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.
D. Stephen, D. Ferlin, D. Shahila· International Conference on...· 0 citations
Images of medical patients that were taken in low light or low contrast areas frequently have issues with noise, visibility, and important information for physicians to use in deciding on patient care. An Adaptive Hybrid Convolutional Neural Network Discrete Wavelet Transform Enhancement Technique for Low Light Medical Images is presented for use in helping to detect latent disease in X-ray and Magnetic Resonance Images. The method works as follows: The input image first goes through a Discrete Wavelet Transform to break it down into high frequency and low frequency components. Separating out the high frequency (noise) and low frequency parts of the image allows for a more efficient way to reduce noise while still preserving the critical structure of the image. Once the first step has been completed, multi-scale wavelet features are used to create a Convolutional Neural Network (CNN) enhancement module that learns how to adaptively learn how to make illumination corrections and improve contrast. The final part of the process is an Adaptive Histogram Equalization postprocessing step that improves visual clarity, therefore enhancing the final image to allow for good clinical interpretation. There are experimental results that demonstrate the new proposed framework is significantly better than existing methods on several common image quality metrics such as PERMANENT CRYSTAL, PSNR, and SSIM, and that it preserves the critical diagnostic features of the medical image. This method is extremely beneficial to radiologists because it allows for the accurate and reliable analysis of MRI images using Computer Assisted Diagnosis (CAD) systems and can be integrated with existing CAD systems to enhance and improve the radiologist’s ability to interpret the medical images of their patients.
D. Ferlin, D. Shahila, D. Stephen· International Conference on...· 0 citations
