Decision Support Systems and Data Analysis in Insect Pest E-monitoring and Control

Dear Colleagues,

Decision support systems (DSSs) in 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 provide farmers, producers, and other stakeholders with insight applied toward making critical decisions of how to control pests. It adds to the complexity of today’s farming issues, which has led to the widespread penetration of DSSs applied in IPM, which are heavily dependent on numerous conditions of the agro-ecological environment used for cultivation.

Data collection and analysis, a data-driven, decision-making concept, may use 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. Nowadays, PA is used in the context of e-monitoring several pests, as well as improving their control. In addition, the e-monitoring of a pest leads to more accurate information and a better understanding of its spatiotemporal distribution. Furthermore, data mining, a specific type of data analysis, uses machine or deep learning algorithms to present hidden patterns and relationships in large agri-data sets (big agri-data), concerning weather information, soil conditions, market demand, and land use.

In the agriculture domain, the above technologies and systems help farmers to solve complex issues related to crop production as well. They can be promoted to predict pest outbreaks, optimise pesticide applications, and identify the most effective IPM control methods. However, as these tools scale into data-extensive, real-time monitoring systems, the goals of these systems become increasingly challenging. They include advances in recognition models of insect pests using image processing, and in machine learning, insect pests’ identification methods that reduce the data preprocessing, as well as the fitting degree of the model fluctuation due to the advantages and disadvantages of the features, and finally, in deep learning they include advantages for more complex patterns and representations of data.

This Special Issue covers current trends and future developments of DSSs applied in IPM, spatiotemporal data analysis, and data mining in the agriculture domain. We welcome research articles and manuscripts that present novel and original work addressing key topics, such as the following:

• DSS principles and concepts particularly applied in IPM—tools, methods, and techniques, interface design, implementation, and evaluation in pest e-monitoring and control;
• Real-time e-monitoring systems, as they measure pest population dynamics on a regular basis, 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, which exploit either machine learning, deep learning, or image processing techniques and aim to identify and count insect pests from images taken by camera-equipped e-traps deployed in a cultivation field;
• DSSs models and algorithms that help managers decide the most precise (where), the optimal timing (when), and the best practices (how) of spray applications against 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;
• Advising growers to either use or not use pesticide application and/or which traditional practices to use to improve pest control.

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

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