Can you make morphometrics work when you know the right answer? Pick and mix approaches for apple identification
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Cited by 4 publications
References 54 publications
When do apples stop growing, and why does it matter?
14Background. Apples in the commercial food chain are harvested up to two weeks 15 before maturity. We explore apple fruit development through the growing season to 16 establish the point at which the features differentiating those cultivars become evident. 17 This is relevant both for the understanding of the growing process and to ensure that 18 any identification and classification tools can be used both on ripened-on-tree and 19 stored fruit. Current literature presents some contradictory findings on apple 20 development, we explored the size development of 12 apple cultivars in the Brogdale 21 National Fruit Collection, UK over two growing seasons. 23Methods. Fruit were sampled at regular time points throughout the growing season and 24 four morphometrics (maximum length, maximum diameter, weight, and centroid size) 25 were collected. These were regressed against growing degree days in order to 26 appropriately describe the growth pattern observed. 28Results. All four morphometrics were adequately described using log-log linear 29 regressions, with adjusted R 2 estimates ranging from 78.3% (maximum length) to 86.7% 30 (weight). For all four morphometrics, a 10% increase in growing degree days was 31 associated with a 1% increase in the morphometric measurement. 33Discussion. Our findings refine previous work presenting rapid early growth followed by 34 a plateau in later stages of development and are in disagreement with published expo-35 linear models. We established that apples harvested for commercial storage purposes, 36 two weeks prior to maturity, showed only a modest decrease in size, demonstrating that 37 size morphometric approaches are appropriate for classification of apples, both ripened-38 on-tree and stored. 39 40 41 42 Describing size development for apples (Malus domestica (Suckow) Borkh.) is both 43 interesting in its own merits, relevant for modern classification methods, and important 44 for commercial fruit trade. Christodoulou et al. (2018) demonstrated that mature apples 45 can be correctly classified to cultivar with a 78% accuracy using only external 46 morphometric characters such as length, weight, or color. In countries such as the UK, 47 This view has been challenged in the literature at least three times. Firstly, when 80 presenting fruit volume and diameter measurements in terms of days from anthesis, 81 Tukey and Young (1942) described a linear growth pattern. The accompanying plots 82 however do not support this view, presenting a sigmoid growth pattern. They interpreted 83 this by suggesting the final plateau observed in their plots was due to weather 84 conditions for the year studied. 85 86 The second time this view was challenged was in the work of Lakso et al. (1995) who 87 suggested that a combination of linear and exponential curves fits the weight growth 88 pattern best. To fit their data, they proposed a model originally described by Goudriaan 89 and Monteith (1990). In the original model, factors such as leaf area were potential 90 limitations to the growth ...
When do apples stop growing, and why does it matter?
14Background. Apples in the commercial food chain are harvested up to two weeks 15 before maturity. We explore apple fruit development through the growing season to 16 establish the point at which the features differentiating those cultivars become evident. 17 This is relevant both for the understanding of the growing process and to ensure that 18 any identification and classification tools can be used both on ripened-on-tree and 19 stored fruit. Current literature presents some contradictory findings on apple 20 development, we explored the size development of 12 apple cultivars in the Brogdale 21 National Fruit Collection, UK over two growing seasons. 23Methods. Fruit were sampled at regular time points throughout the growing season and 24 four morphometrics (maximum length, maximum diameter, weight, and centroid size) 25 were collected. These were regressed against growing degree days in order to 26 appropriately describe the growth pattern observed. 28Results. All four morphometrics were adequately described using log-log linear 29 regressions, with adjusted R 2 estimates ranging from 78.3% (maximum length) to 86.7% 30 (weight). For all four morphometrics, a 10% increase in growing degree days was 31 associated with a 1% increase in the morphometric measurement. 33Discussion. Our findings refine previous work presenting rapid early growth followed by 34 a plateau in later stages of development and are in disagreement with published expo-35 linear models. We established that apples harvested for commercial storage purposes, 36 two weeks prior to maturity, showed only a modest decrease in size, demonstrating that 37 size morphometric approaches are appropriate for classification of apples, both ripened-38 on-tree and stored. 39 40 41 42 Describing size development for apples (Malus domestica (Suckow) Borkh.) is both 43 interesting in its own merits, relevant for modern classification methods, and important 44 for commercial fruit trade. Christodoulou et al. (2018) demonstrated that mature apples 45 can be correctly classified to cultivar with a 78% accuracy using only external 46 morphometric characters such as length, weight, or color. In countries such as the UK, 47 This view has been challenged in the literature at least three times. Firstly, when 80 presenting fruit volume and diameter measurements in terms of days from anthesis, 81 Tukey and Young (1942) described a linear growth pattern. The accompanying plots 82 however do not support this view, presenting a sigmoid growth pattern. They interpreted 83 this by suggesting the final plateau observed in their plots was due to weather 84 conditions for the year studied. 85 86 The second time this view was challenged was in the work of Lakso et al. (1995) who 87 suggested that a combination of linear and exponential curves fits the weight growth 88 pattern best. To fit their data, they proposed a model originally described by Goudriaan 89 and Monteith (1990). In the original model, factors such as leaf area were potential 90 limitations to the growth ...
Apples before the fall: does shape stability coincide with maturity?
Fruit shape is the result of the interaction between genetic, epigenetic, environmental factors, and stochastic processes. As a core biological descriptor both for taxonomy and horticulture, the point at which shape stability is reached becomes paramount in apple cultivar identification, and authentication in commerce. Twelve apple cultivars were sampled at regular intervals from anthesis to harvest over two growing seasons. Linear and geometric morphometrics were analyzed to establish if and when shape stabilized and whether fruit asymmetry influenced this. Shape stability was detected in seven cultivars, four asymmetric and three symmetric. The remaining five did not stabilize. Shape stability, as defined here, is cultivar-dependent, and when it occurs, it is late in the growing season. Geometric morphometrics detected stability more readily than linear, especially in symmetric cultivars. Key shape features are important in apple marketing, giving the distinctness and apparent uniformity between cultivars expected at point of sale.
Machine Learning Applications and Optimization of Clustering Methods Improve the Selection of Descriptors in Blackberry Germplasm Banks
Machine learning (ML) and its multiple applications have comparative advantages for improving the interpretation of knowledge on different agricultural processes. However, there are challenges that impede proper usage, as can be seen in phenotypic characterizations of germplasm banks. The objective of this research was to test and optimize different analysis methods based on ML for the prioritization and selection of morphological descriptors of Rubus spp. 55 descriptors were evaluated in 26 genotypes and the weight of each one and its ability to discriminating capacity was determined. ML methods as random forest (RF), support vector machines, in the linear and radial forms, and neural networks were optimized and compared. Subsequently, the results were validated with two discriminating methods and their variants: hierarchical agglomerative clustering and K-means. The results indicated that RF presented the highest accuracy (0.768) of the methods evaluated, selecting 11 descriptors based on the purity (Gini index), importance, number of connected trees, and significance (p value < 0.05). Additionally, K-means method with optimized descriptors based on RF had greater discriminating power on Rubus spp., accessions according to evaluated statistics. This study presents one application of ML for the optimization of specific morphological variables for plant germplasm bank characterization.
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