Agricultural Yield Estimation of Various Crops in Southern India Using Vegetation Index

Nowadays land use/ land cover in mountain landscape is in critical condition; it leads to high risky and uncertain environments. These areas are facing multiple stresses including degradation of land resources; vagaries of climate and depletion of water resources continuously affect land use practices and livelihoods. To understand the Land use/Land cover (Lu/Lc) changes in a semi-arid mountain landscape, Kallar watershed of Bhavani basin, in southern India has been chosen. Most of the hilly part in the study area covers with forest, plantation, orchards and vegetables and which are highly affected by severe soil erosion, landslide, frequent rainfall failures and associated drought. The foothill regions are mainly utilized for agriculture practices; due to water scarcity and meagre income, the productive agriculture lands are converted into settlement plots and wasteland. Hence, land use/land cover change deduction; a stochastic processed based method is indispensable for future prediction. For identification of land use/land cover, and vegetation changes, Landsat TM, ETM (1995, 2005) and IRS P6- LISS IV (2015) images were used. Through CAMarkov chain analysis, Lu/Lc changes in past three decades (1995, 2005, and 2015) were identified and projected for (2020 and 2025); Normalized Difference Vegetation Index (NDVI) were used to find the vegetation changes. The result shows that, maximum changes occur in the plantation and slight changes found in forest cover in the hilly terrain. In foothill areas, agriculture lands were decreased while wastelands and settlement plots were increased. The outcome of the results helps to farmer and policy makers to draw optimal lands use planning and better management strategies for sustainable development of natural resources.

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YIELD ESTIMATION OF SUNFLOWER PLANT WITH CNN AND ANN USING SENTINEL-2

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xlvi-4-w5-2021-385-2021 ◽

2021 ◽

Vol XLVI-4/W5-2021 ◽

pp. 385-389

Author(s):

O. G. Narin ◽

A. Sekertekin ◽

A. Saygin ◽

F. Balik Sanli ◽

M. Gullu

Keyword(s):

Neural Network ◽

Vegetation Index ◽

Agricultural Land ◽

Crop Yields ◽

Decision Makers ◽

Yield Estimation ◽

Yield Data ◽

Artificial Neural Network Ann ◽

Agricultural Land Management ◽

Sentinel 2

Abstract. Due to food security and agricultural land management, it is crucial for decision makers and farmers to predict crop yields. In remote sensing based agricultural studies, spectral resolutions of satellite images, as well as temporal and spatial resolution, are important. In this study, we investigated whether there is a relationship between the Normalized Different Vegetation Index (NDVI) and Normalized Different Vegetation Index Red-edge (NDVIred) indices derived from the Sentinel-2 satellite. In addition, the efficiency of linear regression, Convolutional Neural Network (CNN), and Artificial Neural Network (ANN) techniques are examined with the use of indices in yield estimation. In this context, yield data of 48 sunflower parcels were obtained in 2018. The obtained results showed that both NDVI and NDVIred can be used to estimate the yield of sunflowers. The best results were obtained from the combination of the NDVI and the CNN technique with the RMSE equal to 20,874 Kg/da on 30 June 2018. Concerning the results, although there is not much superiority between the two indices, the best results were generally obtained from CNN as the method.

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Spatial Scale Transformation-based Estimation Model for Fresh Grass Yield: A Case Study of the Xilingol Grassland, Inner Mongolia, China

10.21203/rs.3.rs-182923/v1 ◽

2021 ◽

Author(s):

Haixin Liu ◽

Anbing Zhang ◽

Yuling Zhao ◽

Anzhou Zhao ◽

Dongli Wang

Keyword(s):

Spatial Scale ◽

Inner Mongolia ◽

Vegetation Index ◽

Net Primary Productivity ◽

Practical Significance ◽

Scale Transformation ◽

Yield Estimation ◽

Power Functions ◽

Estimation Model ◽

Estimation Models

Abstract Estimating the grass yield of a grassland is of vital theoretical and practical significance for reasonably determining its grazing capacity and maintaining its ecological balance. On that account, this paper first compares model precision by adopting normalized differential vegetation index (NDVI) and net primary productivity (NPP) as grass yield estimation factors, and then proposes a spatial scale transformation (SST)-based estimation model for fresh grass yield (FGY) adopting NPP as its estimation factor. Next, it takes the grassland in Xilingol League, Inner Mongolia as the study area for precision verification and grass yield estimation. Results indicated that: (1) The precision of the model adopting NPP as the estimation factor was clearly higher than that of the model adopting NDVI. (2) Through modifying NPP, the SST-based FGY estimation model could greatly improve estimation precision. The relative precisions of the estimation models constructed using linear and power functions were 18.16% and 18.35%, respectively. (3) The estimation models constructed using linear and power functions were employed to estimate the grass yield of the grassland in Xilingol League, and the total FGYs estimated by them were 8.777×10 10 kg and 8.583×10 10 kg, respectively. The two models obtained roughly the same estimates, but there were significant differences between them in the spatial distributions of FGY per unit.

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Using phenology-based enhanced vegetation index and machine learning for soybean yield estimation in Paraná State, Brazil

Journal of Applied Remote Sensing ◽

10.1117/1.jrs.12.026029 ◽

2018 ◽

Vol 12 (02) ◽

pp. 1 ◽

Cited By ~ 2

Author(s):

Jonathan Richetti ◽

Jasmeet Judge ◽

Kenneth Jay Boote ◽

Jerry Adriani Johann ◽

Miguel Angel Uribe-Opazo ◽

...

Keyword(s):

Machine Learning ◽

Vegetation Index ◽

Yield Estimation ◽

Soybean Yield ◽

Enhanced Vegetation Index

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Evaluation of the correlations between the normalized difference vegetation index (NDVI) and yield in a seasoning paprika (Capsicum annuum L. var. longum) stand

Acta Agraria Debreceniensis ◽

10.34101/actaagrar/61/2037 ◽

2014 ◽

pp. 45-49

Author(s):

Attila Dobos ◽

Róbert Víg ◽

János Nagy ◽

Mária Takácsné Hájos

Keyword(s):

Capsicum Annuum ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Estimation Method ◽

Regression Equations ◽

Yield Estimation ◽

Mean Error ◽

Capsicum Annuum L ◽

Significant Difference

The aim of our examination was to evaluate the correlations between the normalized difference vegetation index (NDVI) and yield, as well as to examine the possibility of yield estimation basedon NDVI in a seasoning paprika population. Significant correlations were observed during the examination of the correlation between NDVI and yield. Furthermore, it was concluded that yield can be estimated with a 6.6–8.3% mean error based on the regression equations. No significant difference was shown between the error of estimations performed with various regression types and that of the estimations performed at various dates. For this reason, the identification of the optimum estimation method and the determination of the optimum date for estimation call for further examinations.

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Cotton yield estimation model based on cloud computing

10.21203/rs.2.24213/v1 ◽

2020 ◽

Author(s):

quan xu ◽

Chuanjian Wang ◽

Jianguo Dai ◽

peng guo ◽

Guoshun Zhang ◽

...

Keyword(s):

Time Series ◽

Cloud Computing ◽

Deep Learning ◽

Large Scale ◽

Vegetation Index ◽

Policy Formulation ◽

Cotton Yield ◽

Yield Estimation ◽

Estimation Model ◽

Growth State

Abstract Timely and precise yield estimation is of great significance to agricultural management and macro-policy formulation. In order to improve the accuracy and applicability of cotton yield estimation model, this paper proposes a new method called SENP (Seedling Emergence and Number of Peaches) based on Amazon Web Services (AWS). Firstly, using the high-resolution visible light data obtained by the Unmanned Aerial Vehicle (UAV), the spatial position of each cotton seedling in the region was extracted by U-Net model of deep learning. Secondly, Sentinel-2 data were used in analyzing the correlation between the multi-temporal Normalized Difference Vegetation Index (NDVI) and the actual yield, so as to determine the weighting factor of NDVI in each period in the model. Subsequently, to determine the number of bolls, the growth state of cotton was graded. Finally, combined with cotton boll weight, boll opening rate and other information, the cotton yield in the experimental area was estimated by SENP model, and the precision was verified according to the measured data of yield. The experimental results reveal that the U-Net model can effectively extract the information of cotton seedlings from the background with high accuracy. And the precision rate, recall rate and F1 value reached 93.88%, 97.87% and 95.83% respectively. NDVI based on time series can accurately reflect the growth state of cotton, so as to obtain the predicted boll number of every cotton, which greatly improves the accuracy and universality of the yield estimation model. The determination coefficient (R2) of the yield estimation model reached 0.92, indicating that using SENP model for cotton yield estimation is an effective method. This study also proved that the potential and advantage of combining the AWS platform with SENP, due to its powerful cloud computing capacity, especially for deep learning, time-series crop monitoring and large scale yield estimation. This research can provide the reference information for cotton yield estimation and cloud computing platform application.

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Crop Yield Estimation Using Time-Series MODIS Data and the Effects of Cropland Masks in Ontario, Canada

Remote Sensing ◽

10.3390/rs11202419 ◽

2019 ◽

Vol 11 (20) ◽

pp. 2419 ◽

Cited By ~ 4

Author(s):

Jiangui Liu ◽

Jiali Shang ◽

Budong Qian ◽

Ted Huffman ◽

Yinsuo Zhang ◽

...

Keyword(s):

Time Series ◽

Winter Wheat ◽

Negative Correlation ◽

Linear Correlation ◽

Vegetation Index ◽

Growth Stages ◽

Yield Estimation ◽

Grain Yields ◽

Positive Linear Correlation ◽

Reflectance Data

This study investigated the estimation of grain yields of three major annual crops in Ontario (corn, soybean, and winter wheat) using MODIS reflectance data extracted with a general cropland mask and crop-specific masks. Time-series two-band enhanced vegetation index (EVI2) was derived from the 8 day composite 250 m MODIS reflectance data from 2003 to 2016. Using a general cropland mask, the strongest positive linear correlation between crop yields and EVI2 was observed at the end of July to early August, whereas a negative correlation was observed in spring. Using crop-specific masks, the time of the strongest positive linear correlation for winter wheat was found between mid-May and early June, corresponding to peak growth stages of the crop. EVI2 derived at peak growth stages of a crop provided good predictive capability for grain yield estimation, with considerable inter-annual variation. A multiple linear regression model was established for county-level yield estimation using EVI2 at peak growth stages and the year as independent variables. The model accounted for the spatiotemporal variability of grain yields of about 30% and 47% for winter wheat, 63% and 65% for corn, and 59% and 64% for soybean using the general cropland mask and crop-specific masks, respectively. A negative correlation during the spring indicated that vegetation index extracted using a general cropland mask should be used with caution in regions with mixed crops, as factors other than the growth conditions of the targeted crops may also be captured by remote sensing data.

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Multi-Year Mapping of Major Crop Yields in an Irrigation District from High Spatial and Temporal Resolution Vegetation Index

Sensors ◽

10.3390/s18113787 ◽

2018 ◽

Vol 18 (11) ◽

pp. 3787 ◽

Cited By ~ 5

Author(s):

Bing Yu ◽

Songhao Shang

Keyword(s):

Remote Sensing ◽

Crop Yield ◽

Spatial Resolution ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Crop Yields ◽

Irrigation District ◽

Accurate Estimation ◽

Yield Estimation ◽

Distribution Maps

Crop yield estimation is important for formulating informed regional and national food trade policies. The introduction of remote sensing in agricultural monitoring makes accurate estimation of regional crop yields possible. However, remote sensing images and crop distribution maps with coarse spatial resolution usually cause inaccuracy in yield estimation due to the existence of mixed pixels. This study aimed to estimate the annual yields of maize and sunflower in Hetao Irrigation District in North China using 30 m spatial resolution HJ-1A/1B CCD images and high accuracy multi-year crop distribution maps. The Normalized Difference Vegetation Index (NDVI) time series obtained from HJ-1A/1B CCD images was fitted with an asymmetric logistic curve to calculate daily NDVI and phenological characteristics. Eight random forest (RF) models using different predictors were developed for maize and sunflower yield estimation, respectively, where predictors of each model were a combination of NDVI series and/or phenological characteristics. We calibrated all RF models with measured crop yields at sampling points in two years (2014 and 2015), and validated the RF models with statistical yields of four counties in six years. Results showed that the optimal model for maize yield estimation was the model using NDVI series from the 120th to the 210th day in a year with 10 days’ interval as predictors, while that for sunflower was the model using the combination of three NDVI characteristics, three phenological characteristics, and two curve parameters as predictors. The selected RF models could estimate multi-year regional crop yields accurately, with the average values of root-mean-square error and the relative error of 0.75 t/ha and 6.1% for maize, and 0.40 t/ha and 10.1% for sunflower, respectively. Moreover, the yields of maize and sunflower can be estimated fairly well with NDVI series 50 days before crop harvest, which implicated the possibility of crop yield forecast before harvest.

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