Kaur, Ramanjit, Mahey RK, Mahal JS, Kingra PK and Kaur Pukhraj

Department of Agronomy, Punjab Agricultural University, Ludhiana,141 0014, Punjab

Email: ramaan180103@yahoo.com

The study entitled, Spectral characterization to discriminate grass weeds from wheat crop using remote sensing and GIS” was carried out to find out the optimum time span to distinguishing major grass weeds i.e. Phalaris minor and Avena ludoviciana from wheat crop based on their spectral characteritics. The study was conducted at Student’s Research Farm, Department of Agronomy during 2006-07 and 2007-08. The experimental sites during both the seasons were sandy loam in texture, with normal soil reaction and electrical conductivity, low in organic carbon and available nitrogen and medium in available phosphorus and potassium. The investigation consisted of two experiments each having twelve treatments with different population levels of Phalaris minor in Experiment I and Avena ludoviciana in experiment II viz 0, 10, 15, 25, 50, 75, 100, 125, 150, 200, 250 plants m^-2 and a pure weed plot. In addition to the various agronomic parameters, the remote sensing parameters such as Red reflectance (%), Infrared reflectance (%), Radiance ratio (RR) and NDVI were recorded periodically during the crop growing season for both the years. The results showed that the highest RR and NDVI values were recorded under pure wheat treatment (T₀) and minimum under pure weed plots (T_max). It was observed that by using remote sensing indices like RR and NDVI, pure wheat can be distinguished from pure populations of P. minor and Avena ludoviciana just after 34 DAS and various levels of weed populations can be discriminated amongst themselves from 68 DAS upto 107 DAS. From Such type of study it is concluded that we can discriminate the grass weeds from Wheat crop based on their spectral characterization and in future, weed prescription mapping can be used for forecasting the weed infestations in crops, on the basis of which farmers can be advised to take the preventive control measures to manage the weeds precisely/need based without going for blanket spraying of herbicides and weed maps can be used in yield forecasting models.       

Key Words: Multispectral, Wheat, Little seed canary grass precion weed management,  

                     remote sensing spectral characteristics