## 1 Introduction  
Plant diseases and pests are considered one of the main factors influencing food production and responsible for significantly reducing crops' physical or economic productivity. In order to maintain control of production losses and maintain crop sustainability, some measures must be carried out properly, such as a constant monitoring of the crop, combined with the rapid and accurate diagnosis of the associated diseases, pests, or anomalies. These practices are usually recommended by specialists in plant pathology [1]. Farmers are aware of these challenges, and the role technology can play in addressing these threats in agriculture to increase agricultural productivity and operating profits. Technological progress has enabled the use of techniques and methods capable of optimizing agricultural returns [2], preserving natural resources [3], reducing unnecessary use of fertilizers [4], and identifying diseases in crops from remote sensing images [5]. Automatic detection, identification, and classification of diseases and pests in crops have attracted considerable attention from researchers. Currently, numerous studies propose distinct methods to approach this problem. This growing interest can be seen in the results obtained in some databases of scientific articles. As of March 2023, there have been 605 articles retrieved from Scopus when the query \"automatic plant diseases detection\" is searched. With the same query, 341 articles are retrieved from the Web of Science database. It is also verified that the results have increased exponentially in recent years. Additionally, the results of many of these studies show the potential of this kind of solution in automatically detecting and classifying diseases and pests in crops and their potential applicability in solutions to countless problems. It is, therefore, essential to study the different approaches proposed to characterize the algorithms and models used and the results obtained.  
This review aims to understand the automated processes in detecting and classifying diseases and pests in agricultural crops. Information regarding the type of algorithms, models, and their characteristics is crucial to understand the efficiency and applicability of the various approaches. More specifically, the data gathered in this review are used to answer the following research questions:  
RQ1--Which crops are most focused on studies of automatic detection of diseases and pests?  
RQ2--Do the studies address more diseases or pests?  
RQ3--What inputs were most used in algorithms to detect and classify diseases and pests?  
RQ4--What are the most used datasets for these studies?  
RQ5--What are the most used algorithms and models?  
RQ6--What levels of accuracy have been achieved in the automatic identification and classification of diseases and pests in agricultural crops?  
RQ7--What trends have been most noticed in this area of study?  
The answers to these questions will help characterize the state-of-the-art and the main trends in this study area.  
The remainder of this article is organized as follows. Section 2 details the comparison with previous reviews. Section 3 describes and applies the methodology to perform this review. The data extraction and analysis are presented in Section 4 and discussed in Section 5. Section 6 describes the strengths and limitations of this review, and finally, Section 7 presents some conclusions.