# Algorithms for Plant Monitoring Applications: A Comprehensive Review
## 2 Materials and Methods  
### Search Strategy and Preliminary Search Outcomes  
A comprehensive literature search was conducted to identify relevant studies, investigating the use of algorithms to classify plant phenology and plant diseases. This systematic review was carried out in accordance with the Preferred Reporting Items for Systematic Reviews [13]. Initially, the Web of Science (WoS) database was searched and several keywords were tested on the full journal texts, both from the agronomical and algorithmic points of view [14]. After comparing the results, we identified three agronomical keywords as relevant and complementary: \"phenology\", \"plant disease\", and \"mycotoxin\". From the perspective of the algorithms, the two algorithmic keywords that generated the majority of the results were represented by \"machine learning\" and \"deep learning\". Restricting the search from the full journal text to a set of three fields formed by title, abstract, and keywords, we did not experience limitations with the agronomical keywords, while we noticed that several papers using transfer learning methodology or transformer architecture were no longer selected by the query, as for those papers the set of the three search fields did not include any reference to the two algorithmic keywords. For this reason, we decided to add \"transformer\" and \"transfer learning\" to the set of algorithmic keywords. Moreover, we understood that one additional keyword should be added: \"optimization\". The optimization algorithm's goal is to maximize or minimize an objective function; hence, they are commonly used in machine learning and deep learning, but their usage is wider and alternate applications would have not be accounted for if \"optimization\" was not included in the algorithmic keywords. As a result, the following query was formulated as the basis of the search:  
IN ( (title) OR (abstract) OR (keyword) ) SEARCH (\"phenology\" OR plant disease* OR \"mycotoxin\") AND (\"machine learning\" OR \"deep learning\" OR \"transfer learning\" OR \"transformer\" OR \"optimization\")  
The search on WoS was initially conducted in August 2024 and regularly updated during the preparation of this paper, until the end of November 2024. Consequently, we report here some generic statistical analyses covering the period between 1 January 2000 and 30 November 2024. The yearly publication count since 2000 is shown in Figure 1. It can be noticed that the number of published papers has significantly increased, starting in 2015, since when it has become stably higher than 100 per year. Starting in 2022, the increasing rate has been lowering and the number has progressively stabilized. Figure 2 shows the number of papers containing each of the three agronomic keywords appearing in the search query. As can be seen, the majority of authors concentrate on algorithm application to plant diseases and phenology, with a research trend that follows the general one. Mycotoxin studies are less present but more numerically constant over the past decade. Concerning the different categories of algorithms, the situation is summarized in Figure 3. Deep learning has played the most important role during the past five years, with a constant and significant increase in papers, while machine learning was applied more frequently before 2019. The usage of all algorithms has increased over the years. Optimization employment has been more stable over the past ten years. The comparison  
Figure 1: Web of Science (WoS) search outcomes grouped per year, from 1 January 2000 to 30 November 2024. Figure 2: Recurrence of the agronomic keywords in the papers indexed in the Web of Science (WoS) during the period 2015–2024. between the optimization and machine learning curves shows how optimization examples can be found without a direct application in machine learning. We also classified the distribution of papers per country of application, summarizing results in Figure 4. China and India are the countries with more than 1000 published papers in the past 10 years.