ABSTRACT:Spectral yield models based on Vegetation Index (VI) and the mechanistic crop simulation models are being widely used for crop yield prediction. However, past experience has shown that the empirical nature of the VI based models and the intensive data requirement of the complex mechanistic models has limited their use for regional and spatial crop yield prediction especially for operational use. The present study was aimed at development of an intermediate method based on the use of remote sensing and the physiological concepts such as the photo-synthetically active solar radiation (PAR) and the fraction of PAR absorbed by the crop (fAPAR) in Monteith's radiation use efficiency based equation (Monteith, 1977) for operational wheat yield forecasting by the Department of Agriculture (DoA). Net Primary Product (NPP) has been computed using the Monteith model and stress has been applied to convert the potential NPP to actual NPP. Wheat grain yield has been computed using the actual NPP and Harvest index. Kalpana-VHRR insolation has been used for deriving the PAR. Maximum radiation use efficiency has been collected from literature and wheat crop mask was derived at MNCFC, New Delhi using RS2-AWiFS data. Water stress has been derived from the Land Surface Water Index (LSWI) which has been derived periodically from the MODIS surface reflectance data (NIR and SWIR1). Temperature stress has been derived from the interpolated daily mean temperature. Results indicated that this model underestimated the yield by 3.45 % as compared to the reported yield at state level and hence can be used to predict wheat yield at state level. This study will be able to provide the spatial wheat yield map, as well as the district-wise and state level aggregated wheat yield forecast. It is possible to operationalize this remote sensing based modified Monteith's efficiency model for future yield forecasting with around 0.15 t ha-1 RMSE at state level.
Looking at the current situation faced by farmers, there is an increase in suicide rate over the years. Main reason behind it includes poverty, unpredictable weather, financial problem, parental issue and frequent change in Indian government norms. Education also plays an important role because due to lack of education they are not aware of the soil quality and the climatic change and sometimes farmer was not aware about the feasible crops. Because of lack of knowledge they are not aware of the soil quality, soil nutrients and soil composition. This paper focused on prediction of crop based on the existing data by Artificial Neural Network. We are implementing Artificial Neural Network which is used for classification and prediction as most of intense computation takes place during training phase only, there is no requires of testing phase.
<p><strong>Abstract.</strong> Wheat (<i>Triticum aestivum</i> L.) is a major cereal crop of the world, which plays an important role in global food and nutritional security. In India, wheat grown areas are more as compared to other food crops, except for rice. The total area under wheat cultivation is 30.60 million hectares with production of 98.38 million tonnes and the productivity is 3.22 tonnes /hectare (DES, 2017). The main objective of this paper is to highlight the development of satellite-based methodology, compare the relative deviations (%) at national level, RMSE (%) and correlation coefficient at state level and correlation coefficient at district level between DES and FASAL estimates from 2013 to 2017. It was observed that the area and production estimates improved with improvement in the satellite resolution and ground truth data. During the last 10 years of estimation the spatial resolution of the satellite data has gradually improved from 23.5 meter of (Reourcesat-2, LISS-III) and finally 10&thinsp;m of Sentinel-2, MSI, which is being currently used for acreage estimation purpose. Hooda R.S et al (2006) studied that the improvement in the spatial resolution, spectral and temporal resolution of the satellite data has also improved the crop discrimination. Both accuracy as well as precision of the estimates has improved over the years from 2013 to 2017, as reflected by relative deviation, RMSE (%) and Coefficient of correlation values at national, state and district level respectively.</p>
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