The Digital Agricultural Revolution. Группа авторов
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1 * Corresponding author: [email protected]
2
Comparative Evaluation of Neural Networks in Crop Yield Prediction of Paddy and Sugarcane Crop
K. Krupavathi1*, M. Raghu Babu2 and A. Mani3
1Department of Irrigation and Drainage Engineering, Dr. NTR College of Agricultural Engineering, Bapatla, ANGRAU, India
2Department of Irrigation and Drainage Engineering, College of Agricultural Engineering, Madakasira, ANGRAU, India
3Department of Soil and Water Engineering, Dr. NTR College of Agricultural Engineering, Bapatla, ANGRAU, India
Abstract
Climate change causing extreme temperature events, erratic pattern of rainfall, droughts and floods poses serious limitations on agriculture, in turn requires regular crop monitoring and management of resources to get maximum yields. Food chain of the crops can be transformed by technological innovations, like mechanization, artificial intelligence and robotics, UAVs, sensors, Internet of Things (IoT), remote sensing, machine learning and deep learning in agriculture. The present study focused on ability of machine learning algorithm in integration with remote sensing in crop yield prediction of paddy and sugarcane crops at regional level. Crop-sensitive parameters extracted from high resolution LANDSAT 8 OLI imageries are