An Application of Machine Learning Technique in Forecasting Crop Disease

The Applicability of Big Data in Climate Change Research: The Importance of System of Systems Thinking

“…Neural networks are used to analyse climate change, weather prediction, and visualization (Buszta and Mazurkiewicz, 2015), while machine learning techniques are used for intelligent recognition (Demertzis and Iliadis, 2016) and to define the impact of climate change and resilience (Rolnick et al, 2019). In addition, they are used to predict epidemics and diseases in both social (Rees et al, 2019) and environmental contexts e.g., in the case of crops (Fenu and Malloci, 2019), coffee disease and pest (Lasso and Corrales, 2017), or pedotransfer functions (Benke et al, 2020). Clustering techniques on cloud computing infrastructure have been applied, e.g., to map changes in glaciers (Ayma et al, 2019).…”

“…It allows (Kubo et al, 2020) forecasting and a better understanding of coastal traffic and increases the reliability of disaster resilience estimation (Sasaki et al, 2020). By extracting time series data (Joshi et al, 2019) from satellite imagery, we can indirectly validate the models by comparing the time series or identify the factors of potato disease (Fenu and Malloci, 2019). In urban developments (Milojevic-Dupont et al, 2020) and in building condition surveys (Gouveia and Palma, 2019) the forecast shows the development of infrastructure expansion and maintenance, to which the probability of flood protection problems (Avand et al, 2021) can also be linked.…”

“…And comparable to other approaches (Jimenez et al, 2019), which strengthens confidence in the models (Qin and Chi, 2020). Better resolution data supports marine habitat protection planning (Coro et al, 2020), risk assessment input (Fenu and Malloci, 2019), but can also be used (Gouveia and Palma, 2019) to analyze building consumption data. The efficiency of downscaling techniques can be increased with the Internet of Things (Lambrinos, 2019) toolbar.…”

“…The data of the Internet of Things sensors (Mabrouki et al, 2021) allow the conclusions drawn from the satellite images to be verified (Majidi et al, 2021), as a measuring station (Jimenez et al, 2019) increases the number of observations, thus better downscaling solutions (Wang Q. et al, 2020) can be made. It can be used for causal exploration of plant morphological damage (Fenu and Malloci, 2019) and supports agricultural irrigation water demand planning (Ismail et al, 2020), but can also be imported into flood models (Avand et al, 2021).…”

DSS LANDS: A Decision Support System for Agriculture in Sardinia

“…The agricultural DSS is composed of three components [24]:  An integrated system for semi-real-time monitoring of crop components and storage of their data; These sources include ARPAS (Regional Agency for the Protection of the Sardinian Environment) weather stations, field sensors and external providers;  A models system which performs through several mathematical and forecasting models a cross and dynamic analysis of different types of data. Their elaboration and interpretation allow us to provide the best strategies to be applied in the field in order to forecast possible risk event situations which can damage the production [25,26];…”