scholarly journals Wheat Growth Monitoring and Yield Estimation based on Multi-Rotor Unmanned Aerial Vehicle

2020 ◽  
Vol 12 (3) ◽  
pp. 508 ◽  
Author(s):  
Zhaopeng Fu ◽  
Jie Jiang ◽  
Yang Gao ◽  
Brian Krienke ◽  
Meng Wang ◽  
...  
Keyword(s):  
Linear Regression ◽  
Multiple Linear Regression ◽  
Unmanned Aerial Vehicle ◽  
Vegetation Index ◽  
Wheat Yield ◽  
Crop Growth ◽  
Growth Monitoring ◽  
Yield Estimation ◽  
Wheat Growth ◽  
Aerial Vehicle

Leaf area index (LAI) and leaf dry matter (LDM) are important indices of crop growth. Real-time, nondestructive monitoring of crop growth is instructive for the diagnosis of crop growth and prediction of grain yield. Unmanned aerial vehicle (UAV)-based remote sensing is widely used in precision agriculture due to its unique advantages in flexibility and resolution. This study was carried out on wheat trials treated with different nitrogen levels and seeding densities in three regions of Jiangsu Province in 2018–2019. Canopy spectral images were collected by the UAV equipped with a multi-spectral camera during key wheat growth stages. To verify the results of the UAV images, the LAI, LDM, and yield data were obtained by destructive sampling. We extracted the wheat canopy reflectance and selected the best vegetation index for monitoring growth and predicting yield. Simple linear regression (LR), multiple linear regression (MLR), stepwise multiple linear regression (SMLR), partial least squares regression (PLSR), artificial neural network (ANN), and random forest (RF) modeling methods were used to construct a model for wheat yield estimation. The results show that the multi-spectral camera mounted on the multi-rotor UAV has a broad application prospect in crop growth index monitoring and yield estimation. The vegetation index combined with the red edge band and the near-infrared band was significantly correlated with LAI and LDM. Machine learning methods (i.e., PLSR, ANN, and RF) performed better for predicting wheat yield. The RF model constructed by normalized difference vegetation index (NDVI) at the jointing stage, heading stage, flowering stage, and filling stage was the optimal wheat yield estimation model in this study, with an R2 of 0.78 and relative root mean square error (RRMSE) of 0.1030. The results provide a theoretical basis for monitoring crop growth with a multi-rotor UAV platform and explore a technical method for improving the precision of yield estimation.

scholarly journals Rapid Mosaicking of Unmanned Aerial Vehicle (UAV) Images for Crop Growth Monitoring Using the SIFT Algorithm

2019 ◽  
Vol 11 (10) ◽  
pp. 1226 ◽  
Author(s):  
Jianqing Zhao ◽  
Xiaohu Zhang ◽  
Chenxi Gao ◽  
Xiaolei Qiu ◽  
Yongchao Tian ◽  
...  
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Near Infrared ◽  
Similarity Index ◽  
Crop Growth ◽  
Contrast Threshold ◽  
Growth Monitoring ◽  
Scale Invariant ◽  
Sift Algorithm ◽  
Aerial Vehicle ◽  
Uav Images

To improve the efficiency and effectiveness of mosaicking unmanned aerial vehicle (UAV) images, we propose in this paper a rapid mosaicking method based on scale-invariant feature transform (SIFT) for mosaicking UAV images used for crop growth monitoring. The proposed method dynamically sets the appropriate contrast threshold in the difference of Gaussian (DOG) scale-space according to the contrast characteristics of UAV images used for crop growth monitoring. Therefore, this method adjusts and optimizes the number of matched feature point pairs in UAV images and increases the mosaicking efficiency. Meanwhile, based on the relative location relationship of UAV images used for crop growth monitoring, the random sample consensus (RANSAC) algorithm is integrated to eliminate the influence of mismatched point pairs in UAV images on mosaicking and to keep the accuracy and quality of mosaicking. Mosaicking experiments were conducted by setting three types of UAV images in crop growth monitoring: visible, near-infrared, and thermal infrared. The results indicate that compared to the standard SIFT algorithm and frequently used commercial mosaicking software, the method proposed here significantly improves the applicability, efficiency, and accuracy of mosaicking UAV images in crop growth monitoring. In comparison with image mosaicking based on the standard SIFT algorithm, the time efficiency of the proposed method is higher by 30%, and its structural similarity index of mosaicking accuracy is about 0.9. Meanwhile, the approach successfully mosaics low-resolution UAV images used for crop growth monitoring and improves the applicability of the SIFT algorithm, providing a technical reference for UAV application used for crop growth and phenotypic monitoring.

UAV Remote Sensing Assessment of Crop Growth

2021 ◽  
Vol 87 (12) ◽  
pp. 891-899
Author(s):  
Freda Elikem Dorbu ◽  
Leila Hashemi-Beni ◽  
Ali Karimoddini ◽  
Abolghasem Shahbazi
Keyword(s):  
Remote Sensing ◽  
Unmanned Aerial Vehicle ◽  
Vegetation Index ◽  
Near Infrared ◽  
Normalized Difference Vegetation Index ◽  
Crop Growth ◽  
Infrared Sensors ◽  
Digital Elevation ◽  
Aerial Vehicle ◽  
Growth Indicators

The introduction of unmanned-aerial-vehicle remote sensing for collecting high-spatial- and temporal-resolution imagery to derive crop-growth indicators and analyze and present timely results could potentially improve the management of agricultural businesses and enable farmers to apply appropriate solution, leading to a better food-security framework. This study aimed to analyze crop-growth indicators such as the normalized difference vegetation index (NDVI), crop height, and vegetated surface roughness to determine the growth of corn crops from planting to harvest. Digital elevation models and orthophotos generated from the data captured using multispectral, red/green/blue, and near-infrared sensors mounted on an unmanned aerial vehicle were processed and analyzed to calculate the various crop-growth indicators. The results suggest that remote sensing-based growth indicators can effectively determine crop growth over time, and that there are similarities and correlations between the indicators.

scholarly journals Development of an Unmanned Aerial Vehicle-Borne Crop-Growth Monitoring System

Sensors ◽  
2017 ◽  
Vol 17 (3) ◽  
pp. 502 ◽  
Author(s):  
Jun Ni ◽  
Lili Yao ◽  
Jingchao Zhang ◽  
Weixing Cao ◽  
Yan Zhu ◽  
...  
Keyword(s):  
Monitoring System ◽  
Unmanned Aerial Vehicle ◽  
Crop Growth ◽  
Growth Monitoring ◽  
Aerial Vehicle

scholarly journals Using Digital Cameras on an Unmanned Aerial Vehicle to Derive Optimum Color Vegetation Indices for Leaf Nitrogen Concentration Monitoring in Winter Wheat

2019 ◽  
Vol 11 (22) ◽  
pp. 2667 ◽  
Author(s):  
Jiang ◽  
Cai ◽  
Zheng ◽  
Cheng ◽  
Tian ◽  
...  
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Vegetation Index ◽  
Near Infrared ◽  
Vegetation Indices ◽  
Crop Growth ◽  
Digital Cameras ◽  
Status Monitoring ◽  
Aerial Vehicle ◽  
Rgb Images ◽  
N Status

Commercially available digital cameras can be mounted on an unmanned aerial vehicle (UAV) for crop growth monitoring in open-air fields as a low-cost, highly effective observation system. However, few studies have investigated their potential for nitrogen (N) status monitoring, and the performance of camera-derived vegetation indices (VIs) under different conditions remains poorly understood. In this study, five commonly used VIs derived from normal color (RGB) images and two typical VIs derived from color near-infrared (CIR) images were used to estimate leaf N concentration (LNC). To explore the potential of digital cameras for monitoring LNC at all crop growth stages, two new VIs were proposed, namely, the true color vegetation index (TCVI) from RGB images and the false color vegetation index (FCVI) from CIR images. The relationships between LNC and the different VIs varied at different stages. The commonly used VIs performed well at some stages, but the newly proposed TCVI and FCVI had the best performance at all stages. The performances of the VIs with red (or near-infrared) and green bands as the numerator were limited by saturation at intermediate to high LNCs (LNC > 3.0%), but the TCVI and FCVI had the ability to mitigate the saturation. The results of model validations further supported the superiority of the TCVI and FCVI for LNC estimation. Compared to the other VIs derived using RGB cameras, the relative root mean square errors (RRMSEs) of the TCVI were improved by 8.6% on average. For the CIR images, the best-performing VI for LNC was the FCVI (R2 = 0.756, RRMSE = 14.18%). The LNC–TCVI and LNC–FCVI were stable under different cultivars, N application rates, and planting densities. The results confirmed the applicability of UAV-based RGB and CIR cameras for crop N status monitoring under different conditions, which should assist the precision management of N fertilizers in agronomic practices.

scholarly journals Monitoring Wheat Growth Using a Portable Three-Band Instrument for Crop Growth Monitoring and Diagnosis

Sensors ◽  
2020 ◽  
Vol 20 (10) ◽  
pp. 2894
Author(s):  
Huaimin Li ◽  
Weipan Lin ◽  
Fangrong Pang ◽  
Xiaoping Jiang ◽  
Weixing Cao ◽  
...  
Keyword(s):  
Vegetation Index ◽  
Vegetation Indices ◽  
Growth Index ◽  
Leaf Nitrogen ◽  
Crop Growth ◽  
Growth Monitoring ◽  
Leaf Nitrogen Content ◽  
Growth Indices ◽  
Wheat Growth ◽  
Monitoring And Diagnosis

An instrument developed to monitor and diagnose crop growth can quickly and non-destructively obtain crop growth information, which is helpful for crop field production and management. Focusing on the problems with existing two-band instruments used for crop growth monitoring and diagnosis, such as insufficient information available on crop growth and low accuracy of some growth indices retrieval, our research team developed a portable three-band instrument for crop-growth monitoring and diagnosis (CGMD) that obtains a larger amount of information. Based on CGMD, this paper carried out studies on monitoring wheat growth indices. According to the acquired three-band reflectance spectra, the combined indices were constructed by combining different bands, two-band vegetation indices (NDVI, RVI, and DVI), and three-band vegetation indices (TVI-1 and TVI-2). The fitting results of the vegetation indices obtained by CGMD and the commercial instrument FieldSpec HandHeld2 was high and the new instrument could be used for monitoring the canopy vegetation indices. By fitting each vegetation index to the growth index, the results showed that the optimal vegetation indices corresponding to leaf area index (LAI), leaf dry weight (LDW), leaf nitrogen content (LNC), and leaf nitrogen accumulation (LNA) were TVI-2, TVI-1, NDVI (R730, R815), and NDVI (R730, R815), respectively. R2 values corresponding to LAI, LDW, LNC and LNA were 0.64, 0.84, 0.60, and 0.82, respectively, and their relative root mean square error (RRMSE) values were 0.29, 0.26, 0.17, and 0.30, respectively. The addition of the red spectral band to CGMD effectively improved the monitoring results of wheat LAI and LDW. Focusing the problem of vegetation index saturation, this paper proposed a method to construct the wheat-growth-index spectral monitoring models that were defined according to the growth periods. It improved the prediction accuracy of LAI, LDW, and LNA, with R2 values of 0.79, 0.85, and 0.85, respectively, and the RRMSE values of these growth indices were 0.22, 0.23, and 0.28, respectively. The method proposed here could be used for the guidance of wheat field cultivation.

OIL PALM TREE GROWTH MONITORING FOR SMALLHOLDERS BY USING UNMANNED AERIAL VEHICLE

2015 ◽  
Vol 77 (26) ◽  
Author(s):  
Astina Tugi ◽  
Abd Wahid Rasib ◽  
Muhammad Akmal Suri ◽  
Othman Zainon ◽  
Abdul Razak Mohd Yusoff ◽  
...  
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Tree Growth ◽  
Oil Palm ◽  
Response Curve ◽  
Vegetation Index ◽  
Spectral Response ◽  
Growth Monitoring ◽  
Palm Tree ◽  
Aerial Vehicle ◽  
Uav Images

The development of the latest technology in agriculture such as using Unmanned Aerial Vehicle (UAV) platform, oil palm tree monitoring can be carried out efficiently by smallholders. Therefore, this study aims to determine the spectral response curve of oil palm tree growth for smallholders by using UAV Platform and payloaded with digital compact camera. The series of UAV images are then to be used to generate an orthophotos image whereby contains two types of spectrum bands which are single spectrum of near Infra-Red (NIR) and three spectrums of visible bands (RGB), respectively. Hence, a spectral response curve graph of oil palm tree condition is able to be produced based on the orthophoto as well as on-site ground validation using handheld spectroradiometer. The growth of the oil palm trees also able to be determined by analyzing the reflectance recorded from the images after generating the Normalized Difference Vegetation Index (NDVI) and Modified Soil-Adjusted Vegetation Index 2 (MSAVI2), respectively. This study is successful determined that the low cost UAV platform and digital compact camera able to be used by smallholders in monitoring the oil palm tree growth condition by utilizing remote sensing techniques. As conclusion, this study has showed a good approach for smallholders in determining their oil palm crops condition whereby the results indicate all are identified healthy palm tree after spectral analysis from combination of NIR and RGB UAV images, respectively.  

scholarly journals Ramie Yield Estimation Based on UAV RGB Images

Sensors ◽  
2021 ◽  
Vol 21 (2) ◽  
pp. 669
Author(s):  
Hongyu Fu ◽  
Chufeng Wang ◽  
Guoxian Cui ◽  
Wei She ◽  
Liang Zhao
Keyword(s):  
Linear Regression ◽  
Plant Height ◽  
Growth Period ◽  
Crop Growth ◽  
Growth Monitoring ◽  
Yield Estimation ◽  
Estimation Model ◽  
Multiple Characteristic ◽  
Phenotypic Data ◽  
Uav Images

Timely and accurate crop growth monitoring and yield estimation are important for field management. The traditional sampling method used for estimation of ramie yield is destructive. Thus, this study proposed a new method for estimating ramie yield based on field phenotypic data obtained from unmanned aerial vehicle (UAV) images. A UAV platform carrying RGB cameras was employed to collect ramie canopy images during the whole growth period. The vegetation indices (VIs), plant number, and plant height were extracted from UAV-based images, and then, these data were incorporated to establish yield estimation model. Among all of the UAV-based image data, we found that the structure features (plant number and plant height) could better reflect the ramie yield than the spectral features, and in structure features, the plant number was found to be the most useful index to monitor the yield, with a correlation coefficient of 0.6. By fusing multiple characteristic parameters, the yield estimation model based on the multiple linear regression was obviously more accurate than the stepwise linear regression model, with a determination coefficient of 0.66 and a relative root mean square error of 1.592 kg. Our study reveals that it is feasible to monitor crop growth based on UAV images and that the fusion of phenotypic data can improve the accuracy of yield estimations.

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