Early detection of plant diseases is critical in the agricultural sector to reduce
the loss caused by the diseases. Paddy crop diseases impact yields in various ways,
given that the crop is cultivated globally. The specific disease manifestations and
severity, however, often vary with climatic and geographical conditions. The method
used to treat the disease in one area may not be suitable in other areas. The manual
detection of diseases by farmers is a very costly, time-consuming, and tedious process.
To avoid these issues, researchers have developed various automatic methods. Most
models utilize image processing and machine learning, or deep learning algorithms, to
achieve the designated output. This study analyzed various algorithms used to automate
the plant disease detection process, examining their advantages and disadvantages.
Keywords: Paddy crop diseases, Arrange alphabetically, Machine learning, Deep learning, Image processing.
