Evaluation of Unmanned Aerial Vehicles for Precision Agriculture Based on Integrated Fuzzy Decision-Making Approach

Jasim, Ali Najm; Fourati, Lamia Chaari; Albahri, O. S.

doi:10.1109/access.2023.3294094

Cited by 7 publications

(2 citation statements)

References 61 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…These studies underscore the growing trend of leveraging sophisticated algorithms and vision technologies in agriculture to enhance efficiency and precision. In a similar vein, the work by Yamazaki and Nakajima [3], and Jasim et al [4] offer insights into the energy consumption models in smart agriculture and the evaluation of unmanned aerial vehicles (UAVs), respectively. These studies reflect the sector's increasing reliance on sustainable energy and aerial technology, highlighting the importance of energy efficiency and aerial surveillance in modern farming practices.…”

Section: In-depth Review Of Models Used For Multimodal Analysis Of Cr...mentioning

confidence: 99%

PAMICRM: Improving Precision Agriculture Through Multimodal Image Analysis for Crop Water Requirement Estimation Using Multidomain Remote Sensing Data Samples

Munaganuri,

Rao

2024

IEEE Access

View full text Add to dashboard Cite

The growing necessity for sustainable agriculture in the face of escalating global food demands and climate change underscores the importance of optimizing crop water usage. Current methodologies for estimating crop water requirements, primarily relying on traditional remote sensing and empirical models, face limitations in precision, adaptability to diverse crop types, and dynamic environmental conditions. This paper introduces an innovative machine learning model designed to accurately estimate crop water requirements and optimize irrigation scheduling using remote sensing data samples. The model's robustness stems from its sophisticated pre-processing technique, employing a Grey Wolf Optimized (GWO)-based Non-Local Means (NLM) algorithm for denoising images sourced from various remote sensing technologies. Subsequently, these refined images undergo a transformation into multidomain features through the integration of a Vision Transformer, Fourier, Entropy, Color Map, and Gabor Maps. This multi-faceted approach ensures a comprehensive analysis of the data, capturing intricate details pertinent to crop water requirements.A key innovation in our model is the implementation of the Dual Elephant Herding Optimization (DEHO) Process. This optimizer adeptly selects features by minimizing intra-class differences and maximizing interclass variance, thereby enhancing the model's discriminative capabilities. The selected features are then classified into distinct irrigation schedule classes using a Deep Dyna Q Graph Convolutional Network (DDQGCN). This network not only augments the efficiency of classifying images from diverse sources but also bolsters the model's capability to adapt to varying crop types and environmental conditions. The efficacy of the proposed model is further augmented by incorporating vector autoregressive moving average with eXogenous inputs (VARMAx) algorithms. This addition enables the conversion of output into temporal features, facilitating precise predictions of soil quality and the dynamic adjustment of irrigation schedules. Empirical testing of this model across different geographies on mango, rice, and cotton crops demonstrated significant improvements over existing methods. The model exhibited an 8.5% increase in precision, 8.3% in accuracy, 4.9% in recall, 5.5% in Area Under the Curve (AUC), and 4.5% in specificity, alongside a 3.5% reduction in response delays. Particularly noteworthy is the model's enhanced pre-emption capabilities, reflected in a 2.9% higher precision of pre-emption, 2.5% greater accuracy, 1.5% increased recall, 2.4% higher AUC of pre-emption, 1.5% improved specificity, and a 1.9% reduction in delay compared to existing methodologies.

show abstract

Section: In-depth Review Of Models Used For Multimodal Analysis Of Cr...mentioning

confidence: 99%

PAMICRM: Improving Precision Agriculture Through Multimodal Image Analysis for Crop Water Requirement Estimation Using Multidomain Remote Sensing Data Samples

Munaganuri,

Rao

2024

IEEE Access

View full text Add to dashboard Cite

show abstract

“…The use of drones, also known as unmanned aerial vehicles (UAVs), provides significant benefits to precision agriculture by reducing labor needs and achieving significant time savings [45], [46]. UAV image capture is more effective and userfriendly [47].…”

Section: Related Workmentioning

confidence: 99%

A Novel Method for Optimizing Color Selection Using the Hadamard Product Technique

Kusnandar,

Santoso,

Surendro

2023

IEEE Access

View full text Add to dashboard Cite

Numerous research endeavors have employed the color selection procedure for a vast array of purposes. Detecting defects in fabrics, calculating the Microbial Community Color Index, analyzing digital color in facilitating fashion design processes and applying color to artworks, calculating canopy cover, and achieving other objectives have been the subject of research. This procedure necessitates intricate steps, intricate calculations, and lengthy computational time. In this study, a novel strategy for optimizing the color selection process using the Hadamard product technique is presented. The HSV color space is optimized by selectively selecting the desired colors and establishing threshold limits for each hue, saturation, and value component. The optimization results demonstrate that the desired colors are perfectly distinguished from other colors. Additionally, the proposed method employs a straightforward, step-by-step procedure that does not require feature extraction. In comparison to previous research, a remarkable increase in computational speed of 1,078.82 times faster has been observed. This improvement is achieved by multiplying each element of the HSV matrix resulting from color selection as opposed to the HSV matrix without selection. This study's findings are applicable not only to plant images but also to all cases requiring color selection under visible light conditions.

show abstract

Evaluation of organizational culture in companies for fostering a digital innovation using q-rung picture fuzzy based decision-making model

Albahri,

Alamoodi,

Deveci

et al. 2023

Advanced Engineering Informatics

View full text Add to dashboard Cite

Evaluation of Unmanned Aerial Vehicles for Precision Agriculture Based on Integrated Fuzzy Decision-Making Approach

Cited by 7 publications

References 61 publications

PAMICRM: Improving Precision Agriculture Through Multimodal Image Analysis for Crop Water Requirement Estimation Using Multidomain Remote Sensing Data Samples

PAMICRM: Improving Precision Agriculture Through Multimodal Image Analysis for Crop Water Requirement Estimation Using Multidomain Remote Sensing Data Samples

A Novel Method for Optimizing Color Selection Using the Hadamard Product Technique

Evaluation of organizational culture in companies for fostering a digital innovation using q-rung picture fuzzy based decision-making model

Contact Info

Product

Resources

About