Leaf area index estimation using top-of-canopy airborne RGB images

Raj, Rahul; Walker, Jeffrey P.; Pingale, Rohit; Nandan, Rohit; Naik, B. Balaji; Adinarayana, J.

doi:10.1016/j.jag.2020.102282

Cited by 42 publications

(30 citation statements)

References 34 publications

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“…On the contrary, AquaCrop uses canopy cover to determine the amount of water transpired that later reflected on crop yield through its relation with biomass simulation (Steduto et al, 2009). Accuracy of canopy cover estimates using digital image analysis limited by angle of view during aerial image capturing and canopy closure at later stage of crop growth resulting in saturation signal (Fiala et al, 2006;Raj et al, 2021). On the contrary, LAI estimates using a direct method as done in this study gives the most accurate values (Raj et al, 2021).…”

Section: Evaluation Of Modelsmentioning

confidence: 85%

“…Accuracy of canopy cover estimates using digital image analysis limited by angle of view during aerial image capturing and canopy closure at later stage of crop growth resulting in saturation signal (Fiala et al, 2006;Raj et al, 2021). On the contrary, LAI estimates using a direct method as done in this study gives the most accurate values (Raj et al, 2021). Hence, using LAI by DSSAT and canopy cover by AquaCrop as primary engine for capturing resources during the formation of biomass and subsequent yield might be additional reasons for variation in the accuracy of yield estimation between the two The Journal of Agricultural Science 9 models.…”

Section: Evaluation Of Modelsmentioning

confidence: 99%

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Model-based yield gap analysis and constraints of rainfed sorghum production in Southwest Ethiopia

et al. 2020

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For ensuring food demand of the fast growing population in developing countries, quantification of crop yield gaps and exploring production constraints are very crucial. Sorghum is one of the most important climate change resilient crops in the rainfed farming systems of semi-arid tropics. However, there is little information about yield gaps and production constraints. This study aimed at analysing existing yield gaps and exploring major constraints of sorghum production in Southwest Ethiopia. A crop simulation model approach using AquaCrop and DSSAT was used to estimate potential yield and analyse the yield gaps. Model calibration and evaluation was performed using data from field experiments conducted in 2018 and 2019. Sorghum production constraints were assessed using a survey. The actual and water-limited yield of sorghum ranged from 0.58 to 2.51 and 3.6 to 6.47 t/ha, respectively for the period 2003–17. The regional yield gaps of sorghum for the targeted period were 3.02–3.95 t/ha with a mean value of 3.51 t/ha. Majority of respondent farmers considered seasonal rainfall risk (98%), poor soil fertility (86%), lack of improved varieties (78%) and inadequate weed management (56%) as major factors responsible for the existing yield gaps. The mean exploitable yield gap (2.5 t/ha) between water-limited and actual yield showed the level of existing opportunity for improvement in the actual productivity of sorghum. The gaps also call for introduction of proper interventions such as adoption of improved varieties, planting date adjustment, conservation tillage, fertilizer application and on time weed management.

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Section: Evaluation Of Modelsmentioning

confidence: 85%

Section: Evaluation Of Modelsmentioning

confidence: 99%

Model-based yield gap analysis and constraints of rainfed sorghum production in Southwest Ethiopia

et al. 2020

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“…After the head emergence stage of winter wheat, wheat spikes grow out and the canopies become more and more dense, literature shows that leaf biochemical variables based on remote sensing technique perform well in the early stage, however, it tends to produce uncertainty or error during the reproductive stage [48], and then could result in more difficulties to monitor their vertical profile within canopies. Thus, it is vital to fully understand the effects of spikes on canopy reflectance and the detection degree of leaf information in different vertical layers by remotely sensed data, then to derive spectral indices to estimate vertical leaf biochemical parameters.…”

Section: Discussionmentioning

confidence: 99%

Estimating Vertical Distribution of Leaf Water Content within Wheat Canopies after Head Emergence

Kong

Huang

et al. 2021

Remote Sensing

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Monitoring vertical profile of leaf water content (LWC) within wheat canopies after head emergence is vital significant for increasing crop yield. However, the estimation of vertical distribution of LWC from remote sensing data is still challenging due to the effects of wheat spikes and the efficacy of sensor measurement from the nadir direction. Using two-year field experiments with different growth stages after head emergence, N rates, wheat cultivars, we investigated the vertical distribution of LWC within canopies, the changes of canopy reflectance after spikes removal, the relationship between spectral indices and LWC in the upper-, middle- and bottom-layer. The interrelationship among vertical LWC were constructed, and four ratio of reflectance difference (RRD) type of indices were proposed based on the published WI and NDWSI indices to determine vertical distribution of LWC. The results indicated a bell shape distribution of LWC in wheat plants with the highest value appeared at the middle layer, and significant linear correlations between middle-LWC vs. upper-LWC and middle-LWC vs. bottom-LWC (r ≥ 0.92) were identified. The effects of wheat spikes on spectral reflectance mainly occurred in near infrared to shortwave infrared regions, which then decreased the accuracy of LWC estimation. Spectral indices at the middle layer outperformed the other two layers in LWC assessment and were less susceptible to wheat spikes effects, in particular, the newly proposed narrow-band WI-4 and NDWSI-4 indices exhibited great potential in tracking the changes of middle-LWC (R2 = 0.82 and 0.84, respectively). By taking into account the effects of wheat spikes and the interrelationship of vertical LWC within canopies, an indirect induction strategy was developed for modeling the upper-LWC and bottom-LWC. It was found that the indirect induction models based on the WI-4 and NDWSI-4 indices were more effective than the models obtained from conventional direct estimation method, with R2 of 0.78 and 0.81 for the upper-LWC estimation, and 0.75 and 0.74 for the bottom-LWC estimation, respectively.

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“…Estimating LAI with the help of remote sensing systems has been a key objective in precision agriculture [206]. The most common method for the estimation of LAI is the fusion of empirical relationships between vegetation indices (Vis) and LAI data (Table 1) [207][208][209][210][211][212]. The fusion of empirical models has been widely adopted for LAI monitoring; however, these models do not show efficient results in all environmental conditions, as their parameters are limited to particular regions [213][214][215].…”

Section: Laimentioning

confidence: 99%

Technology and Data Fusion Methods to Enhance Site-Specific Crop Monitoring

et al. 2022

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Digital farming approach merges new technologies and sensor data to optimize the quality of crop monitoring in agriculture. The successful fusion of technology and data is highly dependent on the parameter collection, the modeling adoption, and the technology integration being accurately implemented according to the specified needs of the farm. This fusion technique has not yet been widely adopted due to several challenges; however, our study here reviews current methods and applications for fusing technologies and data. First, the study highlights different sensors that can be merged with other systems to develop fusion methods, such as optical, thermal infrared, multispectral, hyperspectral, light detection and ranging and radar. Second, the data fusion using the internet of things is reviewed. Third, the study shows different platforms that can be used as a source for the fusion of technologies, such as ground-based (tractors and robots), space-borne (satellites) and aerial (unmanned aerial vehicles) monitoring platforms. Finally, the study presents data fusion methods for site-specific crop parameter monitoring, such as nitrogen, chlorophyll, leaf area index, and aboveground biomass, and shows how the fusion of technologies and data can improve the monitoring of these parameters. The study further reveals limitations of the previous technologies and provides recommendations on how to improve their fusion with the best available sensors. The study reveals that among different data fusion methods, sensors and technologies, the airborne and terrestrial LiDAR fusion method for crop, canopy, and ground may be considered as a futuristic easy-to-use and low-cost solution to enhance the site-specific monitoring of crop parameters.

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Leaf area index estimation using top-of-canopy airborne RGB images

Cited by 42 publications

References 34 publications

Model-based yield gap analysis and constraints of rainfed sorghum production in Southwest Ethiopia

Model-based yield gap analysis and constraints of rainfed sorghum production in Southwest Ethiopia

Estimating Vertical Distribution of Leaf Water Content within Wheat Canopies after Head Emergence

Technology and Data Fusion Methods to Enhance Site-Specific Crop Monitoring

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