UAV and Machine Learning Based Refinement of a Satellite-Driven Vegetation Index for Precision Agriculture

Mazzia, Vittorio; Comba, Lorenzo; Khaliq, Aleem; Chiaberge, Marcello

doi:10.3390/s20092530

Cited by 89 publications

(38 citation statements)

References 49 publications

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“…The VI can be seen as the indicator for monitoring the growing conditions, and the precisely confirmed models can be adopted for scale-up estimations. As the state of the art, the unmanned aerial vehicle (UAV) is now widely used for data collection in agricultural- and ecological-related applications, and it can be quickly deployed for acquiring images when needed compared with traditional satellite remote sensing (SRS) [ 30 , 31 , 32 , 33 , 34 ]. The high temporal and spatial images from UAV platforms are better for analysis as the data are not much influenced by the cloud [ 35 , 36 , 37 ].…”

Section: Introductionmentioning

confidence: 99%

Modified Red Blue Vegetation Index for Chlorophyll Estimation and Yield Prediction of Maize from Visible Images Captured by UAV

Guo

Wang

et al. 2020

Sensors

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The vegetation index (VI) has been successfully used to monitor the growth and to predict the yield of agricultural crops. In this paper, a long-term observation was conducted for the yield prediction of maize using an unmanned aerial vehicle (UAV) and estimations of chlorophyll contents using SPAD-502. A new vegetation index termed as modified red blue VI (MRBVI) was developed to monitor the growth and to predict the yields of maize by establishing relationships between MRBVI- and SPAD-502-based chlorophyll contents. The coefficients of determination (R2s) were 0.462 and 0.570 in chlorophyll contents’ estimations and yield predictions using MRBVI, and the results were relatively better than the results from the seven other commonly used VI approaches. All VIs during the different growth stages of maize were calculated and compared with the measured values of chlorophyll contents directly, and the relative error (RE) of MRBVI is the lowest at 0.355. Further, machine learning (ML) methods such as the backpropagation neural network model (BP), support vector machine (SVM), random forest (RF), and extreme learning machine (ELM) were adopted for predicting the yields of maize. All VIs calculated for each image captured during important phenological stages of maize were set as independent variables and the corresponding yields of each plot were defined as dependent variables. The ML models used the leave one out method (LOO), where the root mean square errors (RMSEs) were 2.157, 1.099, 1.146, and 1.698 (g/hundred grain weight) for BP, SVM, RF, and ELM. The mean absolute errors (MAEs) were 1.739, 0.886, 0.925, and 1.356 (g/hundred grain weight) for BP, SVM, RF, and ELM, respectively. Thus, the SVM method performed better in predicting the yields of maize than the other ML methods. Therefore, it is strongly suggested that the MRBVI calculated from images acquired at different growth stages integrated with advanced ML methods should be used for agricultural- and ecological-related chlorophyll estimation and yield predictions.

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Section: Introductionmentioning

confidence: 99%

Modified Red Blue Vegetation Index for Chlorophyll Estimation and Yield Prediction of Maize from Visible Images Captured by UAV

Guo

Wang

et al. 2020

Sensors

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“…After this procedure, it is possible to generate a multispectral orthomosaic and then calculate the requested indexes [91]. Multispectral images are also used in machine learning applications [80,85,92] taking into account the multi-camera nature of sensors and the different bands recorded. Thanks to the availability of a higher number of radiometric bands, the machine learning algorithms can be extended to not-visible recognition such as early stage plant disease, field quality assessment, soil water content, and more [91].…”

Section: Multispectral Sensorsmentioning

confidence: 99%

Drone and sensor technology for sustainable weed management: a review

Esposito

Crimaldi

Cirillo

et al. 2021

Chem. Biol. Technol. Agric.

107

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Weeds are amongst the most impacting abiotic factors in agriculture, causing important yield loss worldwide. Integrated Weed Management coupled with the use of Unmanned Aerial Vehicles (drones), allows for Site-Specific Weed Management, which is a highly efficient methodology as well as beneficial to the environment. The identification of weed patches in a cultivated field can be achieved by combining image acquisition by drones and further processing by machine learning techniques. Specific algorithms can be trained to manage weeds removal by Autonomous Weeding Robot systems via herbicide spray or mechanical procedures. However, scientific and technical understanding of the specific goals and available technology is necessary to rapidly advance in this field. In this review, we provide an overview of precision weed control with a focus on the potential and practical use of the most advanced sensors available in the market. Much effort is needed to fully understand weed population dynamics and their competition with crops so as to implement this approach in real agricultural contexts.

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“…Data retrieved from satellite imagery could be biased by intra-row covering, giving inaccurate information about crop status. Paper [ 16 ] presented a novel procedure, designed to improve the satellite imagery. The proposed procedure exploits high resolution images acquired by airborne multispectral sensors on unmanned aerial vehicle (UAV).…”

Section: Contributionsmentioning

confidence: 99%