<div style="text-align: justify;">Soil formation is a long term process and diverse soils are formed in different localities due to various soil forming factors over the landscape. Soil classification plays critical role in various aspects of agricultural engineering. Physico-chemical parameters play an important role in soil classification. In this paper, we present a comprehensive classification model for soil texture classification by using Linear Discriminant Analysis (LDA). We took the Physico-chemical properties of the soil, which include soil moisture, temperature, electrical conductivity, pH, organic carbon, available nitrogen, available phosphorus and potassium as independent variables, while the soil type was taken as the dependent variable. Feature selection is employed using Boruta algorithm. The performance of the proposed classification model is evaluated and expressed in terms of overall accuracy and kappa coefficient. Results show that the average prediction accuracy and kappa coefficient of the proposed model are 96.3% and 0.944 respectively, indicating that the model can be used effectively for soil classification for a set of suitable dependent variables.</div>

Machine learning model for automation of soil texture classification

<div style="text-align: justify;">The effects of genotype and harvest date on lupin (Lupinus angustifolius) forage yield and quality were evaluated in Zapopan Jalisco, Mexico. Cultivars Haags Blaue, Boregine, Borlu, Probor, Sonate, and Boruta were grown during the 2013&ndash;2014 cool season and harvested twice, in January (early harvest) and February (late harvest). Fresh and dry matter (FM, DM) yield, crude protein(CP), acid detergent fiber (ADF), neutral detergent fiber (NDF), minerals, and total alkaloids were measured at each harvest. FM yields varied from 9.1 to 12.2 t ha-1 and from 23.4 t ha-1 to 35.3 t ha-1 at the first and late harvests, respectively. DM yields from early harvest plants varied from 1.2 to 1.7 t ha-1; those from the second harvest (late harvest date) varied from 3.4 to 6.8 t ha-1. The highest CP content for both (early and late harvests) was identified in the Probor cultivar, at 226 and 187 g/kg, respectively. At early harvest, ADF values varied from 291.2 in Sonate to 392.3 g/kg in Borlu; at late harvest, ADF values varied from 417.2 in Sonate to 488.3 g/kg in Borlu. Harvest date and varieties did not influence alkaloid and mineral content.&nbsp;</div>

Preliminary estimation of forage yield and feeding value of <em>Lupinus angustifolius</em> varieties cultivated in Jalisco, M&eacute;xico, during the cool season<br />&nbsp;

Machine learning model for automation of soil texture classification

Preliminary estimation of forage yield and feeding value of Lupinus angustifolius varieties cultivated in Jalisco, México, during the cool season