AVHRR observation and high-quality VIIRS data. This paper explains the blended version of the data suite. Full article

(This article belongs to the Special Issue Application of Computer Vision on Quality Monitoring of Agricultural Products)

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