Advanced classification of volunteer potato in a sugar beet field

Classification of sugar beet and volunteer potato reflection spectra with a neural network and statistical discriminant analysis to select discriminative wavelengths

Computers and Electronics in Agriculture ◽

10.1016/j.compag.2010.05.008 ◽

2010 ◽

Vol 73 (2) ◽

pp. 146-153 ◽

Cited By ~ 10

Author(s):

A.T. Nieuwenhuizen ◽

J.W. Hofstee ◽

J.C. van de Zande ◽

J. Meuleman ◽

E.J. van Henten

Keyword(s):

Neural Network ◽

Discriminant Analysis ◽

Sugar Beet ◽

Reflection Spectra ◽

Volunteer Potato

Transfer learning for the classification of sugar beet and volunteer potato under field conditions

Biosystems Engineering ◽

10.1016/j.biosystemseng.2018.06.017 ◽

2018 ◽

Vol 174 ◽

pp. 50-65 ◽

Cited By ~ 34

Author(s):

Hyun K. Suh ◽

Joris IJsselmuiden ◽

Jan Willem Hofstee ◽

Eldert J. van Henten

Keyword(s):

Sugar Beet ◽

Transfer Learning ◽

Field Conditions ◽

Volunteer Potato

Chewing behaviour of Holstein steers fed diets containing different levels of non-forage fibre in a low forage diet

Proceedings of the British Society of Animal Science ◽

10.1017/s1752756200030003 ◽

2009 ◽

Vol 2009 ◽

pp. 161-161

Author(s):

M Mojtahedi ◽

M Danesh Mesgaran ◽

A Heravi Moussavi ◽

A Tahmasbi

Keyword(s):

Sugar Beet ◽

Barley Grain ◽

Sugar Beet Pulp ◽

Soluble Fiber ◽

High Concentration ◽

Beet Pulp ◽

Feeding Value ◽

Lactating Dairy Cattle ◽

Chewing Behaviour

Sugar beet pulp (SBP) is fed to ruminants as a non-forage ﬁbre source (NFFS) ingredient. Early work by Ronning and Bath (1962) demonstrated that SBP was similar in feeding value to barley grain for lactating dairy cattle, supporting classiﬁcation of beet pulp as an energy concentrate. Sugar beet pulp contains approximately 40% neutral detergent ﬁbre (NDF) and is unique in its high concentration of neutral detergent soluble ﬁber, especially pectic substances (~25% of dry matter (DM)). However, the effects of SBP when substituted with different feed sources in ration are variable that depend on chemical composition, types and physical characteristics. The time which cows spent eating and ruminating (total chewing time) is a measure of the physically effective fibre value of a feed. The objective of the present experiment was to evaluate the effect of substitution of barley grain with SBP as a NFFS on chewing activity of Holstein steers.

Automatic detection and classification of leaf spot disease in sugar beet using deep learning algorithms

Physica A Statistical Mechanics and its Applications ◽

10.1016/j.physa.2019.122537 ◽

2019 ◽

Vol 535 ◽

pp. 122537 ◽

Cited By ~ 7

Author(s):

Mehmet Metin Ozguven ◽

Kemal Adem

Keyword(s):

Deep Learning ◽

Sugar Beet ◽

Leaf Spot ◽

Learning Algorithms ◽

Automatic Detection ◽

Leaf Spot Disease ◽

Spot Disease

Volunteer potato timing of removal with and without glyphosate in sugar beet.

Proceedings of 35th Biennial Meeting ◽

10.5274/assbt.2009.17 ◽

2009 ◽

Author(s):

Don W. Morishita ◽

Joel Felix ◽

Donald L. Shouse ◽

J. Daniel Henningsen

Keyword(s):

Sugar Beet ◽

Volunteer Potato

Classification of sugar beet strains for resistance to Aphanomyces Cochlioides in greenhouse tests

Journal of Sugarbeet Research ◽

10.5274/jsbr.12.8.651 ◽

1964 ◽

Vol 12 (8) ◽

pp. 651-656 ◽

Cited By ~ 2

Author(s):

C. L. Schneider

Keyword(s):

Sugar Beet ◽

Aphanomyces Cochlioides ◽

Greenhouse Tests

Volunteer potato density interference in sugar beet.

Proceedings of 35th Biennial Meeting ◽

10.5274/assbt.2009.20 ◽

2009 ◽

Author(s):

Donald L. Shouse ◽

Don W. Morishita ◽

J. Daniel Henningsen ◽

Robyn C. Walton ◽

Michael P. Quinn

Keyword(s):

Sugar Beet ◽

Volunteer Potato

Classification of Processing Damage in Sugar Beet (Beta vulgaris) Seeds by Multispectral Image Analysis

Sensors ◽

10.3390/s19102360 ◽

2019 ◽

Vol 19 (10) ◽

pp. 2360 ◽

Cited By ~ 4

Author(s):

Zahra Salimi ◽

Birte Boelt

Keyword(s):

Sugar Beet ◽

Quality Assessment ◽

Multispectral Imaging ◽

Seed Quality ◽

Mechanical Damage ◽

Derived Data ◽

Damage Types ◽

Potential Tool ◽

Vision Technique

The pericarp of monogerm sugar beet seed is rubbed off during processing in order to produce uniformly sized seeds ready for pelleting. This process can lead to mechanical damage, which may cause quality deterioration of the processed seeds. Identification of the mechanical damage and classification of the severity of the injury is important and currently time consuming, as visual inspections by trained analysts are used. This study aimed to find alternative seed quality assessment methods by evaluating a machine vision technique for the classification of five damage types in monogerm sugar beet seeds. Multispectral imaging (MSI) was employed using the VideometerLab3 instrument and instrument software. Statistical analysis of MSI-derived data produced a model, which had an average of 82% accuracy in classification of 200 seeds in the five damage classes. The first class contained seeds with the potential to produce good seedlings and the model was designed to put more limitations on seeds to be classified in this group. The classification accuracy of class one to five was 59, 100, 77, 77 and 89%, respectively. Based on the results we conclude that MSI-based classification of mechanical damage in sugar beet seeds is a potential tool for future seed quality assessment.

New classification of the reproduction modes in sugar beet

Sugar Tech ◽

10.1007/bf02956879 ◽

2002 ◽

Vol 4 (1-2) ◽

pp. 45-51 ◽

Cited By ~ 6

Author(s):

E. V. Levites

Keyword(s):

Sugar Beet ◽

New Classification

Volunteer potato interference and removal timing in sugar beet

Weed Technology ◽

10.1017/wet.2021.13 ◽

2021 ◽

pp. 1-5

Author(s):

Albert T. Adjesiwor ◽

Joel Felix ◽

Don W. Morishita

Keyword(s):

Sugar Beet ◽

Potato Tuber ◽

Tuber Initiation ◽

Competition Experiment ◽

Root Yield ◽

Beet Root ◽

Volunteer Potato ◽

Untreated Check ◽

Initiation Stage ◽

Sugar Beet Root

Abstract Field studies were conducted from 2005 to 2009 in Idaho and Oregon to 1) evaluate the competitive effect of volunteer potato on sugar beet yield (volunteer potato competition experiment), and 2) determine the optimum timing of volunteer potato removal from glyphosate-tolerant sugar beet fields using glyphosate (volunteer potato removal timing experiment). The volunteer potato competition experiment consisted of eight potato densities, including the untreated check: 0, 6,741, 10,092, 13,455, 16,818, 20,184, 26,910, and 40,365 tubers ha−1. The volunteer potato removal experiment consisted of 10 removal timings (including the untreated check) ranging from the 10-cm rosette stage to mid-tuber bulking. There was a linear decrease in sugar beet root and sucrose yield as volunteer potato density increased (P < 0.001) such that with every volunteer potato tuber per square meter, sugar beet root yield decreased by 15% and sucrose yield decreased by 14%. At the highest volunteer potato density (40,365 tubers ha−1), sugar beet root yield was 29,600 kg ha−1 (compared to 73,600 kg ha−1 for the untreated), representing a 60% reduction in sugar beet root yield. In the removal timing study, a one-time application of glyphosate at the 10-cm rosette, hooking, and tuber initiation stages provided 74% to 98% reduction in volunteer potato tuber biomass. Delaying volunteer potato removal beyond the tuber initiation stage reduced sugar beet root and sucrose yield (12% to 20%), resulting in an economic loss of $104 to $161 per hectare. The best potato removal timing that optimizes the trade-off between improved control and potential for sugar beet yield reductions is before or at the tuber initiation stage.