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
• ISSN: 2073-4395; 2073-4395
• DOI: 10.3390/agronomy12092123

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.