## 2 Related Work
\begin{table}
\begin{tabular}{l l l l l} \hline
**Review** & **Year** & **Focus** & **Crop** & **Studies** & **Timespan** \\ \hline
[6] & 2022 & Artificial Intelligence-based disease diagnosis and categorization techniques for paddy crops. & Rice & 20 & 2016–2021 \\
[7] & 2022 & Rice leaf diseases based on the Deep Convolutional Neural Network (D-CNN) architectures & Rice & 8 & 2019–2020 \\
[8] & 2022 & Machine Learning (ML) based techniques for forecasting, detection, and classification of diseases and pests, with an emphasis on tomato crops & Tomato & 16 & 2006–2020 \\
[9] & 2022 & Computational methods operated in different stages of plant-pathogen systems such as image preprocessing, segmentation, feature extraction and selection, and classification to diagnose the diseases & Cotton & 6 & 2012–2018 \\
[10] & 2022 & Review of 100 Convolutional Neural Network (CNN) approaches to detect plant leaf diseases. & Multiple & 100 & 2015–2022 \\
[11] & 2022 & Tomato leaf disease identification using image processing, ML, and deep learning approaches & Tomato & 44 & 2015–2022 \\
[12] & 2022 & Plant leaf disease recognition and classification using deep learning and advanced imaging techniques & Multiple & 50 & NA \\
[13] & 2022 & ML and deep learning for maize leaf disease classification & Maize & 62 & 2015–2022 \\
[14] & 2022 & Diseases that harm potato crops and a survey on computer vision-based techniques and types of ML algorithms used. Crop disease and pest recognition using image processing to extract the features and algorithms used in prediction studies & Multiple & 10 & 2015–2020 \\
[15] & 2022 & Plant disease detection using ML ML and Deep Learning based approaches for plant leaf disease classification & Multiple & 15 & 2014–2021 \\
[16] & 2022 & Image processing techniques with the help of artificial neural networks for automatic pest detection & Multiple & 30 & 1970–2022 \\
[18] & 2022 & Trends and challenges for the detection of plant leaf disease using deep learning and advanced imaging techniques & Multiple & 16 & 2012–2021 \\
[19] & 2021 & D-CNN approaches for identification and diagnosis of various diseases on plant leaves & Multiple & 42 & 2019–2020 \\
[20] & 2021 & Image processing techniques employed in pest and disease recognition using visible light (RGB) images & Multiple & 7 & 2016–2019 \\
[21] & 2021 & Image processing techniques employed in pest and disease recognition using visible light (RGB) images & Multiple & 51 & 2015–2020 \\
[22] & 2020 & ML technologies for plant disease detection & Multiple & 25 & 2016–2022 \\
[23] & 2020 & Grains disease detection, quantification, and classification using computational techniques & Grains & 109 & 2001–2020 \\
[24] & 2020 & Deep learning techniques in plant disease detection & Multiple & 18 & 2015–2018 \\
[25] & 2020 & Sensors for automatic detection and monitoring of insect pests & Multiple & 63 & 2004–2020 \\ \hline \end{tabular}
\end{table} 
Table 1: Studies reviewing the detection, identification, and classification of diseases and pests in agricultural crops. The number of studies analyzed in each review is quite diverse (see Figure 1). Twenty percent of reviews analyze 10 studies or fewer, and 45% analyze 20 studies or fewer. Considering the number of studies that have appeared in recent years, the number of studies included in these reviews is insignificant. On the other hand, 40% (eight reviews) of the reviews analyze more than 40 studies. Of these, three reviews analyze specific crops (tomato, maize, and grains). The other five reviews analyze multiple crops. One of them ([25]) presents a review of sensors for the automatic detection and monitoring of insect pests, which is different from the focus of the review presented in this article. Considering the dimension of the review, i.e., the number of studies included in each review and the scope, the remaining four reviews ([10; 12; 19; 21]) have some similarities with the review presented in this article.