Ensemble and single algorithm models to handle multicollinearity of UAV vegetation indices for predicting rice biomass

Derraz, Radhwane; Muharam, Farrah Melissa; Khairudin, Nurkhairizan; Jaafar, Noraini Ahmad; Yap, Ng Keng

doi:10.1016/j.compag.2023.107621

Cited by 17 publications

(4 citation statements)

References 59 publications

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“…The research was carried out for two months, starting from August 2022 to September 2022. Simple random sampling was used to determine the number of samples (Devi, 2020;Derraz et al, 2023;Nathanel et al, 2023). The Slovin method was used to determine the sample size,samples were obtained using the following formula (Fitri and Nainggolan, 2022;Juni, Efrianti and Fifian, 2022;Jati and Soebagyo, 2023): The data used in this study is primary data, collected through direct interviews with farmers using a questionnaire that has been prepared previously.…”

Section: Methods the Location Sampling Technique And The Research Datamentioning

confidence: 99%

Analyzing The Cost of Paddy Rice Labor in Indonesia: A Case Study in Ten Tons Syngenta Project

Khoiriyah,

Isnaini,

Iriany

et al. 2023

JSEP

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Rice is a commodity strategy and a basic food for nearly all Indonesian households. The purpose of this study is to describe the labor utilization and costs associated with all farming activities, as well as the income and efficiency of lowland rice farming. The study took place in the Ten Tons Syngenta area, specifically in Glanggang Village, Beji District, Pasuruan Regency, East Java. The study took place between August and September 2022. The sample was determined using a 20% random sampling technique, which in the collection of 22 samples of farmers. The research data were derived from primary sources and were gathered through direct interviews with farmers. The results indicated that labor costs is so high in land preparation and planting (53.50 percent) dominated work in lowland rice farming, followed by harvesting (17.37 percent), weeding (14.74 percent), fertilizing (8.92 percent), and spraying (5.4 percent ). Lowland rice farmers earn Rp. 5,381,567 per hectare and have an R/C ratio of 1.35. The application of advanced technology, particularly in land preparation and planting, is critical for increasing the income and efficiency of lowland rice farming in the Syngenta ten-ton area in order to support rice self-sufficiency in the run-up to Indonesia's 100th anniversary of independence in 2045.

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Section: Methods the Location Sampling Technique And The Research Datamentioning

confidence: 99%

Analyzing The Cost of Paddy Rice Labor in Indonesia: A Case Study in Ten Tons Syngenta Project

Khoiriyah,

Isnaini,

Iriany

et al. 2023

JSEP

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“…Ensemble learning integrates the predictions of multiple base models to construct a meta-model, enhancing overall performance. Unlike individual models, ensemble learning frameworks leverage the strengths of different models, improving generalization and mitigating overfitting to some extent [47,48]. The authors divided the original models into meta-models and base models for combined regression.…”

Section: Regression Techniquesmentioning

confidence: 99%

Pretrained Deep Learning Networks and Multispectral Imagery Enhance Maize LCC, FVC, and Maturity Estimation

Hu,

Feng,

Wang

et al. 2024

Remote Sensing

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Crop leaf chlorophyll content (LCC) and fractional vegetation cover (FVC) are crucial indicators for assessing crop health, growth development, and maturity. In contrast to the traditional manual collection of crop trait parameters, unmanned aerial vehicle (UAV) technology rapidly generates LCC and FVC maps for breeding materials, facilitating prompt assessments of maturity information. This study addresses the following research questions: (1) Can image features based on pretrained deep learning networks and ensemble learning enhance the estimation of remote sensing LCC and FVC? (2) Can the proposed adaptive normal maturity detection (ANMD) algorithm effectively monitor maize maturity based on LCC and FVC maps? We conducted the following tasks: (1) Seven phases (tassel initiation to maturity) of maize canopy orthoimages and corresponding ground-truth data for LCC and six phases of FVC using UAVs were collected. (2) Three features, namely vegetation indices (VI), texture features (TF) based on Gray Level Co-occurrence Matrix, and deep features (DF), were evaluated for LCC and FVC estimation. Moreover, the potential of four single-machine learning models and three ensemble models for LCC and FVC estimation was evaluated. (3) The estimated LCC and FVC were combined with the proposed ANMD to monitor maize maturity. The research findings indicate that (1) image features extracted from pretrained deep learning networks more accurately describe crop canopy structure information, effectively eliminating saturation effects and enhancing LCC and FVC estimation accuracy. (2) Ensemble models outperform single-machine learning models in estimating LCC and FVC, providing greater precision. Remarkably, the stacking + DF strategy achieved optimal performance in estimating LCC (coefficient of determination (R2): 0.930; root mean square error (RMSE): 3.974; average absolute error (MAE): 3.096); and FVC (R2: 0.716; RMSE: 0.057; and MAE: 0.044). (3) The proposed ANMD algorithm combined with LCC and FVC maps can be used to effectively monitor maize maturity. Establishing the maturity threshold for LCC based on the wax ripening period (P5) and successfully applying it to the wax ripening-mature period (P5–P7) achieved high monitoring accuracy (overall accuracy (OA): 0.9625–0.9875; user’s accuracy: 0.9583–0.9933; and producer’s accuracy: 0.9634–1). Similarly, utilizing the ANMD algorithm with FVC also attained elevated monitoring accuracy during P5–P7 (OA: 0.9125–0.9750; UA: 0.878–0.9778; and PA: 0.9362–0.9934). This study offers robust insights for future agricultural production and breeding, offering valuable insights for the further exploration of crop monitoring technologies and methodologies.

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“…A UAV as a platform combined with spectral sensors has become an efficient method for biomass estimation of various crops. Good results were obtained for maize [12,13], rice [14], barley [15,16], wheat, and grass [17]. In the related studies, a variety of vegetative indices (VIs), including the normalized difference vegetation index (NDVI) [18], green normalized differential vegetation index (GNDVI) [18], and triangular vegetation index (TVI) [19], were used for biomass estimation.…”

Section: Introductionmentioning

confidence: 99%

Biomass Estimation of Milk Vetch Using UAV Hyperspectral Imagery and Machine Learning

Hu,

Zhou,

Cao

et al. 2024

Remote Sensing

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Milk vetch (Astragalus sinicus L.) is a winter-growing plant that can enhance soil fertility and provide essential nutrients for subsequent season crops. The fertilizing capacity of milk vetch is closely related to its above-ground biomass. Compared to the manual measurement methods of milk vetch biomass, remote sensing-based estimation methods have the advantages of rapid, noninvasive, and large-scale measurement. However, few studies have been conducted on remote sensing-based estimation of milk vetch biomass. To address this shortcoming, this study proposes combining unmanned aerial vehicle (UAV)-based hyperspectral imagery and machine learning algorithms for accurate estimation of milk vetch biomass. Through the analysis of hyperspectral images and feature selection based on the Pearson correlation and principal component analysis, vegetation indices (VIs), including near-infrared reflectance (NIR), red-edge spectral transform index (RE), and difference vegetation index (DVI), are selected as estimation metrics of the model development process. Four machine learning methods, including random forest (RF), multiple linear regression (MLR), deep neural network (DNN), and support vector machine (SVM), are used to construct the biomass models. The results show that the RF estimation model exhibits the highest coefficient of determination (R2) of 0.950 and the lowest relative root-mean-squared error (RRMSE) of 14.86% among all the models. Notably, the DNN model demonstrates promising performance on the test set, with the R2 and RRMSE values slightly superior and inferior to those of the RF, respectively. The proposed method based on UAV imagery and machine learning can provide an accurate and reliable large-scale estimation of milk vetch biomass.

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Ensemble and single algorithm models to handle multicollinearity of UAV vegetation indices for predicting rice biomass

Cited by 17 publications

References 59 publications

Analyzing The Cost of Paddy Rice Labor in Indonesia: A Case Study in Ten Tons Syngenta Project

Analyzing The Cost of Paddy Rice Labor in Indonesia: A Case Study in Ten Tons Syngenta Project

Pretrained Deep Learning Networks and Multispectral Imagery Enhance Maize LCC, FVC, and Maturity Estimation

Biomass Estimation of Milk Vetch Using UAV Hyperspectral Imagery and Machine Learning

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