# Algorithms for Plant Monitoring Applications: A Comprehensive Review
## 3 Literature Analysis
### Agronomic Aims and Data Sources
Applications are found in other crops, like rice [293], maize [294; 295], grapes [47], peanuts [116], palm [194], and pine trees [191]. These studies predominantly focus on the qualitative identification of healthy and infected plants. Instead, pixel-level regression analysis for the quantification of the wheat stripe rust disease index is performed in [158]; the authors successfully obtain continuous phenotypes, including crop yield and plant height, and develop disease distribution maps for wheat stripe rust. RGB cameras are also used, both in remote and proximal sensing. Advantages are highlighted by [98] with a dual-drone collaborative approach, one with multi-spectral cameras, the other with ultra-high-resolution ones; by integrating the vegetation indexes derived from the multi-spectral UAV and the texture index derived from the RGB camera, accuracy improvements are obtained for late blight monitoring in potato. Rice Bakanae disease infection rates are monitored using drones with RGB cameras [296]; the same cameras are used for maize plants, creating a database of pictures acquired in the field, showing healthy plants and those infected by common rust, gray-leaf spot, and blight [4]. Field photos, managed with algorithms, are also used for _Botrytis fabae_[297], tomato disease [179], habanero disease [298], and wheat rust identification [52]. Autonomousrovers, equipped with RGB cameras or smartphones, move in tomato [200] and citrus nurseries to detect diseases; additional equipment for automatic spray application is also proposed in [299]. The collection of plant disease images can be carried out with a smartphone, and [PERSON] et al. in [300] document the effort to unify images from different sources (wheat stripe rust, wheat leaf rust, and wheat powdery mildew) to contribute to disease identification. Even though the source can impact on the identification, satisfactory results can be achieved applying proper algorithms. Several papers aim to build open datasets. The authors in [214] collect high-resolution leaf images in sugarcane crops and construct an open dataset called \"Sugarcane Leaf Dataset\", with leaf images classified in eleven categories, including diseased, healthy, and dried leaves. Ref. [4] illustrates similar work for maize, introducing four categories: common rust, gray-leaf spot, blight, and healthy leaves. Open databases are very helpful for scientists without experimental settlements or with limited ones. PlantVillage is an example; it offers leaf pictures regarding diverse crops and diseases. It is used for algorithm application by several authors interested in different crops and diseases [210; 230; 211; 231; 232; 303]. NWRD is another open-source dataset for wheat diseases [226]. The MODIS dataset [304] is used by [305] to map croplands mixed with paddy fields and rice-crayfish fields over large areas, while [305] takes advantage of data published by the Pan European Phenological Project [306] to elaborate phenological data. Photo collection in the field is crucial for disease identification, but inoculum source detection is equally important. The authors in [307] collect a huge amount of microscope images, both of single (8959) and mixed (1450) spores with the aim of quantifying pathogen inoculum thanks to the application of algorithms, stressing their relevance in the automatic detection of rice blast fungus spores. Similarly, the authors in [308] use a diffraction-polarization imaging system and the diffraction fingerprint images of fungal spores to classify airborne spores in greenhouses. Tomato gray mold, cucumber downy mold, and cucumber powdery mildew spores are correctly identified with around 96 percent correct identification, thanks to SVM. Other examples based on microscope image analysis for spore attribution to fungal species regard _Botrytis cinerea_, for its detection in cucumber [309] and _Lasiodiplodia_ species associated to grapevines showing _Botryosphaeria_ dieback, with four species included (_L. brasiliensis, L. crassipora, L. exigua_, and _L. gianensis_) [187].