Accurate Phase Wise Prediction of Coconut Harvesting Stages Using VGG19 Convolutional Neural Network for Improved Agricultural Automation

The paper aims to develop an efficient deep learning algorithm to classify the coconut harvesting level stages by using the various images of the coconuts. The system uses the images of the various types of coconuts to find, evaluate, and harvest coconuts at the best phases of maturity i.e. tender, snowball, or mature, based on the domain of applications. Using characteristics like color, size, and texture, from the images taken from the existing images of coconuts the proposed Deep Learning model identifies and categorize coconuts, assuring accurate harvesting for products like oil, copra, and coconut water. The proposed methodology using VGG19 algorithm demonstrates the high accuracy to automatically predict the various harvesting stages like tender, snowball and copra using a various feature s from the coconut images captured. The proposed deep learning model was trained on the various existing coconut image datasets using data augmentation to enhance the VGG19 based CNN-model. The proposed model performance is valuated using metrics like precision, recall, specificity and F1-score. Results reveal that the image augmentation, the transformation methods with the VGG19 model attained the testing accuracy 98.94% and validation accuracy of 97.68%. Our research contributes best on the classification of various harvesting stages that will benefit and help the farmers job of various harvesting stages while promoting the economic growth.