Towards Sustainable Farming: A Robust Decision Support System’s Architecture for Agriculture 4.0

Pechlivani, Eleftheria Maria; Gkogkos, Georgios; Giakoumoglou, Nikolaos; Hadjigeorgiou, I.; Tzovaras, Dimitrios

doi:10.1109/dsp58604.2023.10167922

Cited by 9 publications

(7 citation statements)

References 20 publications

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“…In the top-down approach to IPM-DSS typology development, the purpose, objectives, and end users of DSSs were considered, as well as the spatial and temporal constraints on their use. To avoid biases in the structure of the typology (e.g., individual preferences and characteristics from IPM DSS developers favouring their DSS), this step was based on our own experience and the extensive review of available literature on the development of agroecological DSSs [14,17,19,25,27,29,30,38,48,50,[53][54][55][56][57]. In addition, the literature review also indirectly represented the users of IPM DSSs, as the characteristics and features that users want and need are described [19,27,29,30,58,59].…”

Section: (I) Top-down Approachmentioning

confidence: 99%

Typology for Decision Support Systems in Integrated Pest Management and Its Implementation as a Web Application

Marinko,

Blažica,

Jørgensen

et al. 2024

Agronomy

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Decision support systems (DSSs) enable the optimisation of pesticide application timing to increase pesticide efficacy and thus reduce pesticide use without compromising yield quality and quantity. Limited access to information about available DSSs for use in integrated pest management (IPM) is a major barrier to the uptake of DSSs for IPM across Europe. To overcome this barrier, a typology for DSSs for IPM in Europe was developed, introducing a systematic approach to describe the ever-growing number of DSSs for IPM. The developed IPM-DSS typology was implemented in the free web tool “IPM Adviser”, where currently 79 IPM DSSs are described with over 50 attributes describing their structural and performance characteristics. The information about IPM DSSs, which was previously scattered on different websites and difficult to compare, is now standardised and presented in a uniform way, so that it is possible to compare different IPM DSSs on the basis of all the attributes described. The presented IPM-DSS typology implemented in the web tool IPM Adviser facilitates the dissemination and uptake of DSSs for IPM and thus contributes to the achievement of the EU targets for the sustainable use of pesticides.

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Section: (I) Top-down Approachmentioning

confidence: 99%

Typology for Decision Support Systems in Integrated Pest Management and Its Implementation as a Web Application

Marinko,

Blažica,

Jørgensen

et al. 2024

Agronomy

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“…Spectral imaging [30] has recently been enhanced by the integration of AI and DL techniques [31], offering a promising approach for advancements in the agricultural sector. Pechlivani et al (2023) [19] developed a cost-effective hyperspectral camera using off-theshelf components and open-source software, enabling the simplified capture and analysis of hyperspectral imaging data. Giakoumoglou et al (2023) [32] utilized multispectral imaging to identify the plant disease, grey mold, applying DL models that achieved a 93% accuracy in classification and a 0.88 mean Average Precision (mAP) in detection.…”

Section: Spectral Imaging In Agriculturementioning

confidence: 99%

“…Utilizing the sensors contained in the embedded system and the diffraction grating placed in a 3D-printed module, this tool is capable of capturing conventional and hyperspectral images, while, at the same time, monitoring different environmental conditions. Moreover, the HI-based smart embedded system's open interfaces extend its functionality, enabling the transmission of captured information to Decision Support Systems (DSSs), allowing further analyses and empowering farmers with timely insights for effective interventions [21]. To showcase the proposed citizen science tool, one of the most severe pests of tomato plants that quickly spreads throughout Europe and Mediterranean countries [22,23] was chosen-the tomato leafminer, commonly known as Tuta absoluta (Meyrick).…”

Section: Introductionmentioning

confidence: 99%

A Citizen Science Tool Based on an Energy Autonomous Embedded System with Environmental Sensors and Hyperspectral Imaging

Kouzinopoulos,

Pechlivani,

Giakoumoglou

et al. 2024

JLPEA

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Citizen science reinforces the development of emergent tools for the surveillance, monitoring, and early detection of biological invasions, enhancing biosecurity resilience. The contribution of farmers and farm citizens is vital, as volunteers can strengthen the effectiveness and efficiency of environmental observations, improve surveillance efforts, and aid in delimiting areas affected by plant-spread diseases and pests. This study presents a robust, user-friendly, and cost-effective smart module for citizen science that incorporates a cutting-edge developed hyperspectral imaging (HI) module, integrated in a single, energy-independent device and paired with a smartphone. The proposed module can empower farmers, farming communities, and citizens to easily capture and transmit data on crop conditions, plant disease symptoms (biotic and abiotic), and pest attacks. The developed HI-based module is interconnected with a smart embedded system (SES), which allows for the capture of hyperspectral images. Simultaneously, it enables multimodal analysis using the integrated environmental sensors on the module. These data are processed at the edge using lightweight Deep Learning algorithms for the detection and identification of Tuta absoluta (Meyrick), the most important invaded alien and devastating pest of tomato. The innovative Artificial Intelligence (AI)-based module offers open interfaces to passive surveillance platforms, Decision Support Systems (DSSs), and early warning surveillance systems, establishing a seamless environment where innovation and utility converge to enhance crop health and productivity and biodiversity protection.

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“…Pechlivani et al [ 12 ] proposed a robust decision support system (DSS) with user-centric, cloud-based farm management system. It utilized real-time data from digital and space-based technologies, employing artificial intelligence (AI) algorithms and a user-friendly interface for data collection and remote sensing.…”

Section: Related Workmentioning

confidence: 99%

“…Therefore, an inspector system requires multiple sensors for data collection. Furthermore, an Agro-toolbox can support a RESTAPI architectural style for decision support systems, which can allow integrated nutrient management of open fields, as well as of hydroponics cultivations [ 12 ].…”

Section: Introductionmentioning

confidence: 99%

IoT-Based Agro-Toolbox for Soil Analysis and Environmental Monitoring

Pechlivani,

Papadimitriou,

Pemas

et al. 2023

Micromachines

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The agricultural sector faces numerous challenges in ensuring optimal soil health and environmental conditions for sustainable crop production. Traditional soil analysis methods are often time-consuming and labor-intensive, and provide limited real-time data, making it challenging for farmers to make informed decisions. In recent years, Internet of Things (IoT) technology has emerged as a promising solution to address these challenges by enabling efficient and automated soil analysis and environmental monitoring. This paper presents a 3D-printed IoT-based Agro-toolbox, designed for comprehensive soil analysis and environmental monitoring in the agricultural domain. The toolbox integrates various sensors for both soil and environmental measurements. By deploying this tool across fields, farmers can continuously monitor key soil parameters, including pH levels, moisture content, and temperature. Additionally, environmental factors such as ambient temperature, humidity, intensity of visible light, and barometric pressure can be monitored to assess the overall health of agricultural ecosystems. To evaluate the effectiveness of the Agro-toolbox, a case study was conducted in an aquaponics floating system with rocket, and benchmarking was performed using commercial tools that integrate sensors for soil temperature, moisture, and pH levels, as well as for air temperature, humidity, and intensity of visible light. The results showed that the Agro-toolbox had an acceptable error percentage, and it can be useful for agricultural applications.

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Towards Sustainable Farming: A Robust Decision Support System’s Architecture for Agriculture 4.0

Cited by 9 publications

References 20 publications

Typology for Decision Support Systems in Integrated Pest Management and Its Implementation as a Web Application

Typology for Decision Support Systems in Integrated Pest Management and Its Implementation as a Web Application

A Citizen Science Tool Based on an Energy Autonomous Embedded System with Environmental Sensors and Hyperspectral Imaging

IoT-Based Agro-Toolbox for Soil Analysis and Environmental Monitoring

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