# Algorithms for Plant Monitoring Applications: A Comprehensive Review
## 4 Algorithm Usage  
To provide a clear insight into how algorithms are applied to a specific agronomic aim or to a crop, in this section, we present some heat maps showing the number of papers among the 358 selected which introduce a specific algorithm to an agronomic aim or in a crop. Papers introducing optimization and clustering algorithms represent a small portion of the identified set, where machine learning and deep learning play the major role. In Figure 14, the application of optimization algorithms to different crops is reported. GA, PSO, and SGD are the most used, but no pattern with crops emerges. ACO, Arithmetic Optimization Algorithm (AOA) [163], Binary Dragonfly Algorithm (BDA) [312], Forest Optimization Algorithm (FOA) [313], Giza Pyramids Construction (GPC) [314], GSA [314], Golden Search Optimization (GSO) [315], Honey Badger Optimization Algorithm (HBA) [316], HUNG, Evolved Sign Momentum (LION) [317], Satin Bowerbird Optimizer (SBO) [318], Shuffled Complex Evolution (SCE) [319], SFO, SSA, Tunicate Swarm Algorithm (TSA) [320], and Whale Optimization (WO) [263] appear in our search, with smaller numerical consistence. Potato and tomato, maize, and rice are the crops most investigated. Moreover, a good number of papers propose algorithm application to generic cultures. Figure 15 shows the application of optimization algorithms to different agronomic aims, where disease identification plays the most relevant role, with 33 papers. Figure 14: Heat map showing the number of papers using a specific optimization algorithm applied to a specific crop. Regarding clustering, Figure 16 shows the application of this group to crops. Figure 17 reports their application to agronomic aims, with a clear preference for disease identification. KM is the most used, with general interest, independently on crop typology. FC follows, with SC and simple linear iterative clustering (SLIC) being rarely used. Figure 16: Heat map showing the number of papers using a specific clustering algorithm applied to a specific crop. Figure 15: Heat map showing the number of papers using a specific optimization algorithm applied to a specific agronomic aim. When crossing machine learning or deep learning algorithms with crops and agronomic aims, heat maps start to become denser. Figure 18 shows the correlation between machine learning and crops, and Figure 19 displays the correlation between machine learning and agronomic aims. Figure 17: Heat map showing the number of papers using a specific clustering algorithm applied to a specific agronomic aim. Figure 18: Heat map showing the number of papers using a specific machine learning algorithm applied to a specific crop. The most used algorithms are RF [78; 321; 322; 323; 324; 325] and SVM [336; 337], followed by KNN [332; 338] and other tree-based models such as DT [324; 339] and Gradient Boosting Tree (GBT) [321; 322; 323; 324; 325; 326; 327; 333; 340; 341; 342; 343; 344]. Multiple Linear Regression (MLR), Naive Bayes (NB), AdaBoost (ADA), Bagging or Bootstrap Aggregating (BS) [344], Elastic-Net (ELNET), Extra Tree Classifier (EXT) [341], Gaussian Process Regression (GPR) [345], Least Absolute Shrinkage and Selection Operator (LASSO), Linear Discriminant Analysis (LDA) [332; 338; 346], Logistic Regression (LR) [78; 347], Partial Least Square (PLS) [333; 345; 348], and Ridge Regression (RIDGE) [345] are found in papers, but less frequently. Instead, the most frequent crops are forest trees, maize, small grains, and potato/tomato. The generic crop category is the one to which models are most applied. Regarding agronomic aims, generic disease, crop phenology, disease identification, disease prediction, and early disease prediction are the most considered. However, disease identification shows the highest score and the greatest number of different algorithms exploited, while other agronomic aims mostly tend to use only RF and SVM. Deep learning model usage is reported in Figure 20 in correlation to crops and Figure 21 in correlation to agronomic aims.