Digital Transformation of Beekeeping through the Use of a Decision Making Architecture
Abstract
:1. Introduction
- A proposal of Maturity Model (MM) Levels for smart beehives;
- A proposal for analysing all the decisions that can be made in the beekeeping sectors using spatio-temporal matrix;
- A proposal of generic architecture that corresponds to the decisions analysed in the matrix.
2. Context and Related Work
2.1. From Industry 4.0 to Agriculture 4.0
- Internet of Things, because decisions need data collection done through connected objects like smart hives;
- Cyber-Physical System, a set of primordial principles that allow defining the hierarchy of decision makers and decision cycle;
- Big Data, because data is the most important thing on which decisions are based;
- Cloud computing, which refers to the delivery of resources and services on demand over the Internet, i.e., the access to data from different resources and high computing power via the internet. It is an important computing step that facilitates the deployment of intelligent systems;
- Artificial Intelligence: in fact, nowadays, artificial intelligence is a powerful tool that can be used to help users in the process of making decisions.
2.2. IoT for Digital & Green Agriculture
3. Maturity Model Levels and Smart Beehives
3.1. Maturity Model Levels for Smart Beehives
3.2. Smart Beehives Literature Classified by Maturity Model Levels
4. Analysis with a Spatio-Temporal Matrix
5. Decision Support Systems for Smart Beehives
- Sensors in the embedded system of the beehive;
- Open data about the weather (temperature, humidity, etc.) and pollen. The pollen is to know the state of the flora in a sectioned area.
5.1. Generic Architecture for Smart Beehives
5.2. Databases
- Spatial dimensions: sensors/hive/apiary/zone;
- Temporal dimensions: real-time /operational(day /week)/tactic (week/month) /strategic (year).
- Real-time: quantity of honey;
- Operational: varroa counting;
- Tactic: varroa treatment;
- Strategic: define the type and the amount of treatment and write a health risk management.
6. Case Study
- Low-power board computer, because the system has to be embedded on the apiary during several weeks;
- The materials have to be low-cost because we need to convince the beekeepers to use it. It means we have to make a compromise between accuracy and price.
- Arduino: the best embedded low-power single board computer;
- Sensors for the humidity and temperature: AZDelivery DHT11;
- Sensors for the Weight: HX711 Module;
- Sensors for communication: Z-Delivery ESP8266 ESP-01 Serial Wireless WLAN WiFi Transceiver Module Transceiver.
7. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
Abbreviations
API | Application Programming Interface |
CAGR | Compound Annual Growth Rate |
CPS | Cyber-Physical System |
DSS | Decision Support Systems |
ERP | Enterprise Resource Planning |
IoT | Internet of Thing |
IT | Information Technology |
JSON | JavaScript Object Notation |
MM | Maturity Model |
REST | REpresentational State Transfer |
RFID | Radio Frequency Identification |
WSN | Wireless Sensor Networks |
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Maturity Model Levels | 1 | 2 | 3 | 4 | 5 |
---|---|---|---|---|---|
Data Input | Manual or vocal input (semi-automatic) and global connection to environmental data | Automatic input by sensors and global connection to environmental data | Automatic input by sensors and global connection to environmental data | Automatic input by sensors and local/global connection to environmental data | Automatic input by sensors and local/global connection to environmental data |
Intelligence | Centralized | Centralized | Centralized and locally limited | Centralized + Local (including history of decisions made) + Local analysis of the data and automatic alerts sending | Centralized + Local cooperation and negotiation between beehives |
Communication | Vertical with synchronization for several days or weeks | Vertical with synchronization for several days or weeks | Vertical with Real Time Synchronization | Vertical with Real Time Synchronization | Horizontal and Vertical with Real Time Synchronization |
Authors | Date | Input Data | Output Data | Hardware | Software | MM |
---|---|---|---|---|---|---|
Aksoy et al. [31] | 2018 | company age, company province, level of education, status of membership of an association, other activities, hives number, race of bees, frequency of queen change | honey production | Data Mining algorithms: CART, CHAID, MARS | 1 | |
Dineva and Atanasova [32] | 2018 | beehive internal data (temperature, humidity, weight, noise), external data (temperature, humidity, CO2, polluting air) | relation between data, prediction of future events | sensors | OSEMN | 2 |
Braga et al. [33] | 2020 | temperature, weight, weather data, dew point, wind direction, wind speed, rainfall, luminosity | colony health status: good health, bad health, or collapse of colony health | sensors | K nearest neighbors method, Random decision forests, Neural Networks | 2 |
beeinformed.org [34] | 2021 | weight, number of varroas, virus, nosema | statistics | sensors | web application, open data, diagrams | 2 |
Edwards-Murphy et al. [35] | 2016 | CO2, O2, polluting gases, temperature, humidity, acceleration | colony health state, beehive internal weather data and external weather data | sensors (temperature, humidity, acceleration, air), network devices (3G, ZigBee) | Machine learning: decision tree | 3 |
Markovic et al. [36] | 2016 | temperature | critical events | temperature sensors | Complex event processing (CEP) | 3 |
Balta et al. [37] | 2017 | hive data (temperature, humidity, weight, bees traffic), external data (weather, location information) | bees counting, management of devices, resources and sensors, management of beekeeping data (beehive, apiary and region), detecting dangerous situation (robbing, swarming and colony losses) | Raspberry Pi, camera, sensors | sqlite, mongoDB, Node.js, Express.js, Angular.js, java on android, MQTT protocol, RESTful protocol, Cloud computing (Microsoft’s Azure and IBM’s Bluemix), Rule-based algorithm, XML, JSON, CSV | 3 |
Zogović et al. [38] | 2017 | weight, temperature, O2, CO2, vibration, sound, humidity, foragers traffic, thermal images of colonies, atmospheric pressure | decision making aid, automatic actions performed by robots | scale, sensors, robot, actor, camera | OODA cycle: observe, orient, decide, act | 3 |
Cazier (beeculture.com) [39] | 2018 | weight, temperature, humidity, sound, images, knowledge of good beekeeping practices | alerts, suggestions of actions, management practices, optimisation (colony health, production, pollination performance) | use of the beeXML standard, continuous learning and integration of good practices | 3 | |
Mrozek et al. [40] | 2021 | stream video of bees at hive entrance | bee infestation status | RaspberryPi, Camera, GSM modem | Neural Networks, Google Cloud, AWS Cloud, AWS Lambda, AWS DynamoDB | 3 |
Debauche et al. [41] | 2018 | external data: temperature, humidity, atmospheric pressure, luminosity. Beehive internal data: humidity, temperature, acceleration, air contaminants | monitoring of bees behaviour, information for comprehension and analysis of the extinction of bees, colony health state | Network protocols (LPWAN, 3GPP), sensors, PyCom microcontroller | Lambda Architecture, REDIS, PostgreSQL, Apache kafka, Apache Samza, HDFS, Docker | 3 |
Zetterman [42] | 2018 | temperature, weight, sound, humidity, number of bees, acceleration, CO2 | data collection, monitoring, graphics | sensors | web application | 3 |
Latioui et al. [43] | 2019 | expert rules, social network data (images, texts), beekeeper voice, GPS | alerts, recommendations, voice detection | sensors | Chatbot, deep learning, text mining | 3 |
Functionality | Interested Beekeepers (%) |
---|---|
Data visualization | 81 |
Alert service | 78 |
Voice input | 77 |
Data storage | 72 |
Data sharing between beekeepers | 71 |
Maturity Model Levels | 1 | 2 | 3 | 4 | 5 |
---|---|---|---|---|---|
Beehive connected | - | - | yes | - | - |
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Huet, J.-C.; Bougueroua, L.; Kriouile, Y.; Wegrzyn-Wolska, K.; Ancourt, C. Digital Transformation of Beekeeping through the Use of a Decision Making Architecture. Appl. Sci. 2022, 12, 11179. https://doi.org/10.3390/app122111179
Huet J-C, Bougueroua L, Kriouile Y, Wegrzyn-Wolska K, Ancourt C. Digital Transformation of Beekeeping through the Use of a Decision Making Architecture. Applied Sciences. 2022; 12(21):11179. https://doi.org/10.3390/app122111179
Chicago/Turabian StyleHuet, Jean-Charles, Lamine Bougueroua, Yassine Kriouile, Katarzyna Wegrzyn-Wolska, and Corinne Ancourt. 2022. "Digital Transformation of Beekeeping through the Use of a Decision Making Architecture" Applied Sciences 12, no. 21: 11179. https://doi.org/10.3390/app122111179
APA StyleHuet, J.-C., Bougueroua, L., Kriouile, Y., Wegrzyn-Wolska, K., & Ancourt, C. (2022). Digital Transformation of Beekeeping through the Use of a Decision Making Architecture. Applied Sciences, 12(21), 11179. https://doi.org/10.3390/app122111179