Evaluating the potential of red edge position (REP) of hyperspectral remote sensing data for real time estimation of LAI &amp; chlorophyll content of kinnow mandarin (Citrus reticulata) fruit orchards

Ali, Anwar; Imran, Muhammad

doi:10.1016/j.scienta.2020.109326

Cited by 35 publications

(23 citation statements)

References 71 publications

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“…Several research studies have been conducted on Chinese fruit production, but only a few have been published in the literature ( Ali & Imran, 2020 ; Vakil-Baghmisheh & Pavešić, 2003 ). Friis & Nielsen (2016) suggested banana plantation investments in the United States (Luang Namtha Province, Laos).…”

Section: Related Workmentioning

confidence: 99%

Artificial intelligence framework for modeling and predicting crop yield to enhance food security in Saudi Arabia

Al-Adhaileh

Aldhyani

2022

PeerJ Computer Science

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Predicting crop yields is a critical issue in agricultural production optimization and intensification research. Accurate foresights of natural circumstances a year in advance can have a considerable impact on management decisions regarding crop selection, rotational location in crop rotations, agrotechnical methods employed, and long-term land use planning. One of the most important aspects of precision farming is sustainability. The novelty of this study is to evidence the effective of the temperature, pesticides, and rainfall environment parameters in the influence sustainable agriculture and economic efficiency at the farm level in Saudi Arabia. Furthermore, predicting the future values of main crop yield in Saudi Arabia. The use of artificial intelligence (AI) to estimate the impact of environment factors and agrotechnical parameters on agricultural crop yields and to anticipate yields is examined in this study. Using artificial neural networks (ANNs), a highly effective multilayer perceptron (MLP) model was built to accurately predict the crop yield, temperature, insecticides, and rainfall based on environmental data. The dataset is collected from different Saudi Arabia regions from 1994 to 2016, including the temperature, insecticides, rainfall, and crop yields for potatoes, rice, sorghum, and wheat. For this study, we relied on five different statistical evaluation metrics: the mean square error (MSE), the root-mean-square error (RMSE), normalized root mean square error (NRMSE), Pearson’s correlation coefficient (R%), and the determination coefficient (R2). Analyses of datasets for crop yields, temperature, and insecticides led to the development of the MLP models. The datasets are randomly divided into separate samples, 70% for training and 30% for testing. The best-performing MLP model is characterized by values of (R = 100%) and (R2 = 96.33) for predicting insecticides in the testing process. The temperature, insecticides, and rainfall were examined with different crop yields to confirm the effectiveness of these parameters for increasing product crop yields in Saudi Arabia; we found that these items had highest relationships. The average values are R = 98.20%, 96.50, and 99.14% with for the temperature, insecticides, and rainfall, respectively. Based on these findings, it appeared that each of the parameter categories that are considered (temperature, pesticides, and rainfall) had a similar contribution to the accuracy of anticipated yield projection.

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Section: Related Workmentioning

confidence: 99%

Artificial intelligence framework for modeling and predicting crop yield to enhance food security in Saudi Arabia

Al-Adhaileh

Aldhyani

2022

PeerJ Computer Science

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“…Traditional methods to estimate crop parameter are based on destructive measurement, which not only consume time and manpower but also are difficult to be applied in a large area. In recent decades, remote sensing technology has been successfully applied to crop growth monitoring through satellite platforms, manned airborne platforms, and ground spectral equipment (Michele et al, 2015;Maimaitijiang et al, 2017;Ansar and Muhammad, 2020;Dehkordi et al, 2020). There are two kinds of satellite remote sensing data for crop parameters, namely, optical image and synthetic aperture radar data (Cougo et al, 2015;Castillo et al, 2017;Du et al, 2017;Pham and Yoshino, 2017;Pandit et al, 2018;Li et al, 2019), providing different spatial resolutions, such as SPOT (20 m), MODIS (250 m), Sentinel 1A (10 m), and ALOS-2 PLASAR2 (6 m) (Niu et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

Inversion of Winter Wheat Growth Parameters and Yield Under Different Water Treatments Based on UAV Multispectral Remote Sensing

et al. 2021

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In recent years, the unmanned aerial vehicle (UAV) remote sensing system has been rapidly developed and applied in accurate estimation of crop parameters and yield at farm scale. To develop the major contribution of UAV multispectral images in predicting winter wheat leaf area index (LAI), chlorophyll content (called soil and plant analyzer development [SPAD]), and yield under different water treatments (low water level, medium water level, and high water level), vegetation indices (VIs) originating from UAV multispectral images were used during key winter wheat growth stages. The estimation performances of the models (linear regression, quadratic polynomial regression, and exponential and multiple linear regression models) on the basis of VIs were compared to get the optimal prediction method of crop parameters and yield. Results showed that LAI and SPAD derived from VIs both had high correlations compared with measured data, with determination coefficients of 0.911 and 0.812 (multivariable regression [MLR] model, normalized difference VI [NDVI], soil adjusted VI [SAVI], enhanced VI [EVI], and difference VI [DVI]), 0.899 and 0.87 (quadratic polynomial regression, NDVI), and 0.749 and 0.829 (quadratic polynomial regression, NDVI) under low, medium, and high water levels, respectively. The LAI and SPAD derived from VIs had better potential in estimating winter wheat yield by using multivariable linear regressions, compared to the estimation yield based on VIs directly derived from UAV multispectral images alone by using linear regression, quadratic polynomial regression, and exponential models. When crop parameters (LAI and SPAD) in the flowering period were adopted to estimate yield by using multiple linear regressions, a high correlation of 0.807 was found, while the accuracy was over 87%. Importing LAI and SPAD obtained from UAV multispectral imagery based on VIs into the yield estimation model could significantly enhance the estimation performance. This study indicates that the multivariable linear regression could accurately estimate winter wheat LAI, SPAD, and yield under different water treatments, which has a certain reference value for the popularization and application of UAV remote sensing in precision agriculture.

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“…As far as Chinese fruit production is concerned, very few studies have been reported in the literature [19,20]. Friis, C et al [21] proposed banana plantation investments in (Luang Namtha Province, Laos).…”

Section: Introductionmentioning

confidence: 99%

Deep Learning-Based Growth Prediction System: A Use Case of China Agriculture

et al. 2021

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Agricultural advancements have significantly impacted people’s lives and their surroundings in recent years. The insufficient knowledge of the whole agricultural production system and conventional ways of irrigation have limited agricultural yields in the past. The remote sensing innovations recently implemented in agriculture have dramatically revolutionized production efficiency by offering unparalleled opportunities for convenient, versatile, and quick collection of land images to collect critical details on the crop’s conditions. These innovations have enabled automated data collection, simulation, and interpretation based on crop analytics facilitated by deep learning techniques. This paper aims to reveal the transformative patterns of old Chinese agrarian development and fruit production by focusing on the major crop production (from 1980 to 2050) taking into account various forms of data from fruit production (e.g., apples, bananas, citrus fruits, pears, and grapes). In this study, we used production data for different fruits grown in China to predict the future production of these fruits. The study employs deep neural networks to project future fruit production based on the statistics issued by China’s National Bureau of Statistics on the total fruit growth output for this period. The proposed method exhibits encouraging results with an accuracy of 95.56% calculating by accuracy formula based on fruit production variation. Authors further provide recommendations on the AGR-DL (agricultural deep learning) method being helpful for developing countries. The results suggest that the agricultural development in China is acceptable but demands more improvement and government needs to prioritize expanding the fruit production by establishing new strategies for cultivators to boost their performance.

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Evaluating the potential of red edge position (REP) of hyperspectral remote sensing data for real time estimation of LAI & chlorophyll content of kinnow mandarin (Citrus reticulata) fruit orchards

Cited by 35 publications

References 71 publications

Artificial intelligence framework for modeling and predicting crop yield to enhance food security in Saudi Arabia

Artificial intelligence framework for modeling and predicting crop yield to enhance food security in Saudi Arabia

Inversion of Winter Wheat Growth Parameters and Yield Under Different Water Treatments Based on UAV Multispectral Remote Sensing

Deep Learning-Based Growth Prediction System: A Use Case of China Agriculture

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