Prediction of the Effect of Nutrients on Plant Parameters of Rice by Artificial Neural Network
Tanmoy Shankar,
Ganesh Chandra Malik,
Mahua Banerjee,
Sudarshan Dutta,
Subhashisa Praharaj,
Sagar Lalichetti,
Sahasransu Mohanty,
Dipankar Bhattacharyay,
Sagar Maitra,
Ahmed Gaber,
Ashok K. Das,
Ayushi Sharma,
Akbar Hossain
Affiliations
Tanmoy Shankar
Department of Agronomy, Centurion University of Technology and Management, Odisha 761211, India
Ganesh Chandra Malik
Department of Agronomy, PalliSikshaBhavana, Visva-Bharati, Sriniketan 731204, India
Mahua Banerjee
Department of Agronomy, PalliSikshaBhavana, Visva-Bharati, Sriniketan 731204, India
Sudarshan Dutta
International Plant Nutrition Institute, South Asia (East India and Bangladesh) Program, India and African Plant Nutrition Institute, Benguerir 43150, Morocco
Subhashisa Praharaj
Department of Agronomy, Centurion University of Technology and Management, Odisha 761211, India
Sagar Lalichetti
Department of Agronomy, Centurion University of Technology and Management, Odisha 761211, India
Sahasransu Mohanty
Department of Physics, Centurion University of Technology and Management, Odisha 761211, India
Dipankar Bhattacharyay
Department of Agronomy, Centurion University of Technology and Management, Odisha 761211, India
Sagar Maitra
Department of Agronomy, Centurion University of Technology and Management, Odisha 761211, India
Ahmed Gaber
Department of Biology, College of Science, Taif University, P.O. Box 11099, Taif 21944, Saudi Arabia
Ashok K. Das
Department of Smart Agriculture, Sunmoksha Power Private Limited, Bengaluru 560076, India
Ayushi Sharma
Department of Smart Agriculture, Sunmoksha Power Private Limited, Bengaluru 560076, India
Akbar Hossain
Division of Agronomy, Bangladesh Wheat and Maize Research Institute, Dinajpur 5200, Bangladesh
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 understand the role of individual nutrients (N, P, K, Zn, and S) on different plant parameters (plant height, tiller number, dry matter production, leaf area index, grain yield, and straw yield) of rice. A feed-forward neural network with back-propagation training was developed using the neural network (nnet) toolbox available in Matlab. For the training of the model, data obtained from two consecutive crop seasons over two years (a total of four crops of rice) were used. Nutrients interact with each other, and the resulting effect is an outcome of such interaction; hence, understanding the role of individual nutrients under field conditions becomes difficult. In the present study, an attempt was made to understand the role of individual nutrients in achieving crop growth and yield using an artificial neural network-based prediction model. The model predicts that growth parameters such as plant height, tiller number, and leaf area index often achieve their maximum performance at below the maximum applied dose, while the maximum yield in most cases is achieved at 100% N, P, K, Zn, and S dose. In addition, the present study attempted to understand the impact of individual nutrients on both plant growth and yield in order to optimize nutrient recommendation and nutrient management, thereby minimizing environmental pollution and wastage of nutrients.