Volume XLII-5
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-5, 659-664, 2018
https://doi.org/10.5194/isprs-archives-XLII-5-659-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-5, 659-664, 2018
https://doi.org/10.5194/isprs-archives-XLII-5-659-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

  19 Nov 2018

19 Nov 2018

IDENTIFICATION OF FOOD INSECURE ZONES USING REMOTE SENSING AND ARTIFICIAL INTELLIGENCE TECHNIQUES

K. Nivedita Priyadarshini1, M. Kumar2, and K. Kumaraswamy1 K. Nivedita Priyadarshini et al.
  • 1Dept. of Geography, Bharathidasan University, Tamil Nadu, India
  • 2Indian Institute of Remote Sensing (ISRO), Uttarakhand, India

Keywords: food insecure zones, food insecurity, artificial intelligence, neural network, hunger index score, risk metric

Abstract. The basic human need is to ensure adequate access to food without any combat, loss of productivity and cognitive impairment in the supply chain. When an individual is limited to proper procurement of food through various determinants there stems sustained hunger which is termed as ‘food insecurity’. The study portrays to identify the food insecure zones using indicators which are implemented methodically through remote sensing and artificial intelligence techniques. Madhya Pradesh being a semi arid region faces reduction in the agro ecosystem due to the climatic changes and rainfall impacts which are the key trends for demand of food and production thus resulting in risk of malnutrition and hunger. Tackling food shortage requires addressing both environmental and socio-demographic factors in order to minimize food insecurity. The spatial variation of rainfall over years along with significant land degradation affects the common cultivation pattern among the households. In this study, a neural network approach is employed to identify the zones that ensure less access to food using indicators which mainly focuses on child population below five years, hunger index measuring parameters like child stunted, child wasted, children undernourished, child mortality below five years along with supporting environmental factors such as land use/land cover, NDVI and rainfall prevailing in the study area. The result shows a bleak statistics of villages representing the hunger index score that are categorized into low, serious, alarming and extremely alarming estimating a count of 70, 73, 23 and 7 villages respectively in the entire study area.