# Algorithms for Plant Monitoring Applications: A Comprehensive Review
## 1 Introduction  
In recent years, huge amounts of data have been made available for a large variety of astronomical applications. In remote sensing, pictures taken from satellites offer soil and crop RGB and hyper-spectral images [1]. When higher precision is required, one can use an Unmanned Aerial Vehicle (UAV). In proximal sensing, the introduction of new wireless technologies like LoRa, SigFox and Nb-IoT allows data collection from miniaturized sensors with low energy consumption, making it possible to increase deployment density and acquisition frequency. Moreover, new trends have emerged recently, with picture collection in fields performed with smartphones [2] or multimedia proximal sensors and cameras [3]. In this scenario, artificial intelligence, through machine learning and deep learning algorithms, can process these streams of data in real time, enabling the dynamic monitoring and early detection of potential threats, opening new avenues for predictivemodeling and proximal disease management, and leading to smarter and more resilient farming practices [4]. The listed sources primarily offer images with different spatial resolutions. Among satellites, Landsat 8 provides data with a spatial resolution of 30 m, Sentinel-2 offers data with a resolution down to 10 m, Airbus Spot 6 down to to 1.5 m, and Pleiades down to 50 cm. The frequency for Landsat 8 is every 16 days, for Sentinel-2 every 3/5 days (depending on the areas), and for Airbus Spot 6 and Pleiades, it varies upon specific demand. UAVs offer resolution down to 1 cm, with frequency decided by the end-user. Proximal cameras or smartphones are used to acquire punctual daily pictures. As a result, large images datasets can be constructed; this huge flow of images is typically processed by means of algorithms, but algorithms are also adopted to plan UAV flights [5] or to choose the deployment of proximal sensors [6]. Additional inputs are obtained by the application of data fusion to precision agriculture; this process integrates multiple data sources to improve accuracy and efficiency in plant monitoring applications. As an example, recent studies highlight its potential by combining spatial interpolation methods with UAV-based assessments to accelerate crop variability evaluation and enhance site-specific management [7]. Agriculture can benefit from the availability of this large amount of data, especially in two fields: to document plant phenology and to identify or detect plant diseases early. In both cases, data processing through algorithms represents an efficient means to extract indicators strongly supporting crop management, both from the perspective of phenology and that of plant diseases. Phenology is the study of plant and animal life cycles, in particular, the time sequence originated by weather and climate changes [8]. Phenology is a key adaptive feature of organisms, tuning biotic interactions in response to the environment. Knowledge of the phenological phase sequence is crucial for changes across environments and ecosystems with the relevant impact of climate change [9]. Phenological phases can be analyzed through satellite and UAV images and many researchers apply algorithms for the fast and effective extraction of input, soil, and yield indicators. Plant diseases are one of the greatest challenges faced by global agriculture, causing considerable crop losses, food insecurity, and economic hardships and challenging food safety. According to estimates, crop diseases account for 20-40 percent of global agricultural production losses annually, highlighting the need for effective plant disease management strategies [10]. Phytosanitary issues, such as disease outbreaks and the resulting harmful effects on plants, directly threaten crop productivity and represent significant challenges for food security. An accurate identification of diseases, combined with an understanding of crop phenology, is essential for timely interventions to mitigate plant damages and minimize yield losses.