# Algorithms for Plant Monitoring Applications: A Comprehensive Review
## 3 Literature Analysis
### Algorithms
#### 3.2.3 Machine Learning  
Among the classic machine learning predictors, as shown in Figure 11, the most used ones are Random Forest (RF) and Support Vector Machine (SVM), two of the most popular algorithms in the field [69]. Figure 10: Histogram showing the recurrence of clustering algorithms among those used in the reviewed studies. Machine learning algorithms are versatile enough to be used in different scenarios; for example, the authors in [70; 71; 72; 73; 74; 75; 76] use proximal sensed data, acquired in different ways, to feed models that predict conditions in which various diseases may occur. Proximal monitoring is considered fundamental to help farmers in decision making. Besides sensors, meteorological data can be used [77; 78; 79; 80; 81; 82]; as an example, the NASA Power platform provides up-to-date meteorological datasets, used by [PERSON] et al. in [83]. High-resolution data are very important in this context, as reported in [84], where K-Nearest Neighbor (KNN) is used to evaluate _Alternaria_ leaf spot on apple. Similarly, [PERSON] et al. in [85] use RF to forecast the aerobiological risk level of _Pltyophthora_ on potato, while [PERSON] et al. in [86] apply Decision Tree (DT) to predict grape downy mildew. Another available option, which provides interesting insights into phenology and disease analysis, is provided by in-field measurements and observations. In [87; 88; 89; 90; 91; 92], ad hoc generated datasets are used as features for machine learning models, sometimes integrating sensors and meteorological data. Nevertheless, laboratory analyses supply valuable data to discriminate plant status that can be used to train models [93; 94; 95; 96; 97]. Another very common and popular practice is the usage of vegetation indexes to predict the different conditions of the crops. Usually obtained by processing satellite or UAV images, these indexes provide a valuable and convenient way to train classifier and regressor models [98]. Some specific applications regard the detection of potato late blight [99], peanut disease defoliation, leaf spot and southern blight [100; 101; 102], _Fusarium_ head blight and scalp in wheat [103; 104], generic wheat diseases [105], rice panicle blast [106], and _Xanthomonas_ in broccoli [107]. This approach is also applied to describe the spatial variability of biotic and abiotic plant diseases [108; 109; 110; 111; 112; 113; 114], and in plant phenology and yield monitoring [81; 115; 116; 117; 118]. Methods such as RelieF and Pearson Correlation are commonly used in this context for feature selection [119]. Moreover, hyper-spectral images can also be directly processed, using different techniques, to extract different spectral bands. Many papers provide different benchmarks regarding model accuracy and propose different methodologies and workflows applied to different practical applications. The early detection of diseases  
Figure 11: Histogram showing the recurrence of machine learning algorithms among those used in the reviewed studies. is applied for _Phytopthora_ in pepper [120], wheat rust [121], bacterial diseases and crown rot in tomato [121; 122], late blight in potato [56; 123], _Fusarium_ head blight and powdery mildew in wheat [124; 125], Valsa canker in apple [126], _Pseudomonas_ in kiwi [127], and leaf diseases in rubber trees, soya, and coffee [128; 129; 130; 131; 132]. Pathogen and plant field monitoring takes advantage of image processing [133; 134; 135; 136], as does fruit sorting [137; 138; 139]. Working with these kinds of images poses high dimensionality issues, often solved with feature engineering methods, such as Principal Component Analysis (PCA) or SPA. Classic RGB images are also valuable datasets in machine learning, despite the fact that in recent years, image processing has been mostly carried out by means of deep learning.