# Algorithms for Plant Monitoring Applications: A Comprehensive Review
## 3 Literature Analysis
### Comprehensive Reviews  
Algorithms represent useful tools for scientists looking for solutions to enhance agronomic analyses. Some comprehensive reviews include a detailed description of the principle  
Figure 8: Heat map showing the intersection between agronomic aims and crops in the selected papers; the first column refers to studies applied to generic agronomic aims; the first row refers to studies applied to generic crops. and methods of hyper-spectral sensing of plant diseases [15], with algorithms belonging to the categories of artificial intelligence [20], machine learning [21], and deep learning [22; 23]. Major efforts are devoted to rice crops, with six papers discussing remote sensing, image analysis, and algorithm applications for disease detection [24; 25; 26; 27; 28; 29]. Other reviewed crops are fresh and horticultural products [30; 31]. Moreover, causal agents of diseases, like _Ganoderma_[32] and _Pseudomonas syringae_[33], or plant stress conditions [34; 35] are studied. Comparative evaluations assess the performance of algorithms applied to important crops distributed worldwide, like apple, cassava, cotton, and potato. The added value in supporting crop chain management with a holistic view is confirmed, including variables related to host crops and diseases, environmental data, and management strategies [36]. A focus on hyper-spectral images combined with algorithms confirms the high potential to benefit agriculture and forest challenges, including those related to climate change [37]. The transition from Agriculture 4.0 to Agriculture 5.0, crucial in plant disease management, is discussed in [38]. Innovation and keen solutions to increase the level of automation are recommended and they should include the support of different areas, like hardware, telecommunications, and robotics. Automatized decision-making processes, with limited human intervention, are mandatory, therefore needing powerful algorithms and advanced robotics. Agriculture mapping significantly aids decision-making. Artificial intelligence applied to diverse data sources, primarily images, can help land management, identifying cultivated soil, crop distribution, crop phenology, and health status, all of which are relevant aspects for sustainable agriculture [39]. The application of algorithms to plant phenotyping supports crop management to improve plant health. The analysis of spectral and hyper-spectral images by means of algorithms delivers information regarding plant stress, nutritional status, and disease occurrence [40]. UAVs equipped with sensors contribute to plant phenotyping, especially by collecting biophysical, physiological, and biochemical plant characters at a narrow scale. Challenges are documented, mainly related to data validation, but the combination with algorithms results in unquestionable advantages for many applications, including plant breeding [41]. An example regarding sunflower shows the potential improvement of this crop, thanks to the combination of phenotyping and machine learning [40]. An issue regards the dimension of datasets that should include enough data for a fruitful application of algorithms, only partially solved by proximal and remote sensing. The interest in facing this challenge stimulates researchers to develop label-efficient machine learning/deep learning methods, tailored for agricultural applications. Several challenges are still unsolved but the remarkable potential to noticeably reduce the efforts requested for data collection is strongly confirmed [42]. Advantages and challenges are also discussed in a paper focused on soil sensing technology and data management [43]. In terms of projection towards the future, machine learning and deep learning help in deciphering omics data that are generated in plant pathology. In this case, a large amount of data are delivered and algorithm-assisted omics techniques are crucial in data analysis and interpretation, allowing a step forward in understanding plant-pathogen interaction [44].