Estimation of Citrus Maturity with Fluorescence Spectroscopy Using Deep Learning

Itakura, Kenta; Saito, Yoshito; Suzuki, Tetsuhito; Kondo, Naoshi; Hosoi, Fumiki

doi:10.3390/horticulturae5010002

Cited by 51 publications

(23 citation statements)

References 30 publications

(36 reference statements)

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“…The ripening of citrus fruit passes through different phenological stages, and the final stage is characterized by a reduced growth rate with an increase in soluble solids (SS) content, a decrease in titratable acidity (TA), and changes in skin color [8]. The SS content and TA, along with the relationship between them, also known as the maturation index (MI) or ratio (SS/TA), are considered the main indicators of harvest quality [9][10][11]. MI represents the balance between the content of sugars and organic acids in fruit, is associated with juice taste, and is widely used by the processing industry as an indicator of maturation and internal quality of fruit [7,12].…”

Section: Introductionmentioning

confidence: 99%

Fruit Ripening Development of ‘Valencia’ Orange Trees Grafted on Different ‘Trifoliata’ Hybrid Rootstocks

et al. 2020

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The development of fruit ripening of ‘Valencia’ orange trees [Citrus sinensis (L.) Osb.] grafted on the following ‘Trifoliata’ hybrid rootstocks: ‘US-852’, IPEACS-256 and IPEACS-264 citrandarins, and F.80-3, ‘W-2’ citrumelo, and ‘Swingle’ citrumelo (control), was assessed in three different subtropical locations: Rancho Alegre (RA); São Sebastião da Amoreira (SSA); and São Jerônimo da Serra (SJS), Parana state, Brazil. The climate of the RA and SSA locations was classified as Cfa with hot summers, whereas that of the SJS location was Cfb with temperate summers, which are located at 380, 650, and 835 m a.s.l., respectively. A completely randomized block design with four replications and four trees per plot was used as a statistical model for each location. The soluble solids (SS) content, titratable acidity (TA), and the maturation index (MI) or ratio (SS/TA) of the juice, as well as the citrus color index (CCI) of fruit skin, were assessed monthly, beginning 200 days after flowering until harvest, totaling seven sampling dates. The data sets of each location were analyzed independently through a two-way analysis of variance (ANOVA) involving rootstocks in a split plot array in time (days) to allow for the assessment of the significance of the main effects, complemented by regression analysis. In general, the ripening of ‘Valencia’ orange fruits was influenced by the different ‘Trifoliata’ hybrid rootstocks. At the RA location, trees on IPEACS-256 and’ US-852’ citrandarins had the highest SS, and on ‘US-852’ citrandarin had the highest MI, reaching the MIim earlier than the other rootstocks. The highest CCI was achieved when trees were on IPEACS-256 citrandarin. At the SSA location, trees on ‘US-852’, IPEACS-256, and IPEACS-264 citrandarins had the highest SS, but on ‘US–852’ had the highest MI, reaching the earliest MIim among the rootstocks. The highest CCI was achieved when trees were on ‘US-852’ and IPEACS-256 citrandarins. In the SSJ location, there was no significant effect of the rootstocks on and of the variables of ‘Valencia’ orange fruit. This assessment can be useful in the planning of new orchards producing high-quality fruit with desirable features for the orange juice industry.

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Section: Introductionmentioning

confidence: 99%

Fruit Ripening Development of ‘Valencia’ Orange Trees Grafted on Different ‘Trifoliata’ Hybrid Rootstocks

et al. 2020

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“…Image processing and machine vision for the maturity level classification of fruits have been intensively investigated [ 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 ]. Most work to date has focused on maturity analysis of fruit that ripen in a uniform fashion, such as tomato [ 32 , 33 , 34 ], passion fruit [ 27 ], apricot [ 24 ], persimmon [ 35 ], blueberry [ 36 , 37 ], cherry [ 38 ], and date [ 39 ].…”

Section: Introductionmentioning

confidence: 99%

Viewpoint Analysis for Maturity Classification of Sweet Peppers

Harel

Essen

Parmet

et al. 2020

Sensors

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The effect of camera viewpoint and fruit orientation on the performance of a sweet pepper maturity level classification algorithm was evaluated. Image datasets of sweet peppers harvested from a commercial greenhouse were collected using two different methods, resulting in 789 RGB—Red Green Blue (images acquired in a photocell) and 417 RGB-D—Red Green Blue-Depth (images acquired by a robotic arm in the laboratory), which are published as part of this paper. Maturity level classification was performed using a random forest algorithm. Classifications of maturity level from different camera viewpoints, using a combination of viewpoints, and different fruit orientations on the plant were evaluated and compared to manual classification. Results revealed that: (1) the bottom viewpoint is the best single viewpoint for maturity level classification accuracy; (2) information from two viewpoints increases the classification by 25 and 15 percent compared to a single viewpoint for red and yellow peppers, respectively, and (3) classification performance is highly dependent on the fruit’s orientation on the plant.

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“…Fluorescence spectroscopy is considered a cost-effective, fast and sensitive method [ 47 ]. The principles of fluorescence spectroscopy are based on the leaf properties reacting to light.…”

Section: Proximal Sensorsmentioning

confidence: 99%

Active and Passive Electro-Optical Sensors for Health Assessment in Food Crops

Fahey

Pham

Gardi

et al. 2020

Sensors

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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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Estimation of Citrus Maturity with Fluorescence Spectroscopy Using Deep Learning

Cited by 51 publications

References 30 publications

Fruit Ripening Development of ‘Valencia’ Orange Trees Grafted on Different ‘Trifoliata’ Hybrid Rootstocks

Fruit Ripening Development of ‘Valencia’ Orange Trees Grafted on Different ‘Trifoliata’ Hybrid Rootstocks

Viewpoint Analysis for Maturity Classification of Sweet Peppers

Active and Passive Electro-Optical Sensors for Health Assessment in Food Crops

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