A significant majority of the population in India makes their living through agriculture. Different illnesses that develop due to changing weather patterns and are caused by pathogenic organisms impact the yields of diverse plant species. The present article analyzed some of the existing techniques in terms of data sources, pre-processing techniques, feature extraction techniques, data augmentation techniques, models utilized for detecting and classifying diseases that affect the plant, how the quality of images was enhanced, how overfitting of the model was reduced, and accuracy. The research papers for this study were selected using various keywords from peer-reviewed publications from various databases published between 2010 and 2022. A total of 182 papers were identified and reviewed for their direct relevance to plant disease detection and classification, of which 75 papers were selected for this review after exclusion based on the title, abstract, conclusion, and full text. Researchers will find this work to be a useful resource in recognizing the potential of various existing techniques through data-driven approaches while identifying plant diseases by enhancing system performance and accuracy.

This Autism Disorder is a developmental impairmentthat affects how a person perceives, communicates, and behaves.It is brought on by changes in the brain. Before the age of three,ASD develops, and it can persist up to death. The self-harm attempts made by those who have this disorder are significantly higher than people without have it. For diagnosing this disorder at an early stage, early detection is necessary. Various machine learning (ML) techniques namely- Random Forest, SVM, NaiveBayes, Decision Tree, etc., and deep learning (DL) approaches such as VGG16, DenseNet, AlexNet, etc., can be utilized on the questionnaires filled during the conducted survey based on behavior whereas, images can also be provided as input to these approaches for identifying of ASD. The proposed methodology suggests the detection of autism using deep learning algorithms based on transfer learning. ASD identification model consists of various stages namely- data collection/acquisition, pre- processing, data augmentation, feature extraction, and the last stage is classification.

Many people in India rely on agriculture to support their livelihoods. The volume and quality of agricultural commodities are threatened by diseases. Plant disease identification is one of the big concerns for farmers today. By combining diverse Machine Learning, Deep Learning, and Image Processing techniques, numerous researchers have created models for the early identification of disease in plants. An overview of many existing models is provided in this study. Using various keywords, the research papers for this study were chosen from peer-reviewed publications in multiple databases. In order to identify various plant diseases by enhancing system efficiency and precision, researchers will find this work to be a beneficial resource in recognizing the potential of many existing methodologies using data-driven approaches.