Prediction of the Effect of Nutrients on Plant Parameters of Rice by Artificial Neural Network

• Published in: Agronomy (Basel) Vol. 12; no. 9; p. 2123
• Main Authors: Shankar, Tanmoy, Malik, Ganesh Chandra, Banerjee, Mahua, Dutta, Sudarshan, Praharaj, Subhashisa, Lalichetti, Sagar, Mohanty, Sahasransu, Bhattacharyay, Dipankar, Maitra, Sagar, Gaber, Ahmed, Das, Ashok K., Sharma, Ayushi, Hossain, Akbar
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.09.2022
• Subjects: Agricultural production; agronomy; Amino acids; Analysis; artificial neural network; Artificial neural networks; Back propagation networks; Cereal crops; Crop growth; crop performance; Crop yield; Crop yields; dietary recommendations; digital agriculture; Dry matter; dry matter accumulation; Environmental management; Ethylenediaminetetraacetic acid; Experiments; Fertilizers; food security; grain yield; Leaf area; Leaf area index; Leaves; Loam soils; Mathematical models; Metabolism; Neural networks; Nitrogen; nutrient management; Nutrients; Parameters; Phosphorus; Plant growth; plant height; plant nutrients; Plants; Plants (botany); pollution; Potassium; prediction; Prediction models; Productivity; Propagation; Rice; Soil fertility; straw; Sulfur; Training; India

Rice holds key importance in food and nutritional security across the globe. Nutrient management involving rice has been a matter of interest for a long time owing to the unique production environment of rice. In this research, an artificial neural network-based prediction model was developed to und...