EHFT: An Ensembled Hyperopt Fine-Tuned Neural Network for Disease Detection in Tomato Plants

Abstract

The identification of unhealthy plants in the crops at the early stage of cultivation helps for good farming. Unhealthy parts can be recognized using shape, color and texture, which are processed using feature extraction techniques. The feature extraction system stores the images in the matrix pixel format, which requires 3 channels for processing the images. Traditional neural networks utilize backpropagation techniques to adjust the random weights, which requires many resources while extracting a more significant number of features from a huge amount of data. These mechanisms also require more trainable parameters during the transformation of data from one layer to another. The proposed model implements the pre-trained model "RESNET152" (Residual Network), which is efficient for feature extraction and designs the last layer of the network as a "Tuned X-Gradient Boosting" ensemble algorithm for performing the binary classification of tomato leaves. RESNET can reduce computational resources because it implements residual blocks which fasten the learning rate by skipping a few connections in the network. The fine-tuned ensemble model helps the model identify the best parameters quickly. The learnable parameters are the essential elements of any ML model because they can easily identify the patterns associated with the different features. In the proposed model for feature extraction, pattern matching is the crucial step. Therefore, it is very necessary to tune the   XGBOOST algorithm. Compared to the traditional approaches, the proposed model enhanced the accuracy performance in training and testing with 98.58% and 95.56%, correspondingly