Maturity estimation of mangoes using hyperspectral imaging from a ground based mobile platform

Wendel, Alexander; Underwood, James; Walsh, Kerry B.

doi:10.1016/j.compag.2018.10.021

Cited by 75 publications

(46 citation statements)

References 53 publications

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“…To date, phenotyping is labor and time-intensive and is also largely affected by human errors and biases. For these reasons, robotic devices and aerial vehicles are becoming a big opportunity to increase the accuracy of the phenotypic estimations, which in turn can be used in statistical models [ 48 , 49 , 50 , 51 , 52 ]. The development of statistical models capable of accurately predict marker effects has led to the breakthrough of GS increasing the rate of genetic gain per unit of time.…”

Section: Potential Of Gs In Plantmentioning

confidence: 99%

Accelerating Tomato Breeding by Exploiting Genomic Selection Approaches

Cappetta

Andolfo

Matteo

et al. 2020

Plants

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Genomic selection (GS) is a predictive approach that was built up to increase the rate of genetic gain per unit of time and reduce the generation interval by utilizing genome-wide markers in breeding programs. It has emerged as a valuable method for improving complex traits that are controlled by many genes with small effects. GS enables the prediction of the breeding value of candidate genotypes for selection. In this work, we address important issues related to GS and its implementation in the plant context with special emphasis on tomato breeding. Genomic constraints and critical parameters affecting the accuracy of prediction such as the number of markers, statistical model, phenotyping and complexity of trait, training population size and composition should be carefully evaluated. The comparison of GS approaches for facilitating the selection of tomato superior genotypes during breeding programs is also discussed. GS applied to tomato breeding has already been shown to be feasible. We illustrated how GS can improve the rate of gain in elite line selection, and descendent and backcross schemes. The GS schemes have begun to be delineated and computer science can provide support for future selection strategies. A new promising breeding framework is beginning to emerge for optimizing tomato improvement procedures.

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Section: Potential Of Gs In Plantmentioning

confidence: 99%

Accelerating Tomato Breeding by Exploiting Genomic Selection Approaches

Cappetta

Andolfo

Matteo

et al. 2020

Plants

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“…Ground-based spectral imaging system is suitable for experiments in the field. The tripods and vehicles are used as the bearing device for the camera [36,71]. To quickly obtain spectral data of the whole orchard, an unmanned aerial vehicle (UAV) was applied for imaging [72].…”

Section: The Application Of Multispectral and Hyperspectral Imagingmentioning

confidence: 99%

“…Replacing all-terrain vehicles with field robotics, Wendel et al implemented a driverless, automatic spectral scanner to predict the dry matter (DM) content of mangoes [71]. They developed an analytical method that unified the classification and regression analysis of hyperspectral images based on a convolutional neural network (CNN) and the PLS algorithm.…”

Section: Detection Of Biochemical Parameters Of Fruitsmentioning

confidence: 99%

Phenotypic techniques and applications in fruit trees: a review

Huang

Ren

et al. 2020

Plant Methods

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Phenotypic information is of great significance for irrigation management, disease prevention and yield improvement. Interest in the evaluation of phenotypes has grown with the goal of enhancing the quality of fruit trees. Traditional techniques for monitoring fruit tree phenotypes are destructive and time-consuming. The development of advanced technology is the key to rapid and non-destructive detection. This review describes several techniques applied to fruit tree phenotypic research in the field, including visible and near-infrared (VIS-NIR) spectroscopy, digital photography, multispectral and hyperspectral imaging, thermal imaging, and light detection and ranging (LiDAR). The applications of these technologies are summarized in terms of architecture parameters, pigment and nutrient contents, water stress, biochemical parameters of fruits and disease detection. These techniques have been shown to play important roles in fruit tree phenotypic research.

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“…To provide more internal quality information of the fruit, spectral data were utilized. With the ease of implementation, few studies took advantage of the spectrum within the range of 400-867 nm [12], [13]. However, the application of near-infrared spectroscopy (NIRS) to the horticultural field has attracted considerable attention.…”

Section: Introductionmentioning

confidence: 99%

Precise Sweetness Grading of Mangoes (Mangifera indica L.) Based on Random Forest Technique With Low-Cost Multispectral Sensors

et al. 2020

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Before being exported, mangoes generally undergo rigorous external and internal quality inspection processes in which near-infrared (NIR) spectral approaches are favorable for grading purposes. A successful NIR-based grading system depends largely on high-quality spectral sensors and the reliability of the classifier. Motivated by the high economic impact of Cat Hoa Loc mangoes (Mangifera indica L.), we demonstrated that the sweetness of that mangoes could be precisely graded based on a random forest (RF) classifier in a three-phase approach with a low-cost Visible-Near infrared (VIS-NIR) multispectral sensor chipset. This approach is so-called RPR because RF, Partial Least Squares regression, and RF were respectively applied to consecutively determine the significant VIS-NIR responses, the good features as input variables, and the reliable RF classifier via our formulated discriminant index (DI). The experimental results confirmed that higher classification accuracy was achieved by using the extracted latent features rather than the raw VIS-NIR data. The DI was effectively used as a reliability measure to select the optimal classifier among those of identical training and testing accuracies of 100% and 82.1%, respectively. Performance comparison between the optimal RF classifier with a Support Vector Machines classifier and a multinomial logistic regression showed that the developed RF classifier was superior in various performance indices. Therefore, it is promising to extend the proposed approach to more complicated fruit grading problems with sufficient VIS-NIR datasets that are acquired from low-cost multispectral sensors. INDEX TERMS Cost-effective, sweetness grading of Cat Hoa Loc mango, spectral response selection, VIS-NIR features extraction, discriminant index for random forest classifier.

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Maturity estimation of mangoes using hyperspectral imaging from a ground based mobile platform

Cited by 75 publications

References 53 publications

Accelerating Tomato Breeding by Exploiting Genomic Selection Approaches

Accelerating Tomato Breeding by Exploiting Genomic Selection Approaches

Phenotypic techniques and applications in fruit trees: a review

Precise Sweetness Grading of Mangoes (Mangifera indica L.) Based on Random Forest Technique With Low-Cost Multispectral Sensors

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