Plant Nutrient Deficiency Symptoms

2003 ◽  
Vol 65 (4) ◽  
pp. 246-246
Author(s):  
David R. Hershey
Keyword(s):  
Nutrient Deficiency ◽  
Plant Nutrient ◽  
Deficiency Symptoms

Plant Nutrient Deficiency Symptoms. Physiological Basis

1941 ◽  
Vol 33 (5) ◽  
pp. 648-653 ◽  
Author(s):  
E. E. DeTurk
Keyword(s):  
Nutrient Deficiency ◽  
Plant Nutrient ◽  
Physiological Basis ◽  
Deficiency Symptoms

Leaf—Nutrient Deficiency Symptoms

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

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  Soil environment and nutrient status of Norway spruce (Picea abies [L.] Karst.) underplantings in conditions of the 8th FAZ in the Hrubý Jeseník Mts.

2011 ◽  
Vol 57 (No. 4) ◽  
pp. 141-152
Author(s):  
J. Pecháček ◽  
D. Vavříček ◽  
P. Samec
Keyword(s):  
Picea Abies ◽  
Norway Spruce ◽  
Nutrient Deficiency ◽  
Normal Growth ◽  
Nutrient Status ◽  
Limiting Factor ◽  
Soil Environment ◽  
Deficiency Symptoms ◽  
Humus Substances ◽  
Statistical Survey

The main objective of this study was to investigate the causes of nutrient deficiency symptoms in Norway spruce (Picea abies [L.] Karst.) underplantings in the Hrub&yacute; Jesen&iacute;k Mts. In the area concerned 19 research plots were established, representing the ridge sites of the 8th FAZ of acid edaphic categories. On these plots samples were taken from topmost soil horizons and needle samples were collected in two series &ndash; from healthy and from damaged trees. The results of this study demonstrate that the nutrient deficiency symptoms and reduced vitality of evaluated underplantings were caused by the insufficient uptake of main nutrients (Mg, P, K, N<sub>t</sub>). High contents of toxic elements Al, S in damaged needles are another factor that negatively influences the health status of these underplantings.<br />A statistical survey showed that damage to underplantings increased with decreasing proportions of main nutrients (N<sub>t</sub>, Mg, Ca, K) in organomineral horizons. At the same time the content of basic nutrients (N<sub>t</sub>, Mg, Ca, K) was found to increase in this horizon with an increasing proportion of oxidizable organic carbon (C<sub>ox</sub>). The proportion of humus substances and the content of basic nutrients (N<sub>t</sub>, Mg, Ca, K) in organomineral horizons become a limiting factor for the normal growth and development of Norway spruce plantings in the ridge part of the Hrub&yacute; Jesen&iacute;k Mts.

scholarly journals Sistem Pakar dalam Menganalisis Defisiensi Nutrisi Tanaman Hidroponik Menggunakan Metode Certainty Factor

2021 ◽  
pp. 203-208
Author(s):  
Yerri Kurnia Febrina ◽  
Sarjon Defit ◽  
Gunadi Widi Nurcahyo
Keyword(s):  
Expert System ◽  
Nutrient Deficiency ◽  
Nutrient Content ◽  
Nutritional Deficiency ◽  
Nutritional Deficiencies ◽  
Plant Nutrient ◽  
Certainty Factor ◽  
Plant Parts ◽  
Computer Scientists ◽  
Good Nutrition

Currently the Expert system has become a field of research for computer scientists as well as agricultural scientists for applications in various information development. The Expert System can be designed to simulate one or more of the ways an agricultural expert uses his knowledge and experience in making the diagnosis and passing on the necessary recommendations regarding nutritional deficiencies. Nutrient deficiency is a lack of food for survival in plants. The nutrient content of plant parts, especially the leaves, is very relevant to be used to identify nutritional deficiencies. Provide the results of a diagnosis of nutritional deficiency to farmers to be a benchmark for improving plant nutrients and providing good nutrition for hydroponic plants. The data used are nutritional deficiency data and symptoms as well as nutritional solutions obtained from farmer data at the Payakumbuh City Agriculture Office. The method used in this expert system is the Certainty Factor (CF) method. This method provides a diagnosis in the form of certainty or uncertainty of conditions in the rules used to conclude. The results of testing this method showed as many as 12 nutritional deficiencies were detected with 41 symptoms experienced. So that it can measure the level of nutritional deficiency that occurs. Expert System in Analyzing Hydroponic Plant Nutrient Deficiency Using Certainty Factor Method can show that predictions are almost 94% accurate.

Carbon — A plant nutrient, deficiency and sufficiency

1990 ◽  
Vol 13 (9) ◽  
pp. 1073-1078 ◽  
Author(s):  
N. Schwarz ◽  
B. R. Strain
Keyword(s):  
Nutrient Deficiency ◽  
Plant Nutrient

Fiber optic spectrophotometry monitoring of plant nutrient deficiency under hydroponic culture conditions

1999 ◽  
Author(s):  
Oi Wah Liew ◽  
William S. L. Boey ◽  
Anand K. Asundi ◽  
Jun-Wei Chen ◽  
Duo-Min He
Keyword(s):  
Fiber Optic ◽  
Nutrient Deficiency ◽  
Culture Conditions ◽  
Hydroponic Culture ◽  
Plant Nutrient

Nutrient deficiency symptoms in yams (Dioscorea spp.)

2004 ◽  
Vol 44 (4) ◽  
pp. 155-162 ◽  
Author(s):  
H Shiwachi ◽  
CC Okonkwo ◽  
R Asiedu
Keyword(s):  
Nutrient Deficiency ◽  
Deficiency Symptoms

scholarly journals (348) Geranium Nutrient Deficiency Symptoms, Partitioning, and Bioenergetic Costs of Construction

HortScience ◽  
2005 ◽  
Vol 40 (4) ◽  
pp. 1060D-1060
Author(s):  
Dharmalingam S. Pitchay ◽  
Jonathan M. Frantz ◽  
James C. Locke
Keyword(s):  
Nutrient Deficiency ◽  
Production Quality ◽  
Construction Costs ◽  
Additional Tool ◽  
Deficiency Symptoms ◽  
Hidden Hunger ◽  
Advanced Stages ◽  
Essential Nutrient ◽  
Chlorotic Leaf ◽  
The Impact

Geranium (Pelargonium ×hortorum) is considered to be one of the top-selling floriculture plants, and is highly responsive to increased macro- and micronutrient bioavailability. In spite of its economic importance, there are few nutrient disorder symptoms reported for this species. The lack of nutritional information contributes to suboptimal geranium production quality. Understanding the bioenergetic construction costs during nutrient deficiency can provide insight into the significance of that element predisposing plants to other stress. Therefore, this study was conducted to investigate the impact of nutrient deficiency on plant growth. Pelargonium plants were grown hydroponically in a glass greenhouse. The treatment consisted of a complete modified Hoagland's millimolar concentrations of macronutrients (15 NO3-N, 1.0 PO4-P, 6.0 K, 5.0 Ca, 2.0 Mg, and 2.0 SO4-S) and micromolar concentrations of micronutrients (72 Fe, 9.0 Mn, 1.5 Cu, 1.5 Zn, 45.0 B, and 0.1 Mo) and 10 additional solutions each devoid of one essential nutrient (N, P, Ca, Mg, S, Fe, Mn, Cu, Zn, or B). The plants were photographed and divided into young, maturing, and old leaves, the respective petioles, young and old stems, flowers, buds, and roots at “hidden hunger,” incipient, mid- and advanced-stages of nutrient stress. Unique visual deficiency symptoms of interveinal red pigmentation were noted on the matured leaves of P- and Mg-deficient plants, while N-deficient plants developed chlorotic leaf margins. Tissue N concentration greatly influenced bioenergetic construction costs, probably due to differences in protein content. This information will provide an additional tool in producing premium geraniums for the greenhouse industry.

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