AgriSegNet: Deep Aerial Semantic Segmentation Framework for IoT-Assisted Precision Agriculture

Anand, Tanmay; Sinha, Soumendu; Mandal, Murari; Chamola, Vinay; Yu, F. Richard

doi:10.1109/jsen.2021.3071290

Cited by 102 publications

(19 citation statements)

References 39 publications

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“…The presented mechanism would initially compute the volume of water for every irrigation related to the highly trained irrigation method integrated with atmosphere data like light intensity, humidity or air temperature, soil temperature or humidity, etc., and irrigate the crops mechanically through the low-power and long-distance wireless LoRa P2P network. In [15], the authors devise a DL structure AgriSegNet for automated identification of farmland anomaly utilizing multiscale attention semantic segmentation of drone images. This presented technique method will be helpful in farmland monitoring and raise the efficiency of accurate agricultural methods.…”

Section: Related Workmentioning

confidence: 99%

Modeling of Sensor Enabled Irrigation Management for Intelligent Agriculture Using Hybrid Deep Belief Network

Yonbawi¹,

Alahmari²,

Raju³

et al. 2023

Computer Systems Science and Engineering

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Artificial intelligence (AI) technologies and sensors have recently received significant interest in intellectual agriculture. Accelerating the application of AI technologies and agriculture sensors in intellectual agriculture is urgently required for the growth of modern agriculture and will help promote smart agriculture. Automatic irrigation scheduling systems were highly required in the agricultural field due to their capability to manage and save water deficit irrigation techniques. Automatic learning systems devise an alternative to conventional irrigation management through the automatic elaboration of predictions related to the learning of an agronomist. With this motivation, this study develops a modified black widow optimization with a deep belief network-based smart irrigation system (MBWODBN-SIS) for intelligent agriculture. The MBWODBN-SIS algorithm primarily enables the Internet of Things (IoT) based sensors to collect data forwarded to the cloud server for examination purposes. Besides, the MBWODBN-SIS technique applies the deep belief network (DBN) model for different types of irrigation classification: average, high needed, highly not needed, and not needed. The MBWO algorithm is used for the hyperparameter tuning process. A wideranging experiment was conducted, and the comparison study stated the enhanced outcomes of the MBWODBN-SIS approach to other DL models with maximum accuracy of 95.73%.

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Section: Related Workmentioning

confidence: 99%

Modeling of Sensor Enabled Irrigation Management for Intelligent Agriculture Using Hybrid Deep Belief Network

Yonbawi¹,

Alahmari²,

Raju³

et al. 2023

Computer Systems Science and Engineering

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“…Different percentages of pixel-wise annotated images i.e., 50%, 40%, and 30% were used as labelled data to the discriminator during semi-supervised training. In a network developed by Anand et al [83], two image scales are used for training and three image scales are used for prediction, which referred as a hierarchical model. Using image attributes extracted from the lower scale image, it derived a pixel-wise dense relative attention between the lower and higher image scales.…”

Section: Algorithms and Classification Techniques For Weed Mappingmentioning

confidence: 99%

How Can Unmanned Aerial Vehicles Be Used for Detecting Weeds in Agricultural Fields?

et al. 2021

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Weeds are among the most harmful abiotic factors in agriculture, triggering significant yield loss worldwide. Remote sensing can detect and map the presence of weeds in various spectral, spatial, and temporal resolutions. This review aims to show the current and future trends of UAV applications in weed detection in the crop field. This study systematically searched the original articles published from 1 January 2016 to 18 June 2021 in the databases of Scopus, ScienceDirect, Commonwealth Agricultural Bureaux (CAB) Direct, and Web of Science (WoS) using Boolean string: “weed” AND “Unmanned Aerial Vehicle” OR “UAV” OR “drone”. Out of the papers identified, 144 eligible studies did meet our inclusion criteria and were evaluated. Most of the studies (i.e., 27.42%) on weed detection were carried out during the seedling stage of the growing cycle for the crop. Most of the weed images were captured using red, green, and blue (RGB) camera, i.e., 48.28% and main classification algorithm was machine learning techniques, i.e., 47.90%. This review initially highlighted articles from the literature that includes the crops’ typical phenology stage, reference data, type of sensor/camera, classification methods, and current UAV applications in detecting and mapping weed for different types of crop. This study then provides an overview of the advantages and disadvantages of each sensor and algorithm and tries to identify research gaps by providing a brief outlook at the potential areas of research concerning the benefit of this technology in agricultural industries. Integrated weed management, coupled with UAV application improves weed monitoring in a more efficient and environmentally-friendly way. Overall, this review demonstrates the scientific information required to achieve sustainable weed management, so as to implement UAV platform in the real agricultural contexts.

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“…Deep learning models are successfully applied in different computer vision and remote sensing tasks such as object detection (Wu et al, 2020;Zhao et al, 2019), image segmentation (Ghosh et al, 2019;Wang et al, 2019), human activity monitoring (Toshev and Szegedy, 2014;Zheng et al, 2019), object tracking (Ciaparrone et al, 2020;Zhai et al, 2018) and also the semantic segmentation. Semantic segmentation is the essential input for plenty of applications in computer vision and remote sensing, including scene understanding for autonomous driving (Siam et al, 2018), augmented reality (Ko and Lee, 2020), and different environmental monitoring applications such as precision agriculture (Anand et al, 2021), change detection (Venugopal, 2020), and urban mapping and monitoring (Du et al, 2021). In urban remote sensing, discriminating different elements of a city, including different kinds of buildings, paved areas, water bodies, trees and grasslands, cars and clutter are challenging due to variations in shapes, structures, textures, and colours differences (Diakogiannis et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

Building Detection From Aerial Imagery Using Inception Resnet Unet and Unet Architectures

Aghayari¹,

Hadavand²,

Niazi³

et al. 2023

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. Buildings are one of the key components in change detection, urban planning, and monitoring. The automatic extraction of the building from high-resolution aerial imagery is still challenging due to the variations in their shapes, structures, textures, and colours. Recently, the convolutional neural networks (CNN) show a significant improvement in object detection and extraction that surpasses other methods. To extract building, in this paper two segmentation architectures, the UNet and the Inception ResNet UNet are implemented and then tested on the Inria aerial image datasets. The Inception ResNet UNet utilizes the Inception architecture and residual blocks. This makes the model wide and deep, though there are a few differences between numbers of UNet and Inception ResNet UNet parameters. The analyses show that UNet has a high rate of metrics in the training progress. However, on the unseen dataset, Inception ResNet UNet extracts buildings more accurately (97.95% accuracy and 0.96 in the dice metric) in comparison with UNet (94.30% accuracy and 0.55 in the dice metric).

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AgriSegNet: Deep Aerial Semantic Segmentation Framework for IoT-Assisted Precision Agriculture

Cited by 102 publications

References 39 publications

Modeling of Sensor Enabled Irrigation Management for Intelligent Agriculture Using Hybrid Deep Belief Network

Modeling of Sensor Enabled Irrigation Management for Intelligent Agriculture Using Hybrid Deep Belief Network

How Can Unmanned Aerial Vehicles Be Used for Detecting Weeds in Agricultural Fields?

Building Detection From Aerial Imagery Using Inception Resnet Unet and Unet Architectures

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