Ensemble Averaging of Transfer Learning Models for Identification of Nutritional Deficiency in Rice Plant

• Published in: Electronics (Basel) 2022-01, Vol.11 (1), p.148
• Main Authors: Sharma, Mayuri, Nath, Keshab, Sharma, Rupam Kumar, Kumar, Chandan Jyoti, Chaudhary, Ankit
• Format: Article
• Language: English
• Subjects: Accuracy; Agricultural production; Agriculture; Artificial intelligence; Artificial neural networks; Availability; Cell division; Classification; Computer vision; Consumption; Corn; Crop diseases; Crops; Datasets; Deep learning; Discriminant analysis; Embedded systems; Identification; Internet of Things; Iron; Machine learning; Molybdenum; Neural networks; Nitrogen; Nutrients; Performance prediction; Potassium; Prediction models; Rice; Unmanned aerial vehicles
• ISSN: 2079-9292; 2079-9292
• DOI: 10.3390/electronics11010148

Computer vision-based automation has become popular in detecting and monitoring plants’ nutrient deficiencies in recent times. The predictive model developed by various researchers were so designed that it can be used in an embedded system, keeping in mind the availability of computational resources. Nevertheless, the enormous popularity of smart phone technology has opened the door of opportunity to common farmers to have access to high computing resources. To facilitate smart phone users, this study proposes a framework of hosting high end systems in the cloud where processing can be done, and farmers can interact with the cloud-based system. With the availability of high computational power, many studies have been focused on applying convolutional Neural Networks-based Deep Learning (CNN-based DL) architectures, including Transfer learning (TL) models on agricultural research. Ensembling of various TL architectures has the potential to improve the performance of predictive models by a great extent. In this work, six TL architectures viz. InceptionV3, ResNet152V2, Xception, DenseNet201, InceptionResNetV2, and VGG19 are considered, and their various ensemble models are used to carry out the task of deficiency diagnosis in rice plants. Two publicly available datasets from Mendeley and Kaggle are used in this study. The ensemble-based architecture enhanced the highest classification accuracy to 100% from 99.17% in the Mendeley dataset, while for the Kaggle dataset; it was enhanced to 92% from 90%.