scholarly journals Development of an Apparatus for Crop-Growth Monitoring and Diagnosis

Sensors ◽  
2018 ◽  
Vol 18 (9) ◽  
pp. 3129 ◽  
Author(s):  
Jun Ni ◽  
Jingchao Zhang ◽  
Rusong Wu ◽  
Fangrong Pang ◽  
Yan Zhu
Keyword(s):  
Field Experiments ◽  
Leaf Nitrogen ◽  
Crop Growth ◽  
Growth Monitoring ◽  
Nitrogen Accumulation ◽  
Leaf Nitrogen Content ◽  
Area Index ◽  
Linear Fitting ◽  
Monitoring And Diagnosis ◽  
Mean Square Errors

To non-destructively acquire leaf nitrogen content (LNC), leaf nitrogen accumulation (LNA), leaf area index (LAI), and leaf dry weight (LDW) data at high speed and low cost, a portable apparatus for crop-growth monitoring and diagnosis (CGMD) was developed according to the spectral monitoring mechanisms of crop growth. According to the canopy characteristics of crops and actual requirements of field operation environments, splitting light beams by using an optical filter and proper structural parameters were determined for the sensors. Meanwhile, an integral-type weak optoelectronic signal processing circuit was designed, which changed the gain of the system and guaranteed the high resolution of the apparatus by automatically adjusting the integration period based on the irradiance received from ambient light. In addition, a coupling processor system for a sensor information and growth model based on the microcontroller chip was developed. Field experiments showed that normalised vegetation index (NDVI) measured separately through the CGMD apparatus and the ASD spectrometer showed a good linear correlation. For measurements of canopy reflectance spectra of rice and wheat, their linear determination coefficients (R2) were 0.95 and 0.92, respectively while the root mean square errors (RMSEs) were 0.02 and 0.03, respectively. NDVI value measured by using the CGMD apparatus and growth indices of rice and wheat exhibited a linear relationship. For the monitoring models for LNC, LNA, LAI, and LDW of rice based on linear fitting of NDVI, R2 were 0.64, 0.67, 0.63 and 0.70, and RMSEs were 0.31, 2.29, 1.15 and 0.05, respectively. In addition, R2 of the models for monitoring LNC, LNA, LAI, and LDW of wheat on the basis of linear fitting of NDVI were 0.82, 0.71, 0.72 and 0.70, and RMSEs were 0.26, 2.30, 1.43, and 0.05, respectively.

scholarly journals Using a Portable Active Sensor to Monitor Growth Parameters and Predict Grain Yield of Winter Wheat

Sensors ◽  
2019 ◽  
Vol 19 (5) ◽  
pp. 1108 ◽  
Author(s):  
Jiayi Zhang ◽  
Xia Liu ◽  
Yan Liang ◽  
Qiang Cao ◽  
Yongchao Tian ◽  
...  
Keyword(s):  
Winter Wheat ◽  
Grain Yield ◽  
Vegetation Index ◽  
Field Experiments ◽  
Growth Parameters ◽  
Leaf Nitrogen ◽  
Crop Growth ◽  
Nitrogen Accumulation ◽  
Wheat Cultivars ◽  
Area Index

Rapid and effective acquisition of crop growth information is a crucial step of precision agriculture for making in-season management decisions. Active canopy sensor GreenSeeker (Trimble Navigation Limited, Sunnyvale, CA, USA) is a portable device commonly used for non-destructively obtaining crop growth information. This study intended to expand the applicability of GreenSeeker in monitoring growth status and predicting grain yield of winter wheat (Triticum aestivum L.). Four field experiments with multiple wheat cultivars and N treatments were conducted during 2013–2015 for obtaining canopy normalized difference vegetation index (NDVI) and ratio vegetation index (RVI) synchronized with four agronomic parameters: leaf area index (LAI), leaf dry matter (LDM), leaf nitrogen concentration (LNC), and leaf nitrogen accumulation (LNA). Duration models based on NDVI and RVI were developed to monitor these parameters, which indicated that NDVI and RVI explained 80%, 68–70%, 10–12%, and 67–73% of the variability in LAI, LDM, LNC and LNA, respectively. According to the validation results, the relative root mean square error (RRMSE) were all <0.24 and the relative error (RE) were all <23%. Considering the variation among different wheat cultivars, the newly normalized vegetation indices rNDVI (NDVI vs. the NDVI for the highest N rate) and rRVI (RVI vs. the RVI for the highest N rate) were calculated to predict the relative grain yield (RY, the yield vs. the yield for the highest N rate). rNDVI and rRVI explained 77–85% of the variability in RY, the RRMSEs were both <0.13 and the REs were both <6.3%. The result demonstrates the feasibility of monitoring growth parameters and predicting grain yield of winter wheat with portable GreenSeeker sensor.

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.

scholarly journals Estimating the Leaf Nitrogen Content with a New Feature Extracted from the Ultra-High Spectral and Spatial Resolution Images in Wheat

2021 ◽  
Vol 13 (4) ◽  
pp. 739
Author(s):  
Jiale Jiang ◽  
Jie Zhu ◽  
Xue Wang ◽  
Tao Cheng ◽  
Yongchao Tian ◽  
...  
Keyword(s):  
Nitrogen Content ◽  
Precision Agriculture ◽  
Field Experiments ◽  
Mean Squared Error ◽  
Growing Season ◽  
Leaf Nitrogen ◽  
Textural Analysis ◽  
Hyperspectral Images ◽  
Leaf Nitrogen Content ◽  
New Feature

Real-time and accurate monitoring of nitrogen content in crops is crucial for precision agriculture. Proximal sensing is the most common technique for monitoring crop traits, but it is often influenced by soil background and shadow effects. However, few studies have investigated the classification of different components of crop canopy, and the performance of spectral and textural indices from different components on estimating leaf nitrogen content (LNC) of wheat remains unexplored. This study aims to investigate a new feature extracted from near-ground hyperspectral imaging data to estimate precisely the LNC of wheat. In field experiments conducted over two years, we collected hyperspectral images at different rates of nitrogen and planting densities for several varieties of wheat throughout the growing season. We used traditional methods of classification (one unsupervised and one supervised method), spectral analysis (SA), textural analysis (TA), and integrated spectral and textural analysis (S-TA) to classify the images obtained as those of soil, panicles, sunlit leaves (SL), and shadowed leaves (SHL). The results show that the S-TA can provide a reasonable compromise between accuracy and efficiency (overall accuracy = 97.8%, Kappa coefficient = 0.971, and run time = 14 min), so the comparative results from S-TA were used to generate four target objects: the whole image (WI), all leaves (AL), SL, and SHL. Then, those objects were used to determine the relationships between the LNC and three types of indices: spectral indices (SIs), textural indices (TIs), and spectral and textural indices (STIs). All AL-derived indices achieved more stable relationships with the LNC than the WI-, SL-, and SHL-derived indices, and the AL-derived STI was the best index for estimating the LNC in terms of both calibration (Rc2 = 0.78, relative root mean-squared error (RRMSEc) = 13.5%) and validation (Rv2 = 0.83, RRMSEv = 10.9%). It suggests that extracting the spectral and textural features of all leaves from near-ground hyperspectral images can precisely estimate the LNC of wheat throughout the growing season. The workflow is promising for the LNC estimation of other crops and could be helpful for precision agriculture.

scholarly journals Design and Testing of an Active Light Source Apparatus for Crop Growth Monitoring and Diagnosis

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 206474-206490
Author(s):  
Lili Yao ◽  
Rusong Wu ◽  
Shun Wu ◽  
Xiaoping Jiang ◽  
Yan Zhu ◽  
...  
Keyword(s):  
Light Source ◽  
Crop Growth ◽  
Growth Monitoring ◽  
Monitoring And Diagnosis ◽  
Design And Testing

scholarly journals Bibliometric Analysis of Remote Sensing Research Trend in Crop Growth Monitoring: A Case Study in China

2019 ◽  
Vol 11 (7) ◽  
pp. 809 ◽  
Author(s):  
Lijuan Wang ◽  
Guimin Zhang ◽  
Ziyi Wang ◽  
Jiangui Liu ◽  
Jiali Shang ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Vegetation Index ◽  
Crop Growth ◽  
Future Trend ◽  
Current Status ◽  
Research Trend ◽  
Growth Monitoring ◽  
Knowledge Mapping ◽  
Area Index ◽  
Quantitative Visualization

Remote sensing of crop growth monitoring is an important technique to guide agricultural production. To gain a comprehensive understanding of historical progression and current status, and future trend of remote sensing researches and applications in the field of crop growth monitoring in China, a study was carried out based on the publications from the past 20 years by Chinese scholars. Using the knowledge mapping software CiteSpace, a quantitative and qualitative analysis of research development, current hotspots, and future directions of crop growth monitoring using remote sensing technology in China was conducted. Furthermore, the relationship between high-frequency keywords and the emerging hot topics were visually analyzed. The results revealed that Chinese researchers paid more attention on keywords such as “vegetation index”, “crop growth”, “winter wheat”, “leaf area index (LAI)”, and “model” in the field of crop growth monitoring, and “LAI” and “unmanned aerial vehicle (UAV)”, appeared increasingly in frontier research of this discipline. Overall, bibliometric results from this CiteSpace-aided study provide a quantitative visualization to enrich our understanding on the historical development, current status, and future trend of crop growth monitoring in China.

Photosynthesis and respiration decline with light intensity in dominant and suppressed Eucalyptus globulus canopies

2008 ◽  
Vol 35 (6) ◽  
pp. 439 ◽  
Author(s):  
A. P. O'Grady ◽  
D. Worledge ◽  
A. Wilkinson ◽  
M. Battaglia
Keyword(s):  
Eucalyptus Globulus ◽  
Dark Respiration ◽  
Light Availability ◽  
Structural Complexity ◽  
Leaf Nitrogen ◽  
Leaf Nitrogen Content ◽  
Area Index ◽  
Respiration Rates ◽  
Leaf Dark Respiration ◽  
Photosynthesis And Respiration

Within canopy gradients in light-saturated photosynthesis (Amax), foliar nitrogen ([N]area) and leaf dark respiration (R15) were studied in the canopies of dominant and suppressed trees within an even-aged (4-year-old) Eucalyptus globulus (Labill) stand in southern Tasmania. Despite being an even-aged stand growing in a relatively uniform environment with respect to nutrient and water availability, the stand exhibited considerable structural complexity. Diameter at 1.3 m ranged between 3 cm and 21 cm, trees average 12 m height and stand leaf area index was ~6 m2 m–2 leading to strong gradients in light availability. We were interested in understanding the processes governing canopy production in trees of contrasting dominance classes. Vertical gradients in photosynthesis and foliar respiration were studied within the canopies of dominant and suppressed trees during 2006 and 2007. Amax varied from ~18 μmol m–2 s–1 in the upper canopy to 3 μmol m–2 s–1 at lower canopy positions. On average, Amax were higher in the dominant trees than in the suppressed trees. However, at any given height, Amax were similar in suppressed and dominant trees and were strongly related to leaf nitrogen content. Dark respiration varied from ~1.4 μmol m–2 s–1 in the upper canopy to 0.2 μmol m–2 s–1 in the lower canopy positions. In contrast to the patterns for Amax, dark respiration rates in the suppressed trees were higher than dominant trees at similar canopy positions. Respiration rates were also strongly related to [N]area and to Amax.

scholarly journals Effect of Cowpea severe mosaic virus on Crop Growth Characteristics and Yield of Cowpea

Plant Disease ◽  
2005 ◽  
Vol 89 (5) ◽  
pp. 515-520 ◽  
Author(s):  
H. M. Booker ◽  
P. Umaharan ◽  
C. R. McDavid
Keyword(s):  
Mosaic Virus ◽  
Leaf Area ◽  
Growth Phase ◽  
Field Experiments ◽  
Yield Loss ◽  
Crop Growth ◽  
Growth And Yield ◽  
Exponential Growth Phase ◽  
Area Index ◽  
The Impact

Field experiments were carried out in St. Augustine, Trinidad & Tobago, West Indies to determine the effects of time of inoculation of Cowpea severe mosaic virus (CPSMV) and cultivar on crop growth and yield in cowpea (Vigna unguiculata). Crop growth and yield loss were investigated through growth analysis and yield component analysis on three cultivars in two seasons (wet and dry). Time of inoculation had the most profound impact on yield. Inoculations during the early log phase (seedling stage), 12 days after seeding (DAS), consistently had the greatest impact (50 to 85% yield loss) compared with those inoculated during the exponential growth phase (24 DAS; 22 to 66% yield loss) or linear growth phase (35 DAS; 2 to 36% yield loss). The effects were particularly pronounced in the dry season and in the more determinate cultivar, H8-8-27. Reduction in maximum leaf area index, leaf area duration, or maximum vegetative dry matter explained reductions in yield. Yield reductions resulted primarily from reduced pod number per plant and, to a lesser extent, from reduced average pod dry weight. The results show that CPSMV control measures should be aimed at delaying infection by CPSMV to minimize the impact on cowpea yield.

scholarly journals Assessing the Effect of Real Spatial Resolution of In Situ UAV Multispectral Images on Seedling Rapeseed Growth Monitoring

2020 ◽  
Vol 12 (7) ◽  
pp. 1207 ◽  
Author(s):  
Jian Zhang ◽  
Chufeng Wang ◽  
Chenghai Yang ◽  
Tianjin Xie ◽  
Zhao Jiang ◽  
...  
Keyword(s):  
Spatial Resolution ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Vegetation Indices ◽  
Crop Growth ◽  
Growth Monitoring ◽  
Multispectral Images ◽  
Area Index ◽  
Uav Images

The spatial resolution of in situ unmanned aerial vehicle (UAV) multispectral images has a crucial effect on crop growth monitoring and image acquisition efficiency. However, existing studies about optimal spatial resolution for crop monitoring are mainly based on resampled images. Therefore, the resampled spatial resolution in these studies might not be applicable to in situ UAV images. In order to obtain optimal spatial resolution of in situ UAV multispectral images for crop growth monitoring, a RedEdge Micasense 3 camera was installed onto a DJI M600 UAV flying at different heights of 22, 29, 44, 88, and 176m to capture images of seedling rapeseed with ground sampling distances (GSD) of 1.35, 1.69, 2.61, 5.73, and 11.61 cm, respectively. Meanwhile, the normalized difference vegetation index (NDVI) measured by a GreenSeeker (GS-NDVI) and leaf area index (LAI) were collected to evaluate the performance of nine vegetation indices (VIs) and VI*plant height (PH) at different GSDs for rapeseed growth monitoring. The results showed that the normalized difference red edge index (NDRE) had a better performance for estimating GS-NDVI (R2 = 0.812) and LAI (R2 = 0.717), compared with other VIs. Moreover, when GSD was less than 2.61 cm, the NDRE*PH derived from in situ UAV images outperformed the NDRE for LAI estimation (R2 = 0.757). At oversized GSD (≥5.73 cm), imprecise PH information and a large heterogeneity within the pixel (revealed by semi-variogram analysis) resulted in a large random error for LAI estimation by NDRE*PH. Furthermore, the image collection and processing time at 1.35 cm GSD was about three times as long as that at 2.61 cm. The result of this study suggested that NDRE*PH from UAV multispectral images with a spatial resolution around 2.61 cm could be a preferential selection for seedling rapeseed growth monitoring, while NDRE alone might have a better performance for low spatial resolution images.

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