Percentage Of Study Area Research Articles

Application of Artificial Neural Network Technology for Prediction of Sunflower Harvest Losses

Introduction The current stage of economic development is characterized by digitalization. Digital technologies in crop production occupy leading positions in agrocybernetics. The digitalization of society has brought to the fore new methods of studying development processes, among which a significant role is played by deep learning and its most successful methods such as artificial neural networks. “Artificial neural networks (ANNs) have gained popularity an effective tool for offering solutions to a wide variety of different case studies of biological and agricultural background. Their effectiveness emanates from their ability to model complex relationships between observation data from sensors and predicted variables without relying on assumptions about the model structure hence they can predict the real nature of the nonlinear relation between input and output data.” Yield prediction is a major challenge in precision agriculture, closely associated to the adoption of best management practices, crop pricing and security. Various techniques and methodologies have been developed to predict crop yield in agriculture. Yield forecasting requires control of many parameters, including Moisture Content pH, Soil Organic Matter, Total Nitrogen and Organic Carbon, which complicates the forecasting process . The purpose of the paper. The purpose of this paper is to find out and substantiate the possibility of predicting the probable loss of the sunflower crop by the farmer based on the analysis of the distribution of the vegetation index in the field. Our hypothesis is that the distribution of the vegetation index significantly affects the percentage of losses, of course, with additional parameters. Results. The influence of parameters that characterize the harvest on its losses is, but a clear regression relationship can not be built. Therefore, the technology of artificial neural networks is used to build the model. The model is formed in the form of an algorithm at the input of which input parameters are given (value of vegetation index at the beginning of the study, change of index value during the study period, seed moisture in the accounting area, percentage of study area from field area), at the output we get the percentage of possible crop losses. The algorithm is automatically translated into a program in the C ++ programming language (or another programming language), which allows in practice to model the farmer's possible crop losses depending on his actions in relation to growing crops. Keywords: sunflower, machine learning, artificial neural networks, forecast model.

Heavy Mineral Concentration and its Importance along the Attrai River, Dinajpur, Bangladesh

Heavy minerals investigation along the Attrai river sand of Dinajpur district, Bangladesh consists of biotite, chlorite, epidote, garnet, hornblende, kyanite, monazite, rutile, staurolite, tourmaline and zircon from non-opaque and hematite, ilmenite, limonite and magnetite from opaque fraction where garnet and magnetite claim the highest position in non-opaque and opaque group, respectively. The heavy minerals have a wide range of economic potentialities but the observed percentage of study area is only 1.54% which does not qualify as potential with the other known potential zones in Bangladesh. The investigation shows that, major portion of the clastic concentration likely to have been derived mainly from crystalline igneous and metamorphic rocks and a considerable part might have contributed through older sedimentary province. The orientation pattern of heavy mineral deposit suggests that, the possible sources lie in the west, north-west and in the northern side of Bangladesh.