droneONtrap project – Integration of IoT technologies and drones for health status and pests monitoring of orchards

Ristorto, Gianluca; Bojeri, Alex; Scarton, Gabriele; Giannotta, Giovanni; Guglieri, Giorgio

doi:10.1109/metroagrifor52389.2021.9628760

Cited by 4 publications

(2 citation statements)

References 5 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Future research should further explore the use of IoT systems and UAVs with environmental parameters such as temperature and humidity acquired with the ground segment gadgets embedded. Multi-sensor fusion for thorough monitoring using sensors with different bands should also be explored for accurate image analysis of damages inflicted on fruits and plants in orchard management [92]. Research should also look more into the development of machine learning algorithms that predict conditions in which early pest and disease outbreak infestations can occur in orchards based on IoT datasets.…”

Section: Future Researchmentioning

confidence: 99%

A Review on Automated Detection and Assessment of Fruit Damage Using Machine Learning

Safari,

Nakatumba-Nabende,

Nakasi

et al. 2024

IEEE Access

View full text Add to dashboard Cite

Automation improves the quality of fruits through quick and accurate detection of pest and disease infections thus contributing to the country's economic growth and productivity. Although humans can identify the fruit damage caused by pests and diseases, methods used are inconsistent, time-consuming, and variable. The surface features of fruits typically observed by consumers who seek their health benefits, affect their market value. The issue of pest and disease infections further deteriorates fruits' quality, becoming a mounting stressor on farmers as they affect the potential income that could have been realised from production, processing and export. This article reviews various studies on detecting and classifying damages in fruits. Specifically, we review articles where state-of-the-art approaches under segmentation, image processing, machine learning, and deep learning have proved effective in developing automated systems that address hurdles associated with manual methods of assessing damage using visual experiences. This survey reviews 32 Journal and Conference articles spanning 13 years obtained electronically through Google Scholar, Scopus, IEEE, ScienceDirect, and general internet searches. This survey further presents a detailed discussion of related studies done in the past while emphasizing their strengths and limitations and presenting future research directions. It also reveals that much as the use of automated detection and classification of fruit damage has yielded promising results in the horticulture industry, more research is still needed with systems required to fully automate the detection and classification processes, especially those that are mobile phone-based towards addressing occlusion challenges.INDEX TERMS fruit damage detection, classification, deep learning, image analysis and segmentation.

show abstract

Section: Future Researchmentioning

confidence: 99%

A Review on Automated Detection and Assessment of Fruit Damage Using Machine Learning

Safari,

Nakatumba-Nabende,

Nakasi

et al. 2024

IEEE Access

View full text Add to dashboard Cite

show abstract

“…Quantifying moths in these systems is time-consuming and requires specific identification skills. Proposed solutions primarily involve machine- or computer-vision methods [ 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 ]. While they have a range of advantages, the main drawbacks for their field deployment are the increased system cost and high bandwidth requirements in locations where high-speed data are generally unavailable.…”

Section: Introductionmentioning

confidence: 99%

A Multimodal Sensing Platform for Interdisciplinary Research in Agrarian Environments

Reynolds

Williams

Martin

et al. 2022

Sensors

View full text Add to dashboard Cite

Agricultural and environmental monitoring programs often require labor-intensive inputs and substantial costs to manually gather data from remote field locations. Recent advances in the Internet of Things enable the construction of wireless sensor systems to automate these remote monitoring efforts. This paper presents the design of a modular system to serve as a research platform for outdoor sensor development and deployment. The advantages of this system include low power consumption (enabling solar charging), the use of commercially available electronic parts for lower-cost and scaled up deployments, and the flexibility to include internal electronics and external sensors, allowing novel applications. In addition to tracking environmental parameters, the modularity of this system brings the capability to measure other non-traditional elements. This capability is demonstrated with two different agri- and aquacultural field applications: tracking moth phenology and monitoring bivalve gaping. Collection of these signals in conjunction with environmental parameters could provide a holistic and context-aware data analysis. Preliminary experiments generated promising results, demonstrating the reliability of the system. Idle power consumption of 27.2 mW and 16.6 mW for the moth- and bivalve-tracking systems, respectively, coupled with 2.5 W solar cells allows for indefinite deployment in remote locations.

show abstract

Deep Reinforcement Learning for AoI Minimization in UAV-Aided Data Collection for WSN and IoT Applications: A Survey

Amodu,

Jarray,

Azlina Raja Mahmood

et al. 2024

IEEE Access

View full text Add to dashboard Cite

droneONtrap project – Integration of IoT technologies and drones for health status and pests monitoring of orchards

Cited by 4 publications

References 5 publications

A Review on Automated Detection and Assessment of Fruit Damage Using Machine Learning

A Review on Automated Detection and Assessment of Fruit Damage Using Machine Learning

A Multimodal Sensing Platform for Interdisciplinary Research in Agrarian Environments

Deep Reinforcement Learning for AoI Minimization in UAV-Aided Data Collection for WSN and IoT Applications: A Survey

Contact Info

Product

Resources

About