Citrus Orchards Monitoring based on Remote Sensing and Artificial Intelligence Techniques: A Review of the Literature

Moussaid, Abdellatif; Fkihi, Sanaa El; Zennayi, Yahya

doi:10.5220/0010432001720178

Cited by 4 publications

(1 citation statement)

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“…RS allows the analysis of a multispectral image that is useful for identifying and characterizing different Earth features, including soil, vegetation, and water. This technology allows for monitoring farming practices, identifying potential plant stress, and selecting and applying different management approaches to optimize the yield (Moussaid et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

Comparing Remote Sensing and In Situ Analyses on assessing the Phenology of Test Wheat Plants as Means for Optimizing Data Collection in Precision Agriculture

Kebede

Vasileva

Bojinov

et al. 2022

Preprint

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This study assessed the potential applications of open data source satellite images in estimating the phenology of the wheat crop on a study farm found in the village of Ovcha Mogila, Bulgaria. A Landsat-9 and Sentinel-2 satellite images were extracted from the open data sources. An Unmanned Aerial Vehicle (UAV) was used to capture the spectral response of plant leaves. In addition, SpectraVue 710s Leaf Spectrometer was used to measure the spectral response of the crop at five different locations. The soil samples were collected in eight spots within the farm plot. The physicochemical properties of the soil (pH, texture, N, P2 O5, and K2 O) were analyzed in the certified laboratory of AUP. The five broadband vegetation indices (VIs) have been estimated based on the reflectance wavelength range of remote sensing tools. A linear regression analysis was used along with the coefficient of determination (R2 ), Root Mean Square Error (RMSE), and correlation (r) matrix for comparing the performance of the sensors. The soil analysis revealed the study farm plot is slightly alkaline with a dominant soil texture of Clay and Clay Loam. The vegetation indices (VIs) increased linearly with crop development. Significant correlations were observed for most vegetation indices of Sentinel-2, Landsat-9, and the Buteo drone, with the highest correlation for NDVI of Sentinel-2 and Buteo drone (R2 of 0.37 and RMSE of 0.06). In relative terms, the Sentinel-2 VIs correlated better with the Buteo drone vegetation indices than the Landsat-9. The Landsat-9 VIs somewhat align better with the leaf spectrometer.

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Section: Introductionmentioning

confidence: 99%

Comparing Remote Sensing and In Situ Analyses on assessing the Phenology of Test Wheat Plants as Means for Optimizing Data Collection in Precision Agriculture

Kebede

Vasileva

Bojinov

et al. 2022

Preprint

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Citrus yield prediction using deep learning techniques: A combination of field and satellite data

Moussaid

Fkihi

Zennayi

et al. 2023

Journal of Open Innovation: Technology, Market, and Complexity

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Machine Learning Applied to Tree Crop Yield Prediction Using Field Data and Satellite Imagery: A Case Study in a Citrus Orchard

et al. 2022

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The overall goal of this study is to define an intelligent system for predicting citrus fruit yield before the harvest period. This system uses a machine learning algorithm trained on historical field data combined with spectral information extracted from satellite images. To this end, we used 5 years of historical data for a Moroccan orchard composed of 50 parcels. These data are related to climate, amount of water used for irrigation, fertilization products by dose, phytosanitary treatment dose, parcel size, and root-stock type on each parcel. Additionally, two very popular indices, the normalized difference vegetation index and normalized difference water index were extracted from Sentinel 2 and Landsat satellite images to improve prediction scores. We managed to build a total dataset composed of 250 rows, representing the 50 parcels over a period of 5 years labeled with the yield of each parcel. Several machine learning algorithms were tested with the necessary parameter optimization, while the orthonormal automatic pursuit algorithm gave good prediction scores of 0.2489 (MAE: Mean Absolute Error) and 0.0843 (MSE: Mean Squared Error). Finally, the approach followed in this study shows excellent potential for fruit yield prediction. In fact, the test was performed on a citrus orchard, but the same approach can be used on other tree crops to achieve the same goal.

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Citrus Orchards Monitoring based on Remote Sensing and Artificial Intelligence Techniques: A Review of the Literature

Cited by 4 publications

References 0 publications

Comparing Remote Sensing and In Situ Analyses on assessing the Phenology of Test Wheat Plants as Means for Optimizing Data Collection in Precision Agriculture

Comparing Remote Sensing and In Situ Analyses on assessing the Phenology of Test Wheat Plants as Means for Optimizing Data Collection in Precision Agriculture

Citrus yield prediction using deep learning techniques: A combination of field and satellite data

Machine Learning Applied to Tree Crop Yield Prediction Using Field Data and Satellite Imagery: A Case Study in a Citrus Orchard

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