ABSTRACT
An ecosystem that is balanced depends heavily on the agriculture sector. Growing and producing plants and food becomes crucial as a result. The production of plants and crops requires careful attention from farmers. Today, a number of ailments affecting plants have been discovered. Plant disease outbreaks may have a significant impact on crop production, which would slow the nation’s economic growth rate. Early detection and treatment are possible for the plant disease. The early identification and classification of diseases may be greatly aided by machine learning (ML), computer vision-based techniques and deep learning (DL). Many researchers have successfully developed various plant disease recognition algorithms and models in the past. A few machine learning-based approaches and techniques that are currently in use to detect diseases in various plant species were looked at and assessed in this study. Additionally, the challenges and limitations of various strategies were analysed and compared based on the success rate of each.
