Nutrients deficiency diagnosis of rice crop by weighted average ensemble learning

Rice is one of the most extensively cultivated food crops on the planet, especially in Bangladesh, China, and India. However, rice production is frequently hampered by nutrient imbalances. The leaves of rice plants often show signs of nutritional shortages. As a result, rice leaf color and shape can be utilized to detect nutrient deficits. Computer vision-based automatic nutrient deficiency detection by image processing has become prevalent in agriculture. In this research, we have proposed a robust Deep Ensemble Convolutional Neural Network (DECNN) model that can diagnose rice nutrient deficiency with high accuracy. Different pre-trained models named InceptionV3, InceptionResNetV2, DenseNet121, DenseNet169, and DenseNet201 are reformed by adding various layers, and their diagnostic accuracy is observed on the Kaggle dataset. Using appropriate data augmentation, a proper dense layer, a pooling layer, and a dropout layer, each of the models improves its prediction accuracy, precision, recall, and F1 score. Among the five modified pretrained models, the modified DensNet169 model provides the highest test accuracy, which has improved from 92% to 96.66%. Finally, we ensembled the modified DenseNet169, DenseNet201, and InceptionV3 models based on their performance in detecting rice nutrient deficiency diagnosis via weighted averaging. This proposed DECNN model improves testing accuracy by 98.33%.