GIS-based volunteer cotton habitat prediction and plant-level detection with UAV remote sensing

Wang, Tianyi; Mei, Xiaohan; Thomasson, J. Alex; Yang, Cunjian; Han, Xiongzhe; Yadav, Pappu Kumar; Shi, Yeyin

doi:10.1016/j.compag.2021.106629

Cited by 15 publications

(11 citation statements)

References 28 publications

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“…Knowledge of repair and maintenance is also a requirement [64]. Moreover, most governments require special licences for UAV use, and they limit certain aspects of flying, such as flying height ( [71,93]), and flying over inhabited areas and beyond visual lines [88].…”

Section: Uav Challengesmentioning

confidence: 99%

Complementary Use of Ground-Based Proximal Sensing and Airborne/Spaceborne Remote Sensing Techniques in Precision Agriculture: A Systematic Review

Alexopoulos,

Koutras,

Ali

et al. 2023

Agronomy

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As the global population continues to increase, projected to reach an estimated 9.7 billion people by 2050, there will be a growing demand for food production and agricultural resources. Transition toward Agriculture 4.0 is expected to enhance agricultural productivity through the integration of advanced technologies, increase resource efficiency, ensure long-term food security by applying more sustainable farming practices, and enhance resilience and climate change adaptation. By integrating technologies such as ground IoT sensing and remote sensing, via both satellite and Unmanned Aerial Vehicles (UAVs), and exploiting data fusion and data analytics, farming can make the transition to a more efficient, productive, and sustainable paradigm. The present work performs a systematic literature review (SLR), identifying the challenges associated with UAV, Satellite, and Ground Sensing in their application in agriculture, comparing them and discussing their complementary use to facilitate Precision Agriculture (PA) and transition to Agriculture 4.0.

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Section: Uav Challengesmentioning

confidence: 99%

Complementary Use of Ground-Based Proximal Sensing and Airborne/Spaceborne Remote Sensing Techniques in Precision Agriculture: A Systematic Review

Alexopoulos,

Koutras,

Ali

et al. 2023

Agronomy

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Section: Crop Pest Classificationmentioning

confidence: 99%

“…In 2022, Wang et al, [69] proposed UAV remote sensing and Cotton habitat estimate by helpers and detection at the farm level based on GIS. GIS network tool used to determine cotton transit routes and developed to locate and display prospective volunteer Their potential use in the early identification of RLB employing multispectral sensors on various platforms is prevented by the restrictions associated with the ambiguity of the sensitivity of sensitive features.…”

Section: Cotton Pest Classificationmentioning

confidence: 99%

“…Relevance filtering by RF and the connection linkages from the input layer to the hidden layer, in real numbers of BP methods, reveal that the seven distinctive indicators were responsive to D. In BP neural networks, a "over-fitting" issue occurs, but the RF technique was more resistant, and BP neural networks have a better identification effect. As a result, It was possible to use the RF technique for pest impact and higher variables which Indicates that ratio of collecting information from a variety [69] To provide a GIS framework to quickly find stray cotton plants in cotton production, saving time and money.…”

Section: Pest Detection By Random Forest (Rf)mentioning

confidence: 99%

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Survey on crop pest detection using deep learning and machine learning approaches

Chithambarathanu

Jeyakumar

2023

Multimed Tools Appl

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The most important elements in the realm of commercial food standards are effective pest management and control. Crop pests can make a huge impact on crop quality and productivity. It is critical to seek and develop new tools to diagnose the pest disease before it caused major crop loss. Crop abnormalities, pests, or dietetic deficiencies have usually been diagnosed by human experts. Anyhow, this was both costly and time-consuming. To resolve these issues, some approaches for crop pest detection have to be focused on. A clear overview of recent research in the area of crop pests and pathogens identification using techniques in Machine Learning Techniques like Random Forest (RF), Support Vector Machine (SVM), and Decision Tree (DT), Naive Bayes (NB), and also some Deep Learning methods like Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), Deep convolutional neural network (DCNN), Deep Belief Network (DBN) was presented. The outlined strategy increases crop productivity while providing the highest level of crop protection. By offering the greatest amount of crop protection, the described strategy improves crop efficiency. This survey provides knowledge of some modern approaches for keeping an eye on agricultural fields for pest detection and contains a definition of plant pest detection to identify and categorise citrus plant pests, rice, and cotton as well as numerous ways of detecting them. These methods enable automatic monitoring of vast domains, therefore lowering human error and effort.

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“…Likewise, current advances in microelectronics and proximity/optical sensors [ 25 , 26 ], coupled with the availability of detailed geographic information systems mapping [ 27 , 28 , 29 ], contributed to the development of micro-UAVs [ 30 ] that can be operated autonomously at very low altitudes within dense urban locations and provide accurate intelligence data.…”

Section: Introductionmentioning

confidence: 99%

Improvement of Hexacopter UAVs Attitude Parameters Employing Control and Decision Support Systems

Stamate

Pupăză

Nicolescu

et al. 2023

Sensors

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Today, there is a conspicuous upward trend for the development of unmanned aerial vehicles (UAVs), especially in the field of multirotor drones. Their advantages over fixed-wing aircrafts are that they can hover, which allows their usage in a wide range of remote surveillance applications: industrial, strategic, governmental, public and homeland security. Moreover, because the component market for this type of vehicles is in continuous growth, new concepts have emerged to improve the stability and reliability of the multicopters, but efficient solutions with reduced costs are still expected. This work is focused on hexacopter UAV tests carried out on an original platform both within laboratory and on unrestricted open areas during the start–stop manoeuvres of the motors to verify the operational parameters, hover flight, the drone stability and reliability, as well as the aerodynamics and robustness at different wind speeds. The flight parameters extracted from the sensor systems’ comprising accelerometers, gyroscopes, magnetometers, barometers, GPS antenna and EO/IR cameras were analysed, and adjustments were performed accordingly, when needed. An FEM simulation approach allowed an additional decision support platform that expanded the experiments in the virtual environment. Finally, practical conclusions were drawn to enhance the hexacopter UAV stability, reliability and manoeuvrability.

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GIS-based volunteer cotton habitat prediction and plant-level detection with UAV remote sensing

Cited by 15 publications

References 28 publications

Complementary Use of Ground-Based Proximal Sensing and Airborne/Spaceborne Remote Sensing Techniques in Precision Agriculture: A Systematic Review

Complementary Use of Ground-Based Proximal Sensing and Airborne/Spaceborne Remote Sensing Techniques in Precision Agriculture: A Systematic Review

Survey on crop pest detection using deep learning and machine learning approaches

Improvement of Hexacopter UAVs Attitude Parameters Employing Control and Decision Support Systems

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