scholarly journals Fuzzy Logic Tool to Forecast Soil Fertility in Nigeria

2018 ◽  
Vol 2018 ◽  
pp. 1-7 ◽  
Author(s):  
G. O. Ogunleye ◽  
S. G. Fashoto ◽  
Petros Mashwama ◽  
S. A. Arekete ◽  
O. M. Olaniyan ◽  
...  
Keyword(s):  
Fuzzy Logic ◽  
Plant Growth ◽  
Soil Fertility ◽  
Soft Computing ◽  
Soil Test ◽  
Soft Computing Method ◽  
Phosphorus And Potassium ◽  
Computing Method ◽  
Nitrogen Phosphorus ◽  
Effective Growth

The soil is composed of several nutrients which are important for the effective growth of plants. Nitrogen, phosphorus, and potassium are micronutrients which are very important for plant growth. There have been several methods and soil tests developed to test the compositions of these nutrients in the soil. Interpreting the results gotten from such tests has been a herculean task for farmers. Employing the use of a soft computing method to interpret such result would be a noble idea. In this paper, we describe the use of fuzzy logic to interpret the values of nitrogen, phosphorus, and potassium (NPK) gotten from conventional soil test to know their levels in the soil and predict possible NPK inputs.

scholarly journals Fertilization With Nitrogen, Phosphorus and Potassium on Soil Fertility and Nutritional Status of Yellow Passion Fruit Plants

2019 ◽  
Vol 11 (5) ◽  
pp. 142
Author(s):  
Rodrigo T. M. Miyake ◽  
William H. S. Takata ◽  
Nobuyoshi Narita ◽  
José E. Creste
Keyword(s):  
Nutritional Status ◽  
Soil Fertility ◽  
Nitrogen Fertilization ◽  
Passion Fruit ◽  
Triple Superphosphate ◽  
Fruit Plants ◽  
Phosphorus And Potassium ◽  
Maximum Content ◽  
Nitrogen Phosphorus ◽  
Yellow Passion Fruit

The research had as objective to study the influence of the doses of nitrogen, phosphorus and potassium on soil fertility and nutritional status of yellow passion fruit plants. The experimental design was in randomized blocks in an incomplete fractioned factorial 1/2 (4 × 4 × 4), with four doses of N (150, 300, 600 and 1200 Kg-1 ha-1 yr-1 N); four doses of P2O5 (200, 400, 800 and 1600 kg-1 ha-1 yr-1) and four doses of K2O (100, 300, 500 and 700 kg-1 ha-1 yr-1). The fertilizers used were the ammonium nitrate = 32% N; triple superphosphate = 44% P2O5 and potassium chloride = 60% K2O. In the fertility of the nitrogen fertilization increased the content of phosphorus and sulfur. The doses of P2O5 increased the concentration of P in the soil. The doses of K2O influenced the increase in the content of K in the soil in the harvest 2013/2014. There was an interaction of the doses of N and K2O in the availability of K in the soil in the harvest 2012/13 with the maximum levels of K, in the doses of 200-400 kg N ha-1 and 600 to 700 kg ha-1 of K2O. In the nutritional state the yellow passion fruit plants was influenced by the doses of N for the sulfur content of the leaves. The maximum content of S leaf concentration of 3.63 g kg-1 was obtained in the dose estimated at 1120 kg ha-1 of P2O5.

scholarly journals Article Commentary: Rough Set Soft Computing Cancer Classification and Network: One Stone, Two Birds

2010 ◽  
Vol 9 ◽  
pp. CIN.S4874 ◽  
Author(s):  
Yue Zhang
Keyword(s):  
Soft Computing ◽  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Cancer Classification ◽  
Soft Computing Method ◽  
Direct Gene ◽  
Gene Regulatory ◽  
Variable Precision Rough Sets ◽  
Computing Method ◽  
Selection Of

Gene expression profiling provides tremendous information to help unravel the complexity of cancer. The selection of the most informative genes from huge noise for cancer classification has taken centre stage, along with predicting the function of such identified genes and the construction of direct gene regulatory networks at different system levels with a tuneable parameter. A new study by Wang and Gotoh described a novel Variable Precision Rough Sets-rooted robust soft computing method to successfully address these problems and has yielded some new insights. The significance of this progress and its perspectives will be discussed in this article.

scholarly journals A soft computing method for detecting lifetime building thermal insulation failures

2010 ◽  
Vol 17 (2) ◽  
pp. 103-115 ◽  
Author(s):  
Javier Sedano ◽  
Leticia Curiel ◽  
Emilio Corchado ◽  
Enrique de la Cal ◽  
José R. Villar
Keyword(s):  
Soft Computing ◽  
Thermal Insulation ◽  
Soft Computing Method ◽  
Computing Method
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