Design and testing of a crop growth sensor aboard a fixed-wing unmanned aerial vehicle

Yuan, Huali; Yang, Jiayu; Jiang, Xiaoping; Zhu, Yan; Cao, Weixing; Ni, Jun

doi:10.1016/j.compag.2022.106762

Cited by 11 publications

(3 citation statements)

References 21 publications

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“…The multispectral crop growth sensor of the proposed monitoring system is the CGMD302 multispectral crop growth sensor (as shown in Figure 2) developed by Nanjing Agricultural University [38,39]. Its key specifications are monitoring wavelength bands: 730 nm and 815 nm; field of view angle: 27 • ; working height: 1.0-1.5 m above the crop canopy.…”

Section: Vehicle-mounted Crop Growth Sensormentioning

confidence: 99%

Design and Testing of Vehicle-Mounted Crop Growth Monitoring System

Yu,

Cao,

Tian

et al. 2024

Agronomy

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The aim of this study was to overcome the impact of vibration generated by agricultural machinery on the monitoring accuracy and performance of vehicle-mounted crop growth monitoring systems during field operation. This paper developed a vehicle-mounted crop growth monitoring system with vibration damping capability to achieve this goal. The system consists of a multispectral crop growth sensor, signal conditioning module, and truss-type sensor bracket with self-vibration damping capability. The commercial finite element analysis software ABAQUS 6.10 was used to conduct modal and dynamic simulation analyses of the sensor bracket, which indicate that the truss-type sensor bracket can damp vibrations effectively. The p-values (least significant differences) of crop canopy DNRE (red edge normalized difference vegetation index) under different operating speeds (1.5, 3, and 4.5 km/h) are 0.454, 0.703, 0.81, and 0.838, respectively, for four different crop growth stages. In a comparative experiment between the proposed monitoring system and two similar vehicle-mounted sensors (CropSpec and GreenSeeker RT 200) for measuring agronomic parameters at different stages of crop growth, the proposed monitoring system yielded R2 values of 0.8757, 0.7194, and 0.795, respectively, and RMSE values of 0.7157, 2.2341, and 2.0952, respectively, in the tillering stage, jointing stage, and tillering and jointing stage, outperforming the other two sensors.

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Section: Vehicle-mounted Crop Growth Sensormentioning

confidence: 99%

Design and Testing of Vehicle-Mounted Crop Growth Monitoring System

Yu,

Cao,

Tian

et al. 2024

Agronomy

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“…For comparison, we exploited the the coefficient of determination, which is also known as R squared or R 2 . In fact, R 2 is employed to measure variation of the predictions around the GT [46] or quantify the degree of any linear correlation between those quantities. Statistically, R 2 is considered a very informative metric in regression analysis evaluation [47].…”

Section: R 2 Analysismentioning

confidence: 99%

Convolutional neural network regression for low-cost microalgal density estimation

Nguyen¹,

Nguyen²,

Nghiem³

et al. 2022

Preprint

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Density of microalgae is critical information for production of algae in a closed cultivation system since it can be used to optimally control their growth rate, biomass concentration and quality of the products. Given advancement in image processing techniques and thanks to low-cost camera sensors, image based methods are increasingly widely utilized to indirectly estimate the density. Advantages of the image based techniques include being less invasive and more nondestructive and biosecured. However, most of the existing techniques rely on averaging all pixels of a microalgae image, which may eliminate crucial information of their spatial correlation. Therefore, in this work we propose to exploit a convolutional neural network (CNN) to efficiently extract information from the microalgae images, which are then employed to regress the density. Interestingly, the proposed deep CNN regression model accepts the whole color image as its input while the density is calculated in the output. The proposed CNN regression architecture was validated in real-world experiments where the microalgal strain Chlorella vulgaris was cultured and their images were captured by our low-cost camera sensor system. The obtained results demonstrate that the averaged estimation accuracy of the proposed model is 0.55\% (+/- 0.68\%) while the R2 value between the density predictions and the ground truths is 0.9997, which is highly accurate and practical.

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“…DSM includes not only the elevation of the terrain, but also the height of objects such as trees, buildings and structures. These models are widely used in mapping applications, urban planning, environmental monitoring, and civil engineering studies (Yang et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

Determining the height of cotton plants (Gossypium hirsutum L.) Malvaceae f. with the assistance of a drone

Sousa,

Pereira,

Souza

et al. 2024

Braz. J. of Sci.

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The use of drones in crop assessment has become increasingly common. The use of drones presents benefits for evaluating the height of plants in various crops, including cotton, checking nutritional, phytosanitary, genetic parameters, abiotic and biotic effects. The objective of this study was to evaluate the use of drones to aid in the evaluation of trials in cotton cultivation. The study was carried out at the Goiano Institute of Agriculture, located in the municipality of Montividiu, State of Goiás, Brazil. A Phantom 4 Pro drone was used to carry out the mapping and WebODM was used to carry out photogrammetry and obtain a digital model of the surface area in the cotton field. In the experimental design, 4 randomized blocks with 10 treatments were used. The results demonstrated that there was no difference between the four blocks for vegetative index. The digital surface model proved to be efficient in detecting possible differences between the analyzed blocks. This analysis is essential to understand variations in response to cotton cultivation in different blocks and identify possible factors that contribute to these differences.

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Design and testing of a crop growth sensor aboard a fixed-wing unmanned aerial vehicle

Cited by 11 publications

References 21 publications

Design and Testing of Vehicle-Mounted Crop Growth Monitoring System

Design and Testing of Vehicle-Mounted Crop Growth Monitoring System

Convolutional neural network regression for low-cost microalgal density estimation

Determining the height of cotton plants (Gossypium hirsutum L.) Malvaceae f. with the assistance of a drone

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