Conducting research focused on plant disease detection for commonly edible plants in Ethiopia is quite essential to ensure the inclusiveness of frequently occurring plant diseases in Ethiopia and indigenous edible plants such as Teff and Enset. There are a growing number of works, but it is difficult to obtain information about what has been done so far. Thus, the objective of this review is to explore works that attempted to address plant disease detection using digital image processing for commonly edible plants in Ethiopia. To this end, thirty-seven local works published between 2018 and June, 2024 are systematically selected and assessed from different perspectives: what crops and diseases they address, where data are acquired, the approach utilized and the precision reported. The review shows that coffee and enset are the most researched crops, and enset plant has gained much attention lately while no work has been done on Teff plant disease detection. Deep learning is found to be the most applied approach, and the primary data sources for local works are local farms and research institutes located in different regions of the country. The lack of publicly accessible high-quality image datasets is discovered to be the main challenge to conduct research for local plants. The review can aid researchers in identifying less attended edible crops and diseases with regard to automatic plant disease detection as a way forward for further research.