UAV Aided Irrigation Using Object Detection Through Wireless Communication Technology

Ajith, G.S.; Bharadwaj, Chinmoy; Kumar, T Senthil; Nag, Tridibesh; Gururaj, C.

doi:10.1109/icoei.2018.8553787

Cited by 5 publications

(3 citation statements)

References 6 publications

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“…Some algorithms are designed for route planning or precise mapping through UAVs [37,38]. Other solutions incorporate cameras and sensors to assess land and irrigation practices or to evaluate crop quality [39][40][41][42][43]. While these solutions enable the analysis of crop growth, none of them specifically address the identification of damaged plants.…”

Section: Related Workmentioning

confidence: 99%

Quantitative and Qualitative Analysis of Agricultural Fields Based on Aerial Multispectral Images Using Neural Networks

Strzępek,

Salach,

Trybus

et al. 2023

Sensors

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This article presents an integrated system that uses the capabilities of unmanned aerial vehicles (UAVs) to perform a comprehensive crop analysis, combining qualitative and quantitative evaluations for efficient agricultural management. A convolutional neural network-based model, Detectron2, serves as the foundation for detecting and segmenting objects of interest in acquired aerial images. This model was trained on a dataset prepared using the COCO format, which features a variety of annotated objects. The system architecture comprises a frontend and a backend component. The frontend facilitates user interaction and annotation of objects on multispectral images. The backend involves image loading, project management, polygon handling, and multispectral image processing. For qualitative analysis, users can delineate regions of interest using polygons, which are then subjected to analysis using the Normalized Difference Vegetation Index (NDVI) or Optimized Soil Adjusted Vegetation Index (OSAVI). For quantitative analysis, the system deploys a pre-trained model capable of object detection, allowing for the counting and localization of specific objects, with a focus on young lettuce crops. The prediction quality of the model has been calculated using the AP (Average Precision) metric. The trained neural network exhibited robust performance in detecting objects, even within small images.

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Section: Related Workmentioning

confidence: 99%

Quantitative and Qualitative Analysis of Agricultural Fields Based on Aerial Multispectral Images Using Neural Networks

Strzępek,

Salach,

Trybus

et al. 2023

Sensors

View full text Add to dashboard Cite

show abstract

“…The abovementioned sensors used for precision irrigation require particular hardware for wired and wireless connections to buried sensors, which may lose signal due to disconnection issues (Al-Naji et al, 2021). To avoid using SMS buried in the soil, novel techniques have been applied to manage irrigation by using optical sensors such as drones, UAV, and red/green/blue (RGB) cameras (Ajith et al, 2018). UAV-based remote sensing is a breakthrough technology that has been extensively exploited in smart irrigation over a large scale due to its cost-effectiveness, easy construction procedures, convenient transportation, high flexibility, short operating cycle, and relatively high resolution (Boursianis et al, 2020;Shi et al, 2019).…”

Section: Optical Sensors: Plant-based Irrigation Managementmentioning

confidence: 99%

Section: Smart Strategies For Precision Irrigationmentioning

confidence: 99%

A review on smart irrigation management strategies and their effect on water savings and crop yield

Touil

Richa

Fizir

et al. 2022

Irrigation and Drainage

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Appropriate irrigation water management has attracted a considerable degree of attention because it is crucial to guarantee water and food security worldwide. A smart irrigation system is applied for the rational use of water in agricultural fields and for maximizing crop yield. Thus, the high demand for water resources is decreased, and the adverse environmental impacts of irrigation are reduced. Different irrigation approaches were developed to reduce overirrigation by assessing the soil moisture content or crop water stress index. This paper aims to review conventional and smart irrigation technologies and discuss their effect on water savings, yield, and crop quality. The analysis of prominent studies highlighted that the water use efficiency based on soil moisture sensors depends on the volumetric moisture content threshold value set by farmers. Soil moisture sensor controllers, evapotranspiration controllers, and rain sensors have been shown to save 20%-92%, 20%-71%, and 7%-50% of water, respectively, while maintaining crop growth and quality. The optical sensor-based system showed a better ability to assess crop and soil variability in the field than the on-site measurements. The conventional controlled deficit irrigation technique was found to decrease the crop yield to less than 25% with water savings of up to 13%.

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Time-Dependent Mission Planning of Hybrid Multirotors In Surveillance Operations

Ghadiry

2020

2020 12th International Conference on Electrical Engineering (ICEENG)

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UAV Aided Irrigation Using Object Detection Through Wireless Communication Technology

Cited by 5 publications

References 6 publications

Quantitative and Qualitative Analysis of Agricultural Fields Based on Aerial Multispectral Images Using Neural Networks

Quantitative and Qualitative Analysis of Agricultural Fields Based on Aerial Multispectral Images Using Neural Networks

A review on smart irrigation management strategies and their effect on water savings and crop yield

Time-Dependent Mission Planning of Hybrid Multirotors In Surveillance Operations

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