UAV-thermal imaging: A technological breakthrough for monitoring and quantifying crop abiotic stress to help sustain productivity on sodic soils – A case review on wheat

Das, Sumanta; Chapman, Scott C.; Christopher, Jack; Choudhury, Malini Roy; Menzies, Neal W.; Apan, Armando; Dang, Yash P.

doi:10.1016/j.rsase.2021.100583

Cited by 17 publications

(12 citation statements)

References 99 publications

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“…Layer 4 (defuzzification): Each node of this layer calculates the weighted consequent values of rules as indicated in Eq. (17).…”

Section: Anfis-smcmentioning

confidence: 99%

“…Unmanned aerial vehicles (UAVs) have received extensive attention from the engineering community due to their promising potential in military and civilian applications, such as disaster rescue [1,2], intelligent surveillance [3,4], smart agriculture [5][6][7][8], aerial photography [9,10], mapping [11], industry inspection [12,13] logistics [14][15][16], forest fire-fighting [16], and crop disease monitoring [17][18][19], etc.. Numerous types of UAVs have been fabricated and developed as research platforms, among which the quadcopters (also called quadrotors) are the most popular type because of their considerable merits including autonomous flight, easy construction, simple maintenance, low cost, onboard vision, vertical take-off and landing [20], hovering ability, and manoeuvrability.…”

Section: Introductionmentioning

confidence: 99%

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Adaptive Neuro-Fuzzy Inference System Based on Sliding Mode Control for Quadcopter Trajectory Tracking with the Presence of External Disturbance

Darwito¹,

Indayu²

2023

JISC

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Objective of this study is to develop a novel, effective, and robust Sliding Mode Control (SMC) method for quadcopters (also called quadrotors) based on Adaptive Neuro-Fuzzy Inference System (ANFIS) for the purposes of enhancing trajectory tracking performance and realizing safe and reliable flight. In the paper, the ANFIS was combined with SMC technology to propose a scheme of adaptive robust controller, which is composed of three sub-controllers, x position controller, y position controller, and z position (altitude) controller. The proposed method can realize position tracking control of quadcopters in the presence of external disturbances. With the help of ANFIS, an adjustable gain rather than a fixed gain was established for the SMC controller, the optimal output could be attained based on a set of rules, and the position control gain was updated by ANFIS, enabling the SMC to adapt to environmental changes. Through modelling, simulation and comparison, experimental data verified that the proposed ANFIS-SMC controller outperformed conventional SMC controller in terms of convergence speed, robustness, accuracy, and stability with a maximum mean error of 0.125 meters in trajectory tracking. Research findings of this paper could contribute to the development of robust and responsive control strategies for Unmanned aerial vehicles (UAVs) trajectory tracking by providing valuable insights into the design of more effective and efficient control systems for UAVs, particularly in the context of dynamic environmental conditions.

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“…Layer 4 (defuzzification): Each node of this layer calculates the weighted consequent values of rules as indicated in Eq. (17).…”

Section: Anfis-smcmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Adaptive Neuro-Fuzzy Inference System Based on Sliding Mode Control for Quadcopter Trajectory Tracking with the Presence of External Disturbance

Darwito¹,

Indayu²

2023

JISC

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“…World's need of agricultural product is predicted to rise by 50% in 30 years. Further, the agricultural production should increase as high as 2.4 % per year [1]. On the other side, the expansion of land used for agriculture is very little.…”

Section: Introductionmentioning

confidence: 99%

The Smart Agriculture based on Reconstructed Thermal Image

Ismail

2022

j.inf.tech.comp.eng

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The utilization of thermal image in supporting precision agriculture is tremendous nowadays. There are many applications of thermal images in agricultural fields, such as detecting crop water stress, monitoring of free-range rabbits, measuring of crop canopy temperature and so on. Furthermore, the importance of thermal camera became the urgent need of perform the smart agriculture. Otherwise, the price of thermal camera is very expensive todays. Then, this kind of camera is not easy to find in the market. Therefore, it makes the utilization of implementation thermal images difficult. In order to handle this problem, the proposed method intends to generate thermal image from visible images. Further, the thermal information concerning with the agriculture, especially the fertility of leaves in paddy fields and the water stress can be monitored. The proposed method uses deep learning architecture to learn the thermal and visible image dataset. It applies Generative Adversarial Network architecture. This GAN pre-trained model trained using 150 images of training dataset and tested using many images of testset. The obtained model is used for generating thermal images from visible images. The results show the constructed thermal image has high accuracy. The assessment metric uses SSIM and PSNR methods. Their indexes show that the results have the high accuracy. The visual assessment shows the reconstructed thermal images also have high precision. Finally, the constructed thermal images can be implemented in smart agriculture purposes.

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“…However, these single-point temperature instruments may require contacting the targeted object [ 12 ]. In contrast, thermal imaging (TI) or infrared thermography, a two-dimensional, noninvasive, and noncontact technique, has recently been introduced to quantify the changes in surface temperature of objects with a high spatial and temporal resolution in real-time [ 13 , 14 , 15 ]. Thermal images are produced with infrared (IR) cameras that collect electromagnetic radiation (wavelengths of 0.9–14 µm) emitted by the targeted objects/scenes [ 16 ].…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Low-Cost Smartphone-Based Infrared Cameras to Assess the Cooling and Refrigerated Storage Temperatures of Fresh Produce

Yang

Kumar

Solval

2022

Foods

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Populations of pathogens may increase in fresh produce when subjected to temperature abuse. Smartphone-based infrared (SBIR) cameras are potential alternatives for temperature measurements of fresh produce during postharvest handling and storage. This study compared the performance of SBIR cameras (FLIR and Seek) against conventional temperature acquisition devices for evaluating fresh produce’s simulated hydrocooling and storage conditions. First, thermal images of fresh produce were obtained with SBIR cameras and handheld thermal imagers at ~35 °C, ~20 °C, and ~4 °C to simulate outdoor, packinghouse, and refrigerated environments, respectively. Next, fresh produce was incubated at ~42 °C for 20 h and immersed in chilled water for a hydrocooling simulation. Then, boxes containing cooled fresh produce were stored in a walk-in cooler at different heights for three days. FLIR SBIR cameras were more effective at capturing thermal images of fresh produce than Seek SBIR cameras in all evaluated conditions. More importantly, SBIR cameras accurately acquired temperature profiles of fresh produce during simulated hydrocooling and cold storage. Additionally, the accuracy and quality of thermal images obtained with FLIR cameras were better than those obtained with Seek cameras. The study demonstrated that SBIR cameras are practical, easy-to-use, and cost-effective devices to monitor fresh produce’s temperature during postharvest handling and storage.

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UAV-thermal imaging: A technological breakthrough for monitoring and quantifying crop abiotic stress to help sustain productivity on sodic soils – A case review on wheat

Cited by 17 publications

References 99 publications

Adaptive Neuro-Fuzzy Inference System Based on Sliding Mode Control for Quadcopter Trajectory Tracking with the Presence of External Disturbance

Adaptive Neuro-Fuzzy Inference System Based on Sliding Mode Control for Quadcopter Trajectory Tracking with the Presence of External Disturbance

The Smart Agriculture based on Reconstructed Thermal Image

Evaluation of Low-Cost Smartphone-Based Infrared Cameras to Assess the Cooling and Refrigerated Storage Temperatures of Fresh Produce

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