Multi-Modal Soil Health and Crop Productivity Prediction using Hybrid Deep Learning

Abstract

The current study describes an advanced hybrid multi-modal approach that will be used in predicting soil health and crop productivity through the application of several types of deep learning models and machine learning approaches to enhance predictions. In the current work, a hybrid model is considered, and three kinds of pre-trained CNN, known as ResNet50, VGG16, and Mobile Net, are used to extract the features of soil. The pre-fetched features are subsequently processed through multiple fully connected layers with SoftMax activation functions for multi-class types of soil classification, namely clay, sandy, and loam. To enhance the generalization and robustness of the models, data augmentation and normalization techniques are applied to the data. The models are optimized with Adam and SGD optimizers using categorical cross-entropy parameters for the loss function. The results of the experimentation show high accuracy and robust performance for the precision-recall and F1-score metrics. The model would highly benefit precision agriculture applications to facilitate autonomous soil type detection, and to enable farmers and growers to better inform crop selection and nutrient management for sustainable yield optimization models in smart farming.