scholarly journals Deep Learning for Non-Invasive Diagnosis of Nutrient Deficiencies in Sugar Beet Using RGB Images

Sensors ◽  
2020 ◽  
Vol 20 (20) ◽  
pp. 5893 ◽  
Author(s):  
Jinhui Yi ◽  
Lukas Krusenbaum ◽  
Paula Unger ◽  
Hubert Hüging ◽  
Sabine J. Seidel ◽  
...  
Keyword(s):  
Sugar Beet ◽  
Optical Sensors ◽  
Nutrient Deficiency ◽  
Nutrient Deficiencies ◽  
Potential Yield ◽  
Sugar Beets ◽  
Non Invasive ◽  
Deficiency Symptoms ◽  
Rgb Images ◽  
N Deficiency

In order to enable timely actions to prevent major losses of crops caused by lack of nutrients and, hence, increase the potential yield throughout the growing season while at the same time prevent excess fertilization with detrimental environmental consequences, early, non-invasive, and on-site detection of nutrient deficiency is required. Current non-invasive methods for assessing the nutrient status of crops deal in most cases with nitrogen (N) deficiency only and optical sensors to diagnose N deficiency, such as chlorophyll meters or canopy reflectance sensors, do not monitor N, but instead measure changes in leaf spectral properties that may or may not be caused by N deficiency. In this work, we study how well nutrient deficiency symptoms can be recognized in RGB images of sugar beets. To this end, we collected the Deep Nutrient Deficiency for Sugar Beet (DND-SB) dataset, which contains 5648 images of sugar beets growing on a long-term fertilizer experiment with nutrient deficiency plots comprising N, phosphorous (P), and potassium (K) deficiency, as well as the omission of liming (Ca), full fertilization, and no fertilization at all. We use the dataset to analyse the performance of five convolutional neural networks for recognizing nutrient deficiency symptoms and discuss their limitations.

scholarly journals A Study of Factors Influencing Plant Growth by WSN Approach and Plant Nutrient Deficiency Classification in Tomato Using SVM

2021 ◽  
Vol 23 (06) ◽  
pp. 36-46
Author(s):  
Vrunda Kusanur ◽  
◽  
Veena S Chakravarthi ◽  
Keyword(s):  
Machine Learning ◽  
Plant Growth ◽  
Nutrient Deficiency ◽  
Learning Model ◽  
Plant Nutrients ◽  
Polynomial Kernel ◽  
Second Phase ◽  
Nutrient Deficiencies ◽  
Deficiency Symptoms ◽  
Machine Learning Model

Soil temperature and humidity straight away influence plant growth and the availability of plant nutrients. In this work, we carried out experiments to identify the relationship between climatic parameters and plant nutrients. When the relative humidity was very high, deficiency symptoms were shown on plant leaves and fruits. But, recognizing and managing these plant nutrients manually would become difficult. However, no much research has been done in this field. The main objective of this research was to propose a machine learning model to manage nutrient deficiencies in the plant. There were two main phases in the proposed research. In the first phase, the humidity, temperature, and soil moisture in the greenhouse environment were collected using WSN and the influence of these parameters on the growth of plants was studied. During experimentation, it was investigated that the transpiration rate decreased significantly and the macronutrient contents in the plant leave decreased when the humidity was 95%. In the second phase, a machine learning model was developed to identify and classify nutrient deficiency symptoms in a tomato plant. A total of 880 images were collected from Bingo images to form a dataset. Among all these images, 80% (704 images) of the dataset were used to train the machine learning model and 20% (176 images) of the dataset were used for testing the model performance. In this study, we selected K-means Clustering for key points detection and SVM for classification and prediction of nutrient stress in the plant. SVM using linear kernel performed better with the accuracy rates of 89.77 % as compared to SVM using a polynomial kernel.

scholarly journals (213) Establishing Minimum Critical Nutrient Standards for Herbaceous Perennials

HortScience ◽  
2006 ◽  
Vol 41 (4) ◽  
pp. 1084A-1084
Author(s):  
Allison L. Byrd ◽  
Velva A. Groover ◽  
Holly L. Scoggins
Keyword(s):  
Crop Production ◽  
Nutrient Deficiency ◽  
Nutrient Deficiencies ◽  
Chronological Order ◽  
Hydroponic System ◽  
Herbaceous Perennials ◽  
Deficiency Symptoms ◽  
Advanced Stages ◽  
Calcium Magnesium ◽  
Copper Zinc

Herbaceous perennials comprise one of the fastest-growing segments of floriculture crop production. Little information has been published regarding their mineral nutrition requirements, specifically nutrient foliar standards and nutrient deficiency symptoms. Our research documents visual symptoms of nutrient deficiencies in the chronological order in which they appear from incipient to advanced stages, and establishes foliar analysis standards by correlating nutrient levels with initial and advanced stages of symptoms for nitrogen, phosphorus, potassium, calcium, magnesium, sulfur, iron, copper, zinc, manganese, molybdenum, and boron. Rooted cuttings were grown for as many as 12 weeks in a hydroponic system with modified Hoagland's solution minus the element of interest, along with complete nutrient solution controls. Taxa selected for study were representative of commonly grown varieties and of differing families; Verbena canadensis `Homestead Purple' (clump verbena), Heliopsis helianthoides `Bressingham Doubloon' (false sunflower) and Veronica × `Goodness Grows' (speedwell). Days to incipient deficiency symptoms ranged from 5 to 60. Chronological order of appearance was consistent with Fe and Ca symptoms appearing within 10 days for all three taxa. Other deficiency symptoms varied both by taxa and in chronology. Root and shoot dry weights were closely and positively correlated with time to incipient deficiency.

scholarly journals Nutrient Deficiency Symptoms of Woody Ornamental Plants in South Florida

EDIS ◽  
2008 ◽  
Vol 2008 (3) ◽  
Author(s):  
Timothy K. Broschat
Keyword(s):  
Ornamental Plants ◽  
Nutrient Deficiency ◽  
South Florida ◽  
Nutrient Deficiencies ◽  
Fact Sheet ◽  
Deficiency Symptoms ◽  
Woody Ornamental ◽  
Trees And Shrubs

ENH-1098, a 5-page illustrated fact sheet by Timothy K. Broschat, describes and illustrates typical symptoms for common nutrient deficiencies in trees and shrubs grown in south Florida landscapes. Published by the UF Department of Environmental Horticulture, March 2008. ENH1098/EP362: Nutrient Deficiency Symptoms of Woody Ornamental Plants in South Florida (ufl.edu)

CONVISO® SMART – an innovative approach of weed control in sugar beet

2016 ◽  
pp. 517-524 ◽  
Author(s):  
Martin Wegener ◽  
Natalie Balgheim ◽  
Maik Klie ◽  
Carsten Stibbe ◽  
Bernd Holtschulte
Keyword(s):  
Sugar Beet ◽  
Weed Control ◽  
Field Studies ◽  
Essential Amino Acids ◽  
Global Market ◽  
Sugar Beets ◽  
Dose Rates ◽  
Innovative System ◽  
Bayer Cropscience ◽  
Als Inhibitor

KWS SAAT SE and Bayer CropScience AG are jointly developing and commercializing an innovative system of weed control in sugar beet for the global market under the name of CONVISO SMART. The technology is based on the breeding of sugar beet cultivars that are tolerant to herbicides of the ALS-inhibitor-class with a broad-spectrum weed control. This will give farmers a new opportunity to make sugar beet cultivation easier, more flexible in its timing and more efficient. The use of CONVISO, as new herbicide in sugar beet, will make it possible to control major weeds with low dose rates of product and reduced number of applications in the future. The tolerance is based on a change in the enzyme acetholactate synthase, which is involved in the biosynthesis of essential amino acids. This variation can occur spontaneously during cell division. During the development, sugar beets with this spontaneously changed enzyme were specifically selected and used for further breeding of CONVISO SMART cultivars. As such, these varieties are not a product of genetic modification. Field studies with CONVISO SMART hybrids showed complete crop selectivity and a broad and reliable efficacy against a large range of major weeds. The bio-dossier for an EU-wide registration of CONVISO was submitted in April in 2015. The variety inscription process is in preparation in different countries. The system CONVISO SMART is scheduled to be available to farmers in 2018 at the earliest.

Determination of microbiological activity during the processing of frost damaged sugar beets

2014 ◽  
pp. 228-231 ◽  
Author(s):  
Maciej Wojtczak ◽  
Aneta Antczak-Chrobot ◽  
Edyta Chmal-Fudali ◽  
Agnieszka Papiewska
Keyword(s):  
Sugar Beet ◽  
Microbiological Activity ◽  
Microbiological Contamination ◽  
Sugar Beets ◽  
Bacterial Metabolites ◽  
Kinetics Of

The aim of the study is to evaluate the kinetics of the synthesis of dextran and other bacterial metabolites as markers of microbiological contamination of sugar beet.

Sugar Beet Yield and Quality Prediction at Multiple Harvest Dates Using Active-Optical Sensors

2016 ◽  
Vol 108 (1) ◽  
pp. 273-284 ◽  
Author(s):  
Honggang Bu ◽  
Lakesh K. Sharma ◽  
Anne Denton ◽  
David W. Franzen
Keyword(s):  
Sugar Beet ◽  
Optical Sensors ◽  
Quality Prediction ◽  
Yield And Quality ◽  
Harvest Dates

Leaf—Nutrient Deficiency Symptoms

1984 ◽  
pp. 25-25
Author(s):  
Janice Glimn-Lacy ◽  
Peter B. Kaufman
Keyword(s):  
Nutrient Deficiency ◽  
Deficiency Symptoms

scholarly journals Sensitivity of Cercospora beticola Isolates to Azoxystrobin

2020 ◽  
Vol 69 (1-2) ◽  
pp. 1-4
Author(s):  
Milijanka Balandžić ◽  
Vera Stojšin ◽  
Mila Grahovac ◽  
Ferenc Bagi ◽  
Mladen Petreš ◽  
...  
Keyword(s):  
Biological Activity ◽  
High Risk ◽  
Sugar Beet ◽  
Mycelial Growth ◽  
Cercospora Beticola ◽  
Experimental Site ◽  
Sugar Beets ◽  
Qoi Fungicides ◽  
Moderately Resistant ◽  
Very High

SummarySugar beet leaf spot, caused by the air-borne fungus Cercospora beticola Sacc., leads to a decrease in sugar beet leaf mass and the consequent regrowth of leaves based on exploiting the sugar reserves stored in the plant’s roots, thus ultimately resulting in lower yields and sugar contents of sugar beets. Azoxystrobin belongs to the group of QoI fungicides, which inhibit mitochondrial respiration by blocking cytochrome c reductase. The QoI fungicides are characterized by a very high risk of resistance interfering with their biological activity. For the purpose of testing the azoxystrobin sensitivity of the Cercospora beticola population found at the site of Rimski Šančevi, a collection of 84 isolates was assembled and tested for sensitivity to azoxystrobin by measuring the mycelial growth on fungicide-amended media with the addition of SHAM. The results obtained indicate that none of the isolates tested exhibited complete sensitivity to azoxystrobin, 4% were found to have reduced sensitivity, 26% were moderately resistant and 70% were highly resistant. A higher proportion of resistant isolates recorded is associated with the loss of azoxystrobin biological efficacy at the experimental site.

scholarly journals Evaluation of glassy frits as micronutrient fertilizers. II. Manganese frits.

1967 ◽  
Vol 15 (1) ◽  
pp. 21-30
Author(s):  
C.H. Henkens ◽  
K.W. Smilde
Keyword(s):  
Sugar Beet ◽  
Field Tests ◽  
Sugar Production ◽  
Mn Deficiency ◽  
Deficiency Symptoms ◽  
Foliar Treatment ◽  
Marsh Spot ◽  
Mn Content ◽  
Second Year ◽  
Better Than

In pot and field tests MnSO4 and the frits FTE Z 4 (13 % Mn), HZ 1 (15.9 % Mn) and HZ 17 (21 % Mn) increased reducible soil Mn for at least 1 1/2-2 years. Mn content of pasture increased four-fold in the first cut after application of 400 kg/ha MnSO4 but sharply decreased in later cuts and became negligible by the second year. 400 kg/ha HZ 17 did not affect pasture Mn. In peas 400 kg of soil- or foliar applied MnSO4 controlled marsh spot better than 800 kg HZ 1; spraying at the middle and again at the end of the blooming stage gave the best control. With sugar beet, soil dressings of MnSO4, HZ 1 and HZ 17 equally increased yield, sugar production and leaf Mn, and decreased incidence of Mn deficiency. When the rates of these fertilizers were increased from 100, 179 and 86 kg respectively to 400, 714 and 343 kg, sugar production was not significantly improved; leaf Mn and incidence of deficiency symptoms responded to the higher Mn rates. Soil application was rather better than foliar treatment. No treatment controlled Mn deficiency throughout the entire season. The % of Mn-deficient plants was related, negatively, to leaf and reducible soil Mn, but not to yield. Soil-applied Mn did not control gray spot in oats or increase yields but sprayed Mn did. (Abstract retrieved from CAB Abstracts by CABI’s permission)

scholarly journals The intensity of mechanical cultivation measures vs. the amount of weed seeds in the soil

2013 ◽  
Vol 54 (1) ◽  
pp. 163-173
Author(s):  
Marian Wesołowski ◽  
Cezary Kwiatkowski
Keyword(s):  
Sugar Beet ◽  
Species Composition ◽  
Deep Layer ◽  
Mineral Fertilization ◽  
Loessial Soil ◽  
Sugar Beets ◽  
Weed Seeds ◽  
Emergence Phase ◽  
Weed Removal

The effect of the number of mechanical operations in sugar beets plantation on the amount and species composition of weed seeds in the 0-5 cm deep layer of the loessial soil was studied. It has been proved that reduction in the number of weed seeds depends upon both the frequency of weeding-out operations and the level of agrotechnic. The highest decrease in the number of fruit and weed seeds was caused by eightfold weed removal which took place during the period from emergence phase to the joining of sugar beet rows. Application of increased mineral fertilization, microelements, fungicides, and insecticides caused the number of weed seeds to be reduced by 5,9%, in comparison to extensive agrotechnical level.

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