Oil palm yield prediction across blocks from multi-source data using machine learning and deep learning

Ang, Yuhao; Shafri, Helmi Zulhaidi Mohd; Lee, Yang Ping; Bakar, Shahrul Azman; Abidin, Haryati; Junaidi, Mohd Umar Ubaydah Mohd; Hashim, Shaiful Jahari; Che’Ya, Nik Norasma; Hassan, Mohd Roshdi; Lim, H. S.; Abdullah, Rosni; Yusup, Yusri; Muhammad, Syahidah Akmal; Teh, Sin Yin; Samad, Mohd Na’aim

doi:10.1007/s12145-022-00882-9

Cited by 8 publications

(3 citation statements)

References 62 publications

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“…This article further compared the results of using SVR, RF, and DNN with the results of other studies. Ang et al (2020) used DNN comparing with SVR, RF, and accuracy between oil palm yield and actual yield [ 89 ]. After backward elimination, the DNN achieved the highest prediction accuracy among the other models, with a 14% increase in R 2 and a 1% decrease in MAPE.…”

Section: Discussionmentioning

confidence: 99%

Prediction of fruit characteristics of grafted plants of Camellia oleifera by deep neural networks

Yang,

Zhou,

et al. 2024

Plant Methods

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Background Camellia oleifera, an essential woody oil tree in China, propagates through grafting. However, in production, it has been found that the interaction between rootstocks and scions may affect fruit characteristics. Therefore, it is necessary to predict fruit characteristics after grafting to identify suitable rootstock types. Methods This study used Deep Neural Network (DNN) methods to analyze the impact of 106 6-year-old grafting combinations on the characteristics of C.oleifera, including fruit and seed characteristics, and fatty acids. The prediction of characteristics changes after grafting was explored to provide technical support for the cultivation and screening of specialized rootstocks. After determining the unsaturated fat acids, palmitoleic acid C16:1, cis-11 eicosenoic acid C20:1, oleic acid C18:1, linoleic acid C18:2, linolenic acid C18:3, kernel oil content, fruit height, fruit diameter, fresh fruit weight, pericarp thickness, fresh seed weight, and the number of fresh seeds, the DNN method was used to calculate and analyze the model. The model was screened using the comprehensive evaluation index of Mean Absolute Error (MAPE), determinate correlation R2 and and time consumption. Results When using 36 neurons in 3 hidden layers, the deep neural network model had a MAPE of less than or equal to 16.39% on the verification set and less than or equal to 13.40% on the test set. Compared with traditional machine learning methods such as support vector machines and random forests, the DNN method demonstrated more accurate predictions for fruit phenotypic characteristics, with MAPE improvement rates of 7.27 and 3.28 for the 12 characteristics on the test set and maximum R2 improvement values of 0.19 and 0.33. In conclusion, the DNN method developed in this study can effectively predict the oil content and fruit phenotypic characteristics of C. oleifera, providing a valuable tool for predicting the impact of grafting combinations on the fruit of C. oleifera.

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Section: Discussionmentioning

confidence: 99%

Prediction of fruit characteristics of grafted plants of Camellia oleifera by deep neural networks

Yang,

Zhou,

et al. 2024

Plant Methods

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“…Traditional Machine Learning (ML) models like Support Vector Regression (SVR), Ridge and LASSO regression, and Random Forest (RF) are more practical due to the availability of high-quality pre-built solutions in the data science community. These regression models have been used for vegetation cover studies using LST and NDVI (Ang et al 2022 ; Sun et al 2019 ). However, traditional ML-based models may not be able to handle the complex non-linear dependencies of large multi-variable datasets (Chakraborty et al 2021 ).…”

Section: Introductionmentioning

confidence: 99%

Next-level vegetation health index forecasting: A ConvLSTM study using MODIS Time Series

KARTAL,

IBAN,

SEKERTEKIN

2024

Environ Sci Pollut Res

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The Vegetation Health Index (VHI) is a metric used to assess the health and condition of vegetation, based on satellite-derived data. It offers a comprehensive indicator of stress or vigor, commonly used in agriculture, ecology, and environmental monitoring for forecasting changes in vegetation health. Despite its advantages, there are few studies on forecasting VHI as a future projection, particularly using up-to-date and effective machine learning methods. Hence, the primary objective of this study is to forecast VHI values by utilizing remotely sensed images. To achieve this objective, the study proposes employing a combined Convolutional Neural Network (CNN) and a specific type of Recurrent Neural Network (RNN) called Long Short-Term Memory (LSTM), known as ConvLSTM. The VHI time series images are calculated based on the Normalized Difference Vegetation Index (NDVI) and Land Surface Temperature (LST) data obtained from the Moderate Resolution Imaging Spectroradiometer (MODIS) aboard the Terra and Aqua satellites. In addition to the traditional image-based calculation, the study suggests using global minimum and global maximum values (global scale) of NDVI and LST time series for calculating the VHI. The results of the study showed that the ConvLSTM with a 1-layer structure generally provided better forecasts than 2-layer and 3-layer structures. The average Root Mean Square Error (RMSE) values for the 1-step, 2-step, and 3-step ahead VHI forecasts were 0.025, 0.026, and 0.026, respectively, with each step representing an 8-day forecast horizon. Moreover, the proposed global scale model using the applied ConvLSTM structures outperformed the traditional VHI calculation method.

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“…Sustainable agriculture has undergone a tremendous change with the emergence of remote sensing and machine learning tools [10,11]. By leveraging spectral information derived from multispectral data [12][13][14][15] and incorporating geospatial data on surface properties retrieved from synthetic aperture radar (SAR) data [16,17], along with other data sources like climatic and phenological data [18][19][20], researchers have made significant progress in modeling agricultural productivity and monitoring crop growth. For this purpose, various prediction models have been commonly employed for yield estimation, including random forests [21,22], neural networks [23], gradient-boosting trees [24], and linear regression analysis [25], among other tools that involve deep learning and artificial intelligence [26,27].…”

Section: Introductionmentioning

confidence: 99%

Crop Yield Estimation Using Sentinel-3 SLSTR, Soil Data, and Topographic Features Combined with Machine Learning Modeling: A Case Study of Nepal

Sahbeni,

Székely,

Musyimi

et al. 2023

AgriEngineering

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Effective crop monitoring and accurate yield estimation are fundamental for informed decision-making in agricultural management. In this context, the present research focuses on estimating wheat yield in Nepal at the district level by combining Sentinel-3 SLSTR imagery with soil data and topographic features. Due to Nepal’s high-relief terrain, its districts exhibit diverse geographic and soil properties, leading to a wide range of yields, which poses challenges for modeling efforts. In light of this, we evaluated the performance of two machine learning algorithms, namely, the gradient boosting machine (GBM) and the extreme gradient boosting (XGBoost). The results demonstrated the superiority of the XGBoost-based model, achieving a determination coefficient (R2) of 0.89 and an RMSE of 0.3 t/ha for training, with an R2 of 0.61 and an RMSE of 0.42 t/ha for testing. The calibrated model improved the overall accuracy of yield estimates by up to 10% compared to GBM. Notably, total nitrogen content, slope, total column water vapor (TCWV), organic matter, and fractional vegetation cover (FVC) significantly influenced the predicted values. This study highlights the effectiveness of combining multi-source data and Sentinel-3 SLSTR, particularly proposing XGBoost as an alternative tool for accurately estimating yield at lower costs. Consequently, the findings suggest comprehensive and robust estimation models for spatially explicit yield forecasting and near-future yield projection using satellite data acquired two months before harvest. Future work can focus on assessing the suitability of agronomic practices in the region, thereby contributing to the early detection of yield anomalies and ensuring food security at the national level.

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Oil palm yield prediction across blocks from multi-source data using machine learning and deep learning

Cited by 8 publications

References 62 publications

Prediction of fruit characteristics of grafted plants of Camellia oleifera by deep neural networks

Prediction of fruit characteristics of grafted plants of Camellia oleifera by deep neural networks

Next-level vegetation health index forecasting: A ConvLSTM study using MODIS Time Series

Crop Yield Estimation Using Sentinel-3 SLSTR, Soil Data, and Topographic Features Combined with Machine Learning Modeling: A Case Study of Nepal

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