Decision Support Systems and Data Analysis in Insect Pest E-Monitoring and Control, 2nd Edition

Dear Colleagues,

Decision support systems (DSSs) in the integrated pest management (IPM) of insects, diseases and weeds involves controlling the use of data from a variety of sources—such as weather conditions, crop/fruit phenological stages, and pest population dynamics—to assist farmers, producers, and other stakeholders in making critical decisions about pest control. The widespread adoption of DSSs in IPM has contributed to the complexity of today’s farming issues, as they are heavily dependent on various agro-ecological and environmental conditions.

Data collection and analysis, which are central to data-driven decision-making, can incorporate spatiotemporal statistical techniques to process and analyse data from precision agriculture (PA) systems, such as sensors, IoT, and drones, identifying patterns and trends that can improve pest management strategies. PA is a farming management concept that optimises decision-making, data analytics, and data mining. Currently, PA is used in the context of e-monitoring pests and improving control strategies. In addition, the e-monitoring of pests leads to more accurate information and a better understanding of their spatiotemporal distribution. Furthermore, data mining, a specific type of data analysis, uses machine and deep learning algorithms to reveal hidden patterns and relationships within large agri-datasets (big agri-data), including weather information, soil conditions, market demand, and land use.

In the domain of agriculture, these technologies and systems also support farmers in addressing complex issues related to crop production. They can be used to predict pest outbreaks, optimise pesticide applications, and identify the most effective IPM control methods. However, as these tools scale into data-intensive, real-time monitoring systems, they become increasingly complex, including advances in recognition models of insect pests using image processing, developments in methods to identify pests using machine learning that reduce data preprocessing and the fitting degree of model fluctuation, and applications of deep learning to handle complex patterns and representations of data.

This Special Issue highlights current trends and future directions in the use of DSSs for IPM, spatiotemporal data analysis, and data mining in agriculture. We welcome research articles and manuscripts that present novel and original work. Topics of interest include, but are not limited to, the following research areas:

• DSS principles and concepts, especially when applied in IPM, including tools, methods, and techniques, interface design, implementation, and evaluation in pest e-monitoring and control;
• Real-time e-monitoring systems to measure pest population dynamics regularly, using various deployment patterns and IoT tools;
• E-monitoring tools based on PA and spatiotemporal data analysis for pest forecasting models;
• Real case studies or in-field experiments for the identification of key pests, utilizing either machine learning, deep learning, or image processing techniques, which aim to identify and count insect pests from images taken by camera-equipped e-traps deployed in a cultivation field;
• DSS models and algorithms that help managers determine the most precise locations (where), optimal timing (when), and best practices (how) of spray applications targeting certain key pests;
• Algorithms that are based on agri-data collected from automated weather stations, site-specific weather conditions, and weather forecasts in combination with provided field data and knowledge base;
• Recommendations for growers on the use of pesticide applications and/or which traditional practices should be used to improve pest control.

Prof. Dr. Theodore A. Tsiligiridis
Prof. Dr. Andrea Sciarretta
Dr. Dionysios Perdikis
Guest Editors

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• decision support systems
• precision agriculture
• data analysis
• data mining in agriculture