Rice holds a significant position in India's agricultural landscape, contributing substantially to the nation's economy, with approximately 70% dependence on agricultural products. However, the uncertainty surrounding agricultural production due to natural calamities, environmental factors, and unpredictable plant diseases poses challenges. Identifying plant diseases manually is a daunting task for farmers and crop producers. Hence, the adoption of automatic detection systems emerges as a contemporary solution. Researchers are actively developing numerous automatic plant disease detection systems. This paper presents a concise overview of diverse image processing and machine learning techniques applied to identify diseases in rice leaves and seedlings. The survey encompasses various attributes, including segmentation types, segmentation techniques, extracted features, dataset sizes, author details, publication years, disease categories, techniques utilized, detection/classification accuracy, and future prospects/limitations. After reviewing several research papers, we provide a succinct summary of recent image processing and machine learning approaches employed by researchers in the detection and classification of diseases affecting rice leaves and seedlings.