Advanced Generative Adversarial Network Based Image Augmentation for Agricultural Applications Using Convolutional Neural Networks

To improve the efficiency of agricultural output, vision-driven smart agriculture has arisen as a crucial strategy. Though deep learning approaches have many potential applications in agriculture, the high expense and difficulty of picture tagging in varied and unstructured field settings is a big obstacle to their widespread use. We propose an Advanced Generation Adversarial Network and a Convolutional Neural Network, also called to create synthetic agricultural photographs that seem realistic, in order to address this difficulty. Three essential elements of field scenescrops, weeds, and soilwere included into the synthetic visuals. At first, we used Excess Greens and minimal error threshold segmentation to automatically remove weed and crop sections. With the help of the CNN to direct feature learning and guarantee semantic coherence between created and actual pictures, the suggested Advanced GAN was trained using these segmented areas, and it synthesized very varied and realistic variants. The CNNAdvanced GAN framework that was suggested was thoroughly tested on three main computer vision tasks: semantic segmentation, object identification, and picture classification. ResNet, YOLOv5, and DeeplabV3 were among the benchmark models used. Model generalizability and robustness were shown to have significantly improved based on the experimental findings. Accuracy was 0.99 for the classification model, 0.98 for object identification, and 0.96 for semantic segmentation in terms of Intersection over Union. Another benefit of using high-quality synthetic data is that it helped speed up the data set preparation process by decreasing the amount of human annotation effort. In sum, the suggested approach proves that a combination of convolutional neural networks and advanced generative adversarial networks is a powerful tool for agricultural picture augmentation, resulting in enhanced deep learning capabilities in complicated field settings. By using this method, not only is manual labelling made easier, but future smart agricultural applications like automated weed identification, crop monitoring, and precision farming will also be able to scale more easily.