To manage root knot nematodes, various innovative agricultural practices was been adopted. Many of these strategies leverage vision-based artificial intelligence (AI), machine learning (ML), and deep learning (DL) techniques to enhance disease detection. This study conducts a systematic literature review (SLR) and provides a comprehensive survey of existing research that employs data collection methods and publicly accessible datasets. The review highlights the use of vision centered techniques and hyperspectral imaging in assessing various crops, including tomatoes, guava, pomegranate, potatoes, soybeans, and maize. Additionally, while image classification is a key focus, pinpointing the exact location of diseases remains a significant challenge. Some laboratory datasets are relatively small, complicating their application in experiments. To develop robust models, it is essential to create those with fewer parameters that can operate on small devices and handle large datasets encompassing diverse crops and diseases. From all these review process, we will be interested to study of artificial intelligence based as well as other currently used identification methodologies, technologies used for plant parasite nematode specifically root knot nematode, we study that it is the relevance in context of India.