PREVENTION OF SOIL SURFACE HUMIDITY IN MODERATELY VEGETATED FIELDS USING A MACHINE LEARNING BASED REGRESSION MODEL
Author(s):
DR.L. SRIDHARA RAO, J.Akhil, CH. KEERTHI, N. SAGAR
Keywords:
Salinity, SVM ,Logistic regression, Bayesian Network, Random Forest.
Abstract
The soil surface humidity parameter over vegetated fields is of great importance for controlling water consumption; prevention of salinity caused by over-irrigation; efficient use of irrigation system and improving the yield and quality of the cultivated crop. However, determination of the soil surface humidity is very difficult on vegetated fields. In order to overcome this problem, polarimetric decomposition models and machine learning based regression model were implemented. The main purpose of this study is to predict soil surface humidity on moderately vegetated fields. The scenario mainly concentrates on weather forecasting, crop yield prediction and crop forecasting. These factors help the farmers to cultivate the best food crops and raise the right animals with accordance to environmental components. Also, the farmers can adapt to climate changes to some degree by shifting planting dates, choosing varieties with different growth duration, or changing crop rotations. For experimental analysis, the statistical numeric data related to agriculture is undertaken. Whereas, the clustering-based techniques and supervised algorithms are utilized for managing the collected statical data. Additionally, the suitable classification methods like Support Vector Machine(SVM),neural networks are employed for better classification outcome.
Article Details
Unique Paper ID: 155074

Publication Volume & Issue: Volume 8, Issue 12

Page(s): 1334 - 1336
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