Comparative analysis of explainable deep learning models for identification of plant nutrient deficiencies

Ethics Reference no: 214011097/2022/21Essential mineral nutrients play a crucial role in the growth and survival of plants. The lack of nutrients in plants threatens global food security and affects farmers who solely depend on producing healthy crops. Traditionally the identification of nutrient deficiencies in a crop is done manually by experienced farmers. Deep learning (DL) has shown promise in image classification. However, the lack of understanding regarding the accuracy and explainability of specific DL models for the identification of plant nutrient deficiencies is a hindrance to making informed decisions about the suitability of these algorithms for practical implementation. The study used two plant datasets: rice and banana. The three DL models were a Convolutional Neural Network (CNN), and two pre-trained models: Inception-V3 and Visual Geometry Group (VGG-16). For the explainability of the models, the study used two XAI techniques: Shapley Additive exPlanations (SHAP) and Gradient-weighted Class Activation Mapping (Grad-CAM). The study found that the choice of DL models has a significant impact on the performance of nutrient deficiency identification in different plant datasets.