Portable system approach of monitoring plant nutrient deficiency using fiber optic spectrophotometry

1999 ◽  
Author(s):  
Anand K. Asundi ◽  
Jun-Wei Chen ◽  
Duo-Min He ◽  
Oi Wah Liew
Keyword(s):  
System Approach ◽  
Fiber Optic ◽  
Nutrient Deficiency ◽  
Plant Nutrient ◽  
Portable System

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

Plant Nutrient Deficiency Symptoms

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

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.

Noninvasive detection of plant nutrient stress using fiber optic spectrophotometry

2001 ◽  
Author(s):  
Jun-Wei Chen ◽  
Anand K. Asundi ◽  
Oi Wah Liew ◽  
William S. L. Boey
Keyword(s):  
Fiber Optic ◽  
Nutrient Stress ◽  
Noninvasive Detection ◽  
Plant Nutrient

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

Role of Autophagy in Plant Nutrient Deficiency

2014 ◽  
pp. 171-203
Author(s):  
Milagros Collados Rodríguez ◽  
Katarzyna Zientara-Rytter ◽  
Agnieszka Sirko
Keyword(s):  
Nutrient Deficiency ◽  
Plant Nutrient

scholarly journals Critical indexes of compositional nutrient diagnosis (CND) and its validation in wheat fields

2021 ◽  
Vol 38 (3) ◽  
pp. 480-504
Author(s):  
Adel Reyhanitabar ◽  
Nosratollah Najafi
Keyword(s):  
Wheat Yield ◽  
Nutrient Deficiency ◽  
Principal Component ◽  
Plant Nutrient ◽  
Varimax Rotation ◽  
Data Set ◽  
Cutoff Value ◽  
Nutrient Imbalance ◽  
Validation Population ◽  
Nutrient Index

Plant nutrient composition of can be used as an evaluation criterion for optimum plant growth. The objectives of present study were to (a) derive critical compositional nutrient (CND) norms for survived wheat fields and sufficiency ranges as CND nutrient index for validation samples, (b) provide a squared CND threshold nutrient imbalance index (CND r2) and compare with DRIS nutrient imbalance indices, (c) determine balanced nutrients concentration with CND indices. The yield cutoff value was 4,232 kg.ha-1. The CND indexes results indicate that Zn is the most deficient nutrient in wheat, followed by Cu, Fe, Mn and B, whereas N is the most excessive nutrient, followed by K, Ca, Mg and P. In the validation trials, the yield cutoff value were reported 5.023 kg.ha-1. The calculated CND r2 in the validation population was lower than that of the survey wheat fields, indicating a more balanced concentration of nutrients due to the application of fertilizer treatments. Significant principal component (PC) loadings were obtained after the varimax rotation. The first three PCs in high- and low-yielding subgroups and whole data set indicated 52.8, 54.6 and 48.8 % total variance, respectively. This study revealed that the decline in the wheat yield was due to the nutrient imbalance associated with multi nutrient deficiency (Zn, Cu, Fe, Mn and B) and multi nutrient excess (N, K, Ca, Mg and P).

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