## 2 Related Work  
In recent years, several studies have reviewed works related to the automatic detection, identification, and classification of diseases and pests in agricultural crops. To better understand the work that has been carried out in this area, a search was accomplished in the Scopus database. Thus, after some initial experiments and considering an initial analysis of some literature studies, five terms were identified and used in the search. First, terms related to the type of study, in this case, review. Second, the term \"automatic\". Third, terms related to pests, plagues, or diseases. Fourth, terms related to agriculture, crops, leaves, or plants were added to the search terms, as they represent the study area or plants or parts where diseases or pests are typically visible. Finally, classification, identification, or detection terms were also included. The complete string for the search is as follows:  
review AND automatic AND (pest* OR plague* OR disease*) AND (crop* OR leaf* OR plant* OR agricul*) AND (classification OR identification OR detection)  
The database search was conducted in February 2023 and has considered the field's article title, abstract, and keywords. Only studies published in 2012 or later were considered. After searching for the literature, 98 studies were obtained. These studies were evaluated in terms of title and abstract, resulting in the exclusion of 78 studies. Most of them were excluded because they were not studies presenting reviews. Among the others, their focus differed from the intended one (5) or because their full text was unavailable (3). The remaining 20 studies were underwent qualitative and quantitative analysis. Table 1 summarizes some of their characteristics, namely the year in which the article was published, the main objective of the review, the crop(s) analyzed in the review, the number of studies included in its analysis (when the analysis covers several areas, only the studies that are focused on identification or classification of diseases or pests are considered), and the time span of studies considered in the review. The interest that automatic detection, identification, and classification of diseases and pests in agricultural crops have attracted, and the growing importance it has gained in recent years, is evident. After applying the inclusion and exclusion criteria, all identified studies were published between 2020-2022, with a higher prevalence in 2022. Furthermore, more than two-thirds of the studies only analyzed articles published in the last ten years (published in 2013 or later). This highlights the interest that this study area has attracted in recent years. It also indicates that it is still a new area of research and has been the focus of more significant interest only recently. ML approaches are very popular for automatically detecting and monitoring diseases and pests. Eighty percent of the reviews specifically address the use of ML algorithms and, more specifically, deep learning algorithms. Regarding the type of crops studied, 40% of the articles describe reviews of studies that specifically addressed a single crop (e.g., tomato, rice, cotton, potato). The remaining 12 (60%) reviews analyze studies related to multiple crops.