scholarly journals Technologies for Forecasting Tree Fruit Load and Harvest Timing—From Ground, Sky and Time

Agronomy ◽  
2021 ◽  
Vol 11 (7) ◽  
pp. 1409
Author(s):  
Nicholas Todd Anderson ◽  
Kerry Brian Walsh ◽  
Dvoralai Wulfsohn
Keyword(s):  
Machine Vision ◽  
Structural Parameters ◽  
Qualitative Assessment ◽  
Sampling Strategies ◽  
Yield Data ◽  
Proximal Sensing ◽  
Fruit Number ◽  
Fruit Maturity ◽  
Harvest Timing ◽  
Fruit Load

The management and marketing of fruit requires data on expected numbers, size, quality and timing. Current practice estimates orchard fruit load based on the qualitative assessment of fruit number per tree and historical orchard yield, or manually counting a subsample of trees. This review considers technological aids assisting these estimates, in terms of: (i) improving sampling strategies by the number of units to be counted and their selection; (ii) machine vision for the direct measurement of fruit number and size on the canopy; (iii) aerial or satellite imagery for the acquisition of information on tree structural parameters and spectral indices, with the indirect assessment of fruit load; (iv) models extrapolating historical yield data with knowledge of tree management and climate parameters, and (v) technologies relevant to the estimation of harvest timing such as heat units and the proximal sensing of fruit maturity attributes. Machine vision is currently dominating research outputs on fruit load estimation, while the improvement of sampling strategies has potential for a widespread impact. Techniques based on tree parameters and modeling offer scalability, but tree crops are complicated (perennialism). The use of machine vision for flowering estimates, fruit sizing, external quality evaluation is also considered. The potential synergies between technologies are highlighted.

Linking mango infestation by fruit flies to fruit maturity and fly pressure: A prerequisite to improve fruit fly damage management via harvest timing optimization

2021 ◽  
Vol 146 ◽  
pp. 105663
Author(s):  
Isabelle Grechi ◽  
Anne-Laure Preterre ◽  
Aude Caillat ◽  
Frédéric Chiroleu ◽  
Alain Ratnadass
Keyword(s):  
Fruit Fly ◽  
Fruit Flies ◽  
Timing Optimization ◽  
Fruit Maturity ◽  
Harvest Timing

Estimation of fruit load in mango orchards: tree sampling considerations and use of machine vision and satellite imagery

2018 ◽  
Vol 20 (4) ◽  
pp. 823-839 ◽  
Author(s):  
N. T. Anderson ◽  
J. P. Underwood ◽  
M. M. Rahman ◽  
A. Robson ◽  
K. B. Walsh
Keyword(s):  
Machine Vision ◽  
Satellite Imagery ◽  
Fruit Load

scholarly journals Estimation of Fruit Load in Australian Mango Orchards Using Machine Vision

Agronomy ◽  
2021 ◽  
Vol 11 (9) ◽  
pp. 1711
Author(s):  
Nicholas Todd Anderson ◽  
Kerry Brian Walsh ◽  
Anand Koirala ◽  
Zhenglin Wang ◽  
Marcelo Henrique Amaral ◽  
...  
Keyword(s):  
Machine Vision ◽  
Crop Damage ◽  
Tree Level ◽  
Percentage Error ◽  
Canopy Architecture ◽  
Night Time ◽  
Average Percentage ◽  
Absolute Percentage Error ◽  
Average Percentage Error ◽  
Fruit Load

The performance of a multi-view machine vision method was documented at an orchard level, relative to packhouse count. High repeatability was achieved in night-time imaging, with an absolute percentage error of 2% or less. Canopy architecture impacted performance, with reasonable estimates achieved on hedge, single leader and conventional systems (3.4, 5.0, and 8.2 average percentage error, respectively) while fruit load of trellised orchards was over-estimated (at 25.2 average percentage error). Yield estimations were made for multiple orchards via: (i) human count of fruit load on ~5% of trees (FARM), (ii) human count of 18 trees randomly selected within three NDVI stratifications (CAL), (iii) multi-view counts (MV-Raw) and (iv) multi-view corrected for occluded fruit using manual counts of CAL trees (MV-CAL). Across the nine orchards for which results for all methods were available, the FARM, CAL, MV-Raw and MV-CAL methods achieved an average percentage error on packhouse counts of 26, 13, 11 and 17%, with SD of 11, 8, 11 and 9%, respectively, in the 2019–2020 season. The absolute percentage error of the MV-Raw estimates was 10% or less in 15 of the 20 orchards assessed. Greater error in load estimation occurred in the 2020–2021 season due to the time-spread of flowering. Use cases for the tree level data on fruit load was explored in context of fruit load density maps to inform early harvesting and to interpret crop damage, and tree frequency distributions based on fruit load per tree.

scholarly journals Measurement of fruit color-heterogeneity index and their relation to Jatropha curcas L., oil in Colombia

2016 ◽  
Vol 66 (1) ◽  
Author(s):  
Luis Fernando Campuzano Duque ◽  
Fernando Cardeño
Keyword(s):  
Jatropha Curcas ◽  
Soil Conditions ◽  
Jatropha Curcas L ◽  
High Oleic ◽  
Brown State ◽  
Fruit Maturity ◽  
Harvest Timing ◽  
Adaptive Processes ◽  
In The Wild ◽  
Fruit Harvest

Jatropha curcas L. is a wild species in the domestication process with an oil and biofuel potential use. The evolutionary-adaptive processes have conferred a reproductive mechanism based on protogyny-geitonogamy, achieving a floral asynchrony addressed to ensure the outcrossing and survival in extreme climate and soil conditions. In the wild, this plant performs a fruit heterogeneity, an unattractive attribute for harvest timing. Therefore, this research aimed to determine the number of harvest, yield and proportion, and physiological states of fruit maturity with the maximum oil quantity and quality. A randomized complete block factorial arrangement of treatments design was performed. The varieties and stages of fruit ripeness, were the factors. A relation of 19: 31: 36: 14 with fruits pericarp colors: green: yellow: yellow-brown: brown, was obtained. The best harvest timing was during the yellow-brown state, which represented the highest oil content associated with high oleic acid and linoleic contents. Nevertheless, for a fruit harvest timing, the use of the epicarp color index is recommended. These results suggest the floral asynchrony breeding traits, requires the ensuring of the fruit homogeneity for mechanical or manual harvesting.

scholarly journals Differential Fruit Load in Melon (Cucumis melo L.) Affects Shoot and Root Growth, and Vine Decline Symptoms

HortScience ◽  
1997 ◽  
Vol 32 (3) ◽  
pp. 526B-526 ◽  
Author(s):  
David W. Wolff ◽  
Daniel I. Leskovar ◽  
Mark C. Black ◽  
Marvin E. Miller
Keyword(s):  
Root Rot ◽  
Cucumis Melo ◽  
Growth Parameters ◽  
Symptom Expression ◽  
Germplasm Screening ◽  
Fruit Maturity ◽  
Vine Decline ◽  
Cucumis Melo L ◽  
Root Size ◽  
Fruit Load

The effect of zero, one, and two fruits per vine on plant growth and reaction to Monosporascus root rot/vine decline were investigated. In the first study, four cultivars with differing levels of tolerance were evaluated (`Primo', `Deltex', `Caravelle', `Magnum 45'). Vine decline ratings were taken weekly during the harvest period for 4 weeks. Treatments with no fruit showed delayed and less-severe vine decline symptoms. Temperature also effected vine decline symptom expression. In a Fall test, with lower temperatures during fruit maturity, symptoms were delayed in all treatments and often absent in treatments with no fruit load. Vine decline symptom expression is greatly effected by physiological (fruit load) and temperature stress. A subsequent study was conducted to more precisely quantify the effect of various fruit loads on shoot/root partitioning and vine decline symptoms. In addition to growth parameters root disease ratings were taken. `Caravelle', the most-susceptible genotype, was grown under differing fruit loads as mentioned above in Weslaco and Uvalde, Texas. As fruit load increased, root size decreased. Increased vine decline symptoms were observed under higher fruit loads. The implications on germplasm screening and breeding for resistance will be discussed.

scholarly journals Attempting to Estimate the Unseen—Correction for Occluded Fruit in Tree Fruit Load Estimation by Machine Vision with Deep Learning

Agronomy ◽  
2021 ◽  
Vol 11 (2) ◽  
pp. 347
Author(s):  
Anand Koirala ◽  
Kerry B. Walsh ◽  
Zhenglin Wang
Keyword(s):  
Deep Learning ◽  
Random Forest ◽  
Machine Vision ◽  
Reference Method ◽  
Ground Truth ◽  
Image Features ◽  
Ground Vehicles ◽  
Method Error ◽  
Robust Model ◽  
Fruit Load

Machine vision from ground vehicles is being used for estimation of fruit load on trees, but a correction is required for occlusion by foliage or other fruits. This requires a manually estimated factor (the reference method). It was hypothesised that canopy images could hold information related to the number of occluded fruits. Several image features, such as the proportion of fruit that were partly occluded, were used in training Random forest and multi-layered perceptron (MLP) models for estimation of a correction factor per tree. In another approach, deep learning convolutional neural networks (CNNs) were directly trained against harvest count of fruit per tree. A R2 of 0.98 (n = 98 trees) was achieved for the correlation of fruit count predicted by a Random forest model and the ground truth fruit count, compared to a R2 of 0.68 for the reference method. Error on prediction of whole orchard (880 trees) fruit load compared to packhouse count was 1.6% for the MLP model and 13.6% for the reference method. However, the performance of these models on data of another season was at best equivalent and generally poorer than the reference method. This result indicates that training on one season of data was insufficient for the development of a robust model.

The time course of calcium deposition in shells of the barnacle (Balanus amphititre amphititre) during cyprid-juvenile metamorphosis

1994 ◽  
Vol 52 ◽  
pp. 178-179
Author(s):  
Nancy R. Wallace ◽  
Craig C. Freudenrich ◽  
Karl Wilbur ◽  
Peter Ingram ◽  
Ann LeFurgey
Keyword(s):  
Electron Microscopy ◽  
Scanning Electron Microscopy ◽  
Time Course ◽  
Vertical Plane ◽  
Mantle Cavity ◽  
Qualitative Assessment ◽  
Calcium Deposition ◽  
Free Swimming ◽  
Freeze Dried ◽  
Scanning Electron

The morphology of balanomorph barnacles during metamorphosis from the cyprid larval stage to the juvenile has been examined by light microscopy and scanning electron microscopy (SEM). The free-swimming cyprid attaches to a substrate, rotates 90° in the vertical plane, molts, and assumes the adult shape. The resulting metamorph is clad in soft cuticle and has an adult-like appearance with a mantle cavity, thorax with cirri, and incipient shell plates. At some time during the development from cyprid to juvenile, the barnacle begins to mineralize its shell, but it is not known whether calcification occurs before, during, or after ecdysis. To examine this issue, electron probe x-ray microanalysis (EPXMA) was used to detect calcium in cyprids and juveniles at various times during metamorphosis.Laboratory-raised, free-swimming cyprid larvae were allowed to settle on plastic coverslips in culture dishes of seawater. The cyprids were observed with a dissecting microscope, cryopreserved in liquid nitrogen-cooled liquid propane at various times (0-24 h) during metamorphosis, freeze dried, rotary carbon-coated, and examined with scanning electron microscopy (SEM). EPXMA dot maps were obtained in parallel for qualitative assessment of calcium and other elements in the carapace, wall, and opercular plates.

Machine Vision

2009 ◽  
Author(s):  
Wesley E. Snyder ◽  
Hairong Qi
Keyword(s):  
Machine Vision

Who owns a personal home page?

2003 ◽  
Vol 62 (2) ◽  
pp. 121-129 ◽  
Author(s):  
Astrid Schütz ◽  
Franz Machilek
Keyword(s):  
Search Engine ◽  
Home Page ◽  
Sampling Strategies ◽  
Home Pages ◽  
Personal Home Pages ◽  
Age And Sex

Research on personal home pages is still rare. Many studies to date are exploratory, and the problem of drawing a sample that reflects the variety of existing home pages has not yet been solved. The present paper discusses sampling strategies and suggests a strategy based on the results retrieved by a search engine. This approach is used to draw a sample of 229 personal home pages that portray private identities. Findings on age and sex of the owners and elements characterizing the sites are reported.

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