Effect of water on Neural Network based soil image Recognizer and Classifier
From the last few years, more attention has been directed towards the usage of information technology in agricultlire. This new way of farming offers tlie promise of improving farm profitability. Using Internet the farmers can collect data like geographical- referred yield, weather, soil and other important data related to farming. The abn is to use these data to produce area-specific crop production decisions. For increasing the production quality of crop soil plays von- important role. To help the farmer in deciding how to increase the crop quality based on soil. We have proposed, soil image recognizer and classifier, which classifies soil image samples based on their color and morphological features. Different t>pes of soil image samples considered like red soil, black soil. bla/:k cotton soil. Using color and morphological features a Neural Network Based Classifier Is designed. The effect of water on the soil image classifier is analyzed by adding the water into different portions of soil samples. The accuracy of the soil image classifier is improved by considering wet soil samples.
Classifier ANN BPNN Soil image
Nagaraj V. Dharwadkar D. G. Savakar S. S. Panchal A. A. Javaji S. R. Rathod
Deprtment. of Computer Science and Engineering Deprtmeut of Information Science and Engineering B.L.D.E.As. College of Engg. & Tech.. Bijapur. Karn
国际会议
Third International Conference on Digital Image Processing(ICDIP 2011)(第三届数字图像处理国际会议)
成都
英文
440-445
2011-04-15（万方平台首次上网日期，不代表论文的发表时间）