scholarly journals Stub Length and Stub Angle Did Not Influence Renewal Shoot Number or Branch Angle of Tall Spindle ‘Gala’/Malling 9 Apple Trees

2019 ◽  
Vol 29 (1) ◽  
pp. 46-49 ◽  
Author(s):  
James R. Schupp ◽  
H. Edwin Winzeler ◽  
Melanie A. Schupp
Keyword(s):  
Machine Learning ◽  
Position Angle ◽  
Apple Trees ◽  
Training Systems ◽  
Branch Angle ◽  
Shoot Number ◽  
Shoot Quality

Renewal of limbs by pruning to leave a short, angled, upward-facing stub is common practice for spindle-type apple (Malus ×domestica) training systems. A short, beveled stub cut is thought to stimulate renewal growth from latent buds present underneath the base of the excised branch, and to stimulate smaller, more fruitful renewal limbs with wide crotch angles. We conducted trials over the course of 2 years that involved dormant pruning of ‘Buckeye Gala’ with renewal cuts to compare two stub lengths, 0.5 and 2 cm, and three stub orientations, upward facing, downward facing, and vertical facing, to determine the effects on renewal shoot number, position, angle, and length. We found no clear advantages with either stub length that we evaluated, and there was no improvement in renewal shoot quality with a bevel cut at any orientation. Stub length and stub angle did not influence limb renewal and may be unimportant for training orchard-pruning crews and for machine-learning and robotic pruning.

scholarly journals Harvesting Semidwarf Freestanding Apple Trees with an Over-the-row Mechanical Harvester

1994 ◽  
Vol 119 (6) ◽  
pp. 1114-1120 ◽  
Author(s):  
D.L. Peterson ◽  
S.S. Miller ◽  
J.D. Whitney
Keyword(s):  
Malus Domestica ◽  
Market Quality ◽  
Fresh Market ◽  
Cumulative Yield ◽  
Apple Trees ◽  
Training Systems ◽  
Golden Delicious ◽  
Harvesting Efficiency ◽  
Mechanical Harvester

Three years of mechanical harvesting (shake and catch) trials with two freestanding apple (Malus domestica Borkh.) cultivars on a semidwarf rootstock (M.7a) and two training systems (central leader and open center) yielded 64% to 77% overall harvesting efficiency. Mechanically harvested `Bisbee Delicious' apples averaged 70% Extra Fancy and 10% Fancy grade, while two `Golden Delicious' strains (`Smoothee' and `Frazier Goldspur') averaged 40% Extra Fancy and 13% Fancy grade fruit. Mechanically harvesting fresh-market-quality apples from semidwarf freestanding trees was difficult and its potential limited. Cumulative yield of open-center trees was less than that of central-leader trees during the 3 years (sixth through eighth leaf) of our study. `Golden Delicious' trees generally produced higher yields than `Delicious' trees.

scholarly journals 267 Improving Red Color on Apples with a Reflective Groundcover

HortScience ◽  
2000 ◽  
Vol 35 (3) ◽  
pp. 437C-437
Author(s):  
Stephen S. Miller ◽  
George M. Greene
Keyword(s):  
Ambient Air ◽  
Apple Trees ◽  
Standard Size ◽  
Full Sunlight ◽  
Training Systems ◽  
Strong Trend ◽  
Air Temperatures ◽  
Red Color ◽  
Color Response ◽  
Apple Cultivars

In many years, apples grown in the mid-Atlantic region fail to exhibit a high percentage of the dark red color that buyers and consumers desire. In 1996, we initiated studies to examine the use of a metalized silver low-density polyethylene reflective groundcover (RGC) to improve red color on several apple cultivars under several training systems. A RGC placed in the orchard drive middle of 8-year-old `Delicious' apple trees trained to a “Y” trellis increased the percent surface red color and resulted in darker, more red-colored apples at harvest. A RGC increased surface red color on `Empire' apples on a “Y” trellis, but on central leader-trained semi-dwarf and standard size `Empire' showed no effects on color. Central leader-trained `Fuji'/EMLA.7 apples with a RGC had more red color than untreated fruit at harvest. In 1997, RGC placed under the canopy of 3-year-old `Fuji' trees trained to a “Y” trellis increased the full sunlight on the underside of the canopy by 28%. Ambient air temperatures within the RGC illuminated canopy averaged 2.1 °C higher than the non-RGC canopy. The level of percent full sunlight was increased within the canopy of well-pruned 32-year-old `Miller Spur Delicious' apple trees 4- to 8-fold with RGC placed in the row middle or under the canopy in a commercial orchard in 1998. Position of the RGC to the canopy affected fruit red color response differently between the lower and upper part of the canopy. Bins of fruit graded with a commercial color sorter showed no difference in fruit color; however, there was a strong trend toward increased red color where the RGC material was applied.

scholarly journals Use of Images of Leaves and Fruits of Apple Trees for Automatic Identification of Symptoms of Diseases and Nutritional Disorders

2017 ◽  
Vol 5 (2) ◽  
pp. 1-14
Author(s):  
Lucas Garcia Nachtigall ◽  
Ricardo Matsumura Araujo ◽  
Gilmar Ribeiro Nachtigall
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Rapid Diagnosis ◽  
Nutritional Deficiencies ◽  
Automatic Identification ◽  
Apple Trees ◽  
Data Set ◽  
Apple Fruits

Rapid diagnosis of symptoms caused by pest attack, diseases and nutritional or physiological disorders in apple orchards is essential to avoid greater losses. This paper aimed to evaluate the efficiency of Convolutional Neural Networks (CNN) to automatically detect and classify symptoms of diseases, nutritional deficiencies and damage caused by herbicides in apple trees from images of their leaves and fruits. A novel data set was developed containing labeled examples consisting of approximately 10,000 images of leaves and apple fruits divided into 12 classes, which were classified by algorithms of machine learning, with emphasis on models of deep learning. The results showed trained CNNs can overcome the performance of experts and other algorithms of machine learning in the classification of symptoms in apple trees from leaves images, with an accuracy of 97.3% and obtain 91.1% accuracy with fruit images. In this way, the use of Convolutional Neural Networks may enable the diagnosis of symptoms in apple trees in a fast, precise and usual way.

scholarly journals Long-term Performance of ‘Delicious’ Apple Trees Grafted on Geneva® Rootstocks and Trained to Four High-density Systems under New York State Climatic Conditions

HortScience ◽  
2020 ◽  
Vol 55 (10) ◽  
pp. 1538-1550
Author(s):  
Gemma Reig ◽  
Jaume Lordan ◽  
Stephen Hoying ◽  
Michael Fargione ◽  
Daniel J. Donahue ◽  
...  
Keyword(s):  
Fruit Quality ◽  
New York State ◽  
Climatic Conditions ◽  
Training System ◽  
High Density ◽  
Cumulative Yield ◽  
Apple Trees ◽  
Cross Sectional ◽  
Training Systems ◽  
G 11

We conducted a large (0.8 ha) field experiment of system × rootstock, using Super Chief Delicious apple as cultivar at Yonder farm in Hudson, NY, between 2007 and 2017. In this study, we compared six Geneva® rootstocks (‘G.11’, ‘G.16’, ‘G.210’, ‘G.30’, ‘G.41’, and ‘G.935’) with one Budagovsky (‘B.118’) and three Malling rootstocks (‘M.7EMLA’, ‘M.9T337’ and ‘M.26EMLA’). Trees on each rootstock were trained to four high-density systems: Super Spindle (SS) (5382 apple trees/ha), Tall Spindle (TS) (3262 apple trees/ha), Triple Axis Spindle (TAS) (2243 apple trees/ha), and Vertical Axis (VA) (1656 apple trees/ha). Rootstock and training system interacted to influence growth, production, and fruit quality. When comparing systems, SS trees were the least vigorous but much more productive on a per hectare basis. Among the rootstocks we evaluated, ‘B.118’ had the largest trunk cross-sectional area (TCSA), followed by ‘G.30’ and ‘M.7EMLA’, which were similar in size but they did not differ statistically from ‘G.935’. ‘M.9T337’ was the smallest and was significantly smaller than most of the other rootstocks but it did not differ statistically from ‘G.11’, ‘G.16’, ‘G.210’, ‘G.41’, and ‘M.26EMLA’. Although ‘B.118’ trees were the largest, they had low productivity, whereas the second largest rootstock ‘G.30’ was the most productive on a per hectare basis. ‘M.9’ was the smallest rootstock and failed to adequately fill the space in all systems except the SS, and had low cumulative yield. The highest values for cumulative yield efficiency (CYE) were with ‘G.210’ for all training systems except for VA, where ‘M.9T337’ had the highest value. The lowest values were for all training systems with ‘B.118’ and ‘M.7EMLA’. Regardless of the training system, ‘M.7EMLA’ trees had the highest number of root suckers. Some fruit quality traits were affected by training system, rootstock or system × rootstock combination.

scholarly journals Multi-label classification approach for quranic verses labeling

2021 ◽  
Vol 24 (1) ◽  
pp. 484
Author(s):  
Abdullahi Adeleke ◽  
Noor Azah Samsudin ◽  
Mohd Hisyam Abdul Rahim ◽  
Shamsul Kamal Ahmad Khalid ◽  
Riswan Efendi
Keyword(s):  
Machine Learning ◽  
Performance Metrics ◽  
Research Work ◽  
Support Vector ◽  
Classification Problems ◽  
K Nearest Neighbors ◽  
Training Systems ◽  
Learning Tasks ◽  
Binary Relevance ◽  
Problem Transformation

Machine learning involves the task of training systems to be able to make decisions without being explicitly programmed. Important among machine learning tasks is classification involving the process of training machines to make predictions from predefined labels. Classification is broadly categorized into three distinct groups: single-label (SL), multi-class, and multi-label (ML) classification. This research work presents an application of a multi-label classification (MLC) technique in automating Quranic verses labeling. MLC has been gaining attention in recent years. This is due to the increasing amount of works based on real-world classification problems of multi-label data. In traditional classification problems, patterns are associated with a single-label from a set of disjoint labels. However, in MLC, an instance of data is associated with a set of labels. In this paper, three standard <em>MLC</em> methods: <span>binary relevance (BR), classifier chain (CC), and label powerset (LP) algorithms are implemented with four baseline classifiers: support vector machine (SVM), naïve Bayes (NB), k-nearest neighbors (k-NN), and J48. The research methodology adopts the multi-label problem transformation (PT) approach. The results are validated using six conventional performance metrics. These include: hamming loss, accuracy, one error, micro-F1, macro-F1, and avg. precision. From the results, the classifiers effectively achieved above 70% accuracy mark. Overall, SVM achieved the best results with CC and LP algorithms.</span>

scholarly journals Mature `Starkrimson Delicious' Apple Tree Response to Paclobutrazol Application Method

HortScience ◽  
1990 ◽  
Vol 25 (4) ◽  
pp. 429-430 ◽  
Author(s):  
Laura J. Lehman ◽  
C.R. Unrath ◽  
Eric Young
Keyword(s):  
Malus Domestica ◽  
Soil Cover ◽  
Apple Tree ◽  
Root Zone ◽  
Fruit Weight ◽  
Shoot Length ◽  
Apple Trees ◽  
Foliar Sprays ◽  
Pedicel Length ◽  
Shoot Number

Mature spur-type `Delicious'/seedling apple trees (Malus domestica Borkh.) were examined for 2 years after paclobutrazol (PB) foliar sprays with or without a soil cover to direct spray runoff away from the root zone, soil sprays, or a trunk drench. Foliar sprays with runoff reduced shoot number and fruit pedicel length in the year of treatment, but had no effect on shoot length. Trees that received foliar sprays with no runoff had fewer and shorter shoots and shorter pedicels the year after treatment. Soil sprays or a trunk drench reduced shoot number and pedicel length for 2 years after application, while only soil sprays reduced fruit weight, diameter, and length. Chemical name used: β- [(4-chlorophenyl)methyl]- α -(1,1-dimethylethyl)-1 H -1,2,4,-triazol-1-ethanol (paclobutrazol).

scholarly journals Use of Images of Leaves and Fruits of Apple Trees for Automatic Identification of Symptoms of Diseases and Nutritional Disorders

2020 ◽  
pp. 1072-1086
Author(s):  
Lucas Garcia Nachtigall ◽  
Ricardo Matsumura Araujo ◽  
Gilmar Ribeiro Nachtigall
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Rapid Diagnosis ◽  
Nutritional Deficiencies ◽  
Automatic Identification ◽  
Apple Trees ◽  
Data Set ◽  
Apple Fruits

Rapid diagnosis of symptoms caused by pest attack, diseases and nutritional or physiological disorders in apple orchards is essential to avoid greater losses. This paper aimed to evaluate the efficiency of Convolutional Neural Networks (CNN) to automatically detect and classify symptoms of diseases, nutritional deficiencies and damage caused by herbicides in apple trees from images of their leaves and fruits. A novel data set was developed containing labeled examples consisting of approximately 10,000 images of leaves and apple fruits divided into 12 classes, which were classified by algorithms of machine learning, with emphasis on models of deep learning. The results showed trained CNNs can overcome the performance of experts and other algorithms of machine learning in the classification of symptoms in apple trees from leaves images, with an accuracy of 97.3% and obtain 91.1% accuracy with fruit images. In this way, the use of Convolutional Neural Networks may enable the diagnosis of symptoms in apple trees in a fast, precise and usual way.

scholarly journals Soccer goalkeeper expertise identification based on eye movements

PLoS ONE ◽  
2021 ◽  
Vol 16 (5) ◽  
pp. e0251070
Author(s):  
Benedikt W. Hosp ◽  
Florian Schultz ◽  
Oliver Höner ◽  
Enkelejda Kasneci
Keyword(s):  
Machine Learning ◽  
Eye Movements ◽  
Machine Learning Techniques ◽  
Gaze Behavior ◽  
Perceptual Skills ◽  
The Gaze ◽  
Training Systems ◽  
Learning Techniques ◽  
Three Stages

By focusing on high experimental control and realistic presentation, the latest research in expertise assessment of soccer players demonstrates the importance of perceptual skills, especially in decision making. Our work captured omnidirectional in-field scenes displayed through virtual reality glasses to 12 expert players (picked by DFB), 10 regional league intermediate players, and13 novice soccer goalkeepers in order to assess the perceptual skills of athletes in an optimized manner. All scenes were shown from the perspective of the same natural goalkeeper and ended after the return pass to that goalkeeper. Based on the gaze behavior of each player, we classified their expertise with common machine learning techniques. Our results show that eye movements contain highly informative features and thus enable a classification of goalkeepers between three stages of expertise, namely elite youth player, regional league player, and novice, at a high accuracy of 78.2%. This research underscores the importance of eye tracking and machine learning in perceptual expertise research and paves the way for perceptual-cognitive diagnosis as well as future training systems.

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