# Algorithms for Plant Monitoring Applications: A Comprehensive Review
## 4 Algorithm Usage
The most studied crops in this context, for different diseases, are maize [349; 350; 351], rice [352; 353], wheat [354; 355; 356], potato/tomato [357; 358; 359],  
Figure 19: Heat map showing the number of papers using a specific machine learning algorithm applied to a specific agronomic aim. berries [360; 361; 362; 363; 364], fruits [348; 363; 364], beans [365; 366; 367; 368; 369], peanuts [370; 371], cotton [372; 373], sugarcane [322], coffee [341], paprika and pepper [330; 374], shallot [375], cassava [376], and camelia oleifera [377]. However, the majority of papers analyze generic crops, with many authors proposing the use of CNNs with public large datasets, including different crops, focusing on the whole plant [339; 344; 378; 379; 380; 381; 382; 383] or specific leaf diseases [384; 385; 386; 387; 388; 389; 390; 382; 383; 384; 385]. Other applications are investigated: monitoring the crop characteristics and their abiotic stress factors [386; 387; 388; 389; 391; 392], or supporting tools for smart agriculture [393; 394; 395; 396; 397]. Some other algorithms appear: ResNN [398] and MLP [323; 330; 331; 341; 346; 356; 385; 399; 400; 401; 402], alongside Attention Mechanism (ATN) [403; 404; 405; 406; 407; 408; 409; 400; 403; 404; 405; 406; 407; 408; 409; 410; 411], ELM [333; 412], Feature Pyramid Network (FPN) [413], GAN [366; 414], Long Short-Term Memory (LSTM) [342; 366; 415; 416], Radial Basis Function Neural Network (RBFNN), Recurrent Neural Network (RNN) [358], and SSD. Transformer (T) [348; 356; 362; 367; 368; 396; 417; 418] appears in the very last part of the searched period, foreseeing promising implementations in the next months. Regarding agronomic aims, CNNs are widely used for disease identification and slightly less for other topics. All other models, with smaller numbers, follow the same trend. In general, we can conclude that the papers that apply algorithms to agronomics are mainly focused on disease identification (264 for deep learning and 137 for machine learning), disease prediction (24 and 55, respectively), and early disease detection (25 and 43, respectively), sometimes combined. In addition, crop phenology is analyzed, with 21 papers for deep learning and 57 for machine learning. The most dominant deep learning algorithm used for plant disease is CNN (146 for disease identification, 24 for disease prediction, and 25 for early disease detection), followed by MLP (20, 7, and 5, respectively) and ResNN (48, 2, and 4, respectively). SVM (36, 10, and 11, respectively) and RF (27, 14, and 9, respectively) are the most used machine learning algorithms applied to the same agronomic aims. The same algorithm relevance is confirmed looking at crop phenology. Regarding crops, cereals attract the main interest, with 69 pa  
Figure 20: Heat map showing the number of papers using a specific deep learning algorithm applied to a specific crop. 60 small grains, including both deep and machine learning, but the group including potato and tomato also counts 75 papers. Analyzing Figures 19 and 21, we can conclude that machine learning algorithms, compared to deep learning ones, are uniformly applied to a broader variety of agronomic aims, with a light preference for crop phenology prediction, thanks to the easy applicability of some methods like RF or GBT. On the contrary, deep learning algorithms are preferred for disease detection, thanks to their capability to use CNNs on heavy tasks like image analysis, in a simpler and more efficient way, compared to machine learning methods.