Postharvest ripeness assessment of ‘Hass’ avocado based on development of a new ripening index and Vis-NIR spectroscopy

Herreros, Ángela Melado; Nieto-Ortega, Sonia; Olabarrieta, Idoia; Gutiérrez, Mónica; Villar, Alberto; Zufía, Jaime; Gorretta, Nathalie; Roger, Jean‐Michel

doi:10.1016/j.postharvbio.2021.111683

Cited by 20 publications

(13 citation statements)

References 33 publications

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“…Hyperspectral imaging could not predict foliar S concentrations using PLSR modelling. PLSR is commonly applied to datasets with a small number of samples [18,27,[58][59][60], and foliar S concentrations have been predicted using PLSR models previously [18,27]. Therefore, the small number of samples in the dataset may not be directly associated with the lack of prediction capacity for foliar S concentrations.…”

Section: Discussionmentioning

confidence: 99%

“…Therefore, the small number of samples in the dataset may not be directly associated with the lack of prediction capacity for foliar S concentrations. PLSR is appropriate when a linear relationship exists between spectral data and the variable of interest [59,60]. Other advanced machine learning methods such as artificial neural network (ANN) could be applied to increase the prediction capacity of the models [30,61].…”

Section: Discussionmentioning

confidence: 99%

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Hyperspectral Imaging of Adaxial and Abaxial Leaf Surfaces for Rapid Assessment of Foliar Nutrient Concentrations in Hass Avocado

et al. 2023

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Rapid assessment tools are required for monitoring crop nutrient status and managing fertiliser applications in real time. Hyperspectral imaging has emerged as a promising assessment tool to manage crop nutrition. This study aimed to determine the potential of hyperspectral imaging for predicting foliar nutrient concentrations in avocado trees and establish whether imaging different sides of the leaves affects prediction accuracy. Hyperspectral images (400–1000 nm) were taken of both surfaces of leaves collected from Hass avocado trees 0, 6, 10 and 28 weeks after peak anthesis. Partial least squares regression (PLSR) models were developed to predict mineral nutrient concentrations using images from (a) abaxial surfaces, (b) adaxial surfaces and (c) combined images of both leaf surfaces. Modelling successfully predicted foliar nitrogen (RP2 = 0.60, RPD = 1.61), phosphorus (RP2 = 0.71, RPD = 1.90), aluminium (RP2 = 0.88, RPD = 2.91), boron (RP2 = 0.63, RPD = 1.67), calcium (RP2 = 0.88, RPD = 2.86), copper (RP2 = 0.86, RPD = 2.76), iron (RP2 = 0.81, RPD = 2.34), magnesium (RP2 = 0.87, RPD = 2.81), manganese (RP2 = 0.87, RPD = 2.76) and zinc (RP2 = 0.79, RPD = 2.21) concentrations from either the abaxial or adaxial surface. Foliar potassium concentrations were predicted successfully only from the adaxial surface (RP2 = 0.56, RPD = 1.54). Foliar sodium concentrations were predicted successfully (RP2 = 0.59, RPD = 1.58) only from the combined images of both surfaces. In conclusion, hyperspectral imaging showed great potential as a rapid assessment tool for monitoring the crop nutrition status of avocado trees, with adaxial surfaces being the most useful for predicting foliar nutrient concentrations.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Hyperspectral Imaging of Adaxial and Abaxial Leaf Surfaces for Rapid Assessment of Foliar Nutrient Concentrations in Hass Avocado

et al. 2023

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“…Other studies have demonstrated NIRS applications for the detection of bruises and the prediction of rot susceptibility in 'Hass' avocado fruit [34]. The progression of ripening has been monitored using NIRS to assess issues related to fruit transpiration [35], as well as postharvest classification strategies during avocado ripening, categorizing it by maturity stages based on firmness [36]. While these works are concerned with postharvest avocado quality, our focus here lies on research endeavors centered around the determination of dry matter content to ascertain the degree of maturity before or at the time of Hass avocado harvest.…”

Section: Application Of Different Nirs Scans By Fruit In Maturity Mon...mentioning

confidence: 99%

Quantification of Dry Matter Content in Hass Avocado by Near-Infrared Spectroscopy (NIRS) Scanning Different Fruit Zones

Rodríguez,

Villamizar,

Londoño

et al. 2023

Plants

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Accurate dry matter determination (DM) in Hass avocados is vital for optimal harvesting and ensuring fruit quality. Predictive models based on NIRS need to capture fruit DM gradient. This work aimed to determine the DM content in Hass avocado whole by NIRS scanning different fruit zones. Spectra were recorded for each zone of the fruit: peduncle (P), equator (E), and base (B). The calibration and validation included fruit from different orchards in two harvest cycles. The results show a DM gradient within the fruit: 24.47% (E), 24.68% (B), and 24.79% (P). The DM gradient was observed within the spectra using the RMSi (root mean square) criterion and PCA. The results show that at least one spectrum per fruit zone was needed to represent the variability within the fruit. The performances of the calibration using the whole set of data were R2: 0.74 and standard error of cross-validation (SECV) = 1.18%. In the validation stage using independent validation sets, the models showed similar performance (R2: 0.75, SECV 1.15%) with low values of the standard error of prediction (SEP): 1.62%. These results demonstrate the potential of near-infrared spectroscopy for high-throughput sorting of avocados based on their commercial quality.

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“…These methodologies offer the advantage of gathering comprehensive information without compromising the integrity of the fruit [4]. Leading this innovation in maturity assessment are tools such as hyper-spectral imaging [5], visible imaging [6], and Vis-NIR spectroscopy [7]. Fig.…”

Section: Introductionmentioning

confidence: 99%

HapticFormers: Utilizing Transformers for Avocado Maturity Grading through Vision-based Tactile Assessment

Fahmy,

Hussain,

Werghi

et al. 2024

Preprint

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Detecting maturity of fruits and vegetables, especially avocados, is a critical task in modern agriculture and supply chain management. Moreover, the accurate assessment of maturity can improve the harvesting time and ensure consistent quality for consumers through the supply chain process. A key approach to achieving this is the non-destructive estimation of produce quality. Vision-Based Tactile Sensing (VBTS) technologies, which mimic human tactile perception, offer a novel approach to address this challenge. This paper focuses on the use of two notable VBTS technologies, GelSight and Facebook's DIGIT sensor. Using these technologies, we developed two novel datasets that assess the avocado maturity using the transformer models, marking a novel contribution in this area. We adapted several transformer architectures to the task, conducting experiments on both image classification and regression to estimate avocado firmness. Among the variants tested, the PoolFormer displayed notable results with accuracy of 92% in detecting avocado maturity level when used with tactile data. The datasets and code used in this study will be shared at this URL.Index terms -Vision-based tactile sensors (VBTS), Vision Transformer (ViT), self-attention block, maturity classification.

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Postharvest ripeness assessment of ‘Hass’ avocado based on development of a new ripening index and Vis-NIR spectroscopy

Cited by 20 publications

References 33 publications

Hyperspectral Imaging of Adaxial and Abaxial Leaf Surfaces for Rapid Assessment of Foliar Nutrient Concentrations in Hass Avocado

Hyperspectral Imaging of Adaxial and Abaxial Leaf Surfaces for Rapid Assessment of Foliar Nutrient Concentrations in Hass Avocado

Quantification of Dry Matter Content in Hass Avocado by Near-Infrared Spectroscopy (NIRS) Scanning Different Fruit Zones

HapticFormers: Utilizing Transformers for Avocado Maturity Grading through Vision-based Tactile Assessment

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