Assessing four different macroscopic water uptake models for maize plant ( ZEA mays L.) under salinity stress

The Species Composition and Seasonal Dynamics of Thrips (Thysanoptera) Populations on Maize (Zea MaysL.) in Southeastern PolandThrips species composition and seasonal abundance was studied on maize crops during two seasons (2006-2007), in southeastern Poland. Altogether 21 species have been identified, among themFrankliniella tenuicornis(Uzel 1895) andHaplothrips aculeatus(Fabricius 1803) which are a graminicolous species and were eudominants, comprising 96.8% in 2006 and 82.0% in 2007 of all collected specimens. Other species occurred only in low numbers. The frequent and numerous presence ofF. tenuicornisspecies in their immature stages in the samples, confirmed the role of the maize plant as the host.H. aculeatusprobably chose maize as a food source and substitute plant for breeding.

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Exogenous ascorbic acid improved tolerance in maize (Zea mays L.) by increasing antioxidant activity under salinity stress

African Journal of Agricultural Research ◽

10.5897/ajar2017.12295 ◽

2017 ◽

Vol 12 (17) ◽

pp. 1437-1446 ◽

Cited By ~ 8

Author(s):

Billah M. ◽

M. Rohman M. ◽

Hossain N. ◽

Shalim Uddin M.

Keyword(s):

Antioxidant Activity ◽

Zea Mays ◽

Ascorbic Acid ◽

Salinity Stress ◽

Zea Mays L

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Chemical residuality in maize (Zea mays L.) fields irrigated with deep well water

Ecosistemas y Recursos Agropecuarios ◽

10.19136/era.a5n13.1311 ◽

2018 ◽

Vol 5 (13) ◽

pp. 111

Author(s):

Ma. Elizabeth Azpilcueta-Pérez ◽

Aurelio Pedroza Sandoval ◽

Ricardo Trejo-Calzada ◽

Ignacio Sanchez-Cohen ◽

María Del Rosario Jacobo-Salcedo

Keyword(s):

Heavy Metals ◽

Zea Mays ◽

Zea Mays L ◽

Residual Analysis ◽

Maize Plant ◽

Well Water ◽

Deep Well ◽

Fodder Production ◽

Heavy Metals And Arsenic

The aim was to conduct a residual analysis of the main cationic elements, heavy metals and arsenic in irrigated maize fodder production. Four soil and maize plant samplings were conducted in eight sites in April, May, June and July, 2014. Ca, Na, As, and Pb concentrations were higher in the soil. The As concentration was higher in June and July. La Purísima had a higher As concentration, while Bermejillo, La Galicia and La Rosita had a higher Ca concentration. K, Ca, Pb and Zn had higher concentrations in the maize plant, with Ca, Na and K having highervalues in July and Mg, Pb and Zn being higher in May and July. The content of Ca, Mg, Na and K did not dier among regions; arsenic was higher in Leon Guzmán and La Rosita.

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Maize Plant Desease Identification (Zea Mays L. Saccharata) Using Image Processing and K-Nearest Neighbor (K-Nn)

International Journal of Engineering & Technology ◽

10.14419/ijet.v7i3.20.20581 ◽

2018 ◽

Vol 7 (3.20) ◽

pp. 402

Author(s):

Prihastuti Harsan ◽

Arie Qurania ◽

Karina Damayanti

Keyword(s):

Image Processing ◽

Zea Mays ◽

Digital Image Processing ◽

Digital Image ◽

Plant Disease ◽

Zea Mays L ◽

Maize Plant ◽

Computer Algorithms ◽

K Nearest Neighbor ◽

Plant Pests

Plant pests of maize are known to attack in all phases of corn plant growth (Zea mays L. saccharata), both vegetative and generative. Common pests found in maize are seed flies (Atherigona sp.), Stem borers (Ostrinia furnacalis), Boricoverpa armigera, leaf-eaters (Spodoptera litura). The process of identification of maize plant disease is done through laboratory analysis and direct observation. The time required to obtain the identification result is 4 (four) months. Plant pests will attack some parts of the plant, including leaves, stems and fruit. Early detection is usually done through leaves. Plant pests will attack the plant leaf area with certain characteristics. Digital image processing is the use of computer algorithms to perform image processing on digital images. Identification of maize plant disease can apply image processing techniques through the characteristics or symptoms of disease raised on the leaves. Characteristic of attacks by pests in maize plants can be detected through the colors and patterns that appear on the leaves. This research performs implementation of digital image processing method to identify disease in maize plant caused by pest. The disease is Hawar Leaf, Bulai (Downy Midew), Hama Grasshopper, Leaf Spot (Sourthern Leaf Blight). Through color and edge detection, the accuracy obtained is 91.7%.

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