{'fraction_non_alphanumeric': 0.013713080168776372, 'fraction_numerical': 0.0, 'mean_word_length': 5.243421052631579, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
A Systematic Literature Survey on Generative Adversarial Network Based Crop Disease Identification

"However, a deep learning network requires a large amount of data, and because certain plant lesion data is difficult to acquire and has a similar structure, deep learning has lately showed potential in the identification of plant lesions.", The data set has to be increased by generating full plant lesion leaf pictures. To address this issue, this article offers a survey on technique for generating full and rare picture of plant lesion leaf that may be enhance the accuracy of classification network. Some of the benefits of our research in this article are a systematic survey on GAN based plant disease identification where many authors gave the theory and practical implementation on that. My approach has been shown to successfully extend plant lesion research and improve the classification network’s identification accuracy in the future.