Agronomy

Research

24 pages, 7545 KiB

Open AccessEditor’s ChoiceArticle

Precise Estimation of Sugarcane Yield at Field Scale with Allometric Variables Retrieved from UAV Phantom 4 RTK Images

by Qiuyan Huang, Juanjuan Feng, Maofang Gao, Shuangshuang Lai, Guangping Han, Zhihao Qin, Jinlong Fan and Yuling Huang

Agronomy 2024, 14(3), 476; https://doi.org/10.3390/agronomy14030476 - 27 Feb 2024

Cited by 1 | Viewed by 2117

Abstract

The precise estimation of sugarcane yield at the field scale is urgently required for harvest planning and policy-oriented management. Sugarcane yield estimation from satellite remote sensing is available, but satellite image acquisition is affected by adverse weather conditions, which limits the applicability at [...] Read more.

The precise estimation of sugarcane yield at the field scale is urgently required for harvest planning and policy-oriented management. Sugarcane yield estimation from satellite remote sensing is available, but satellite image acquisition is affected by adverse weather conditions, which limits the applicability at the field scale. Secondly, existing approaches from remote sensing data using vegetation parameters such as NDVI (Normalized Difference Vegetation Index) and LAI (Leaf Area Index) have several limitations. In the case of sugarcane, crop yield is actually the weight of crop stalks in a unit of acreage. However, NDVI’s over-saturation during the vigorous growth period of crops results in significant limitations for sugarcane yield estimation using NDVI. A new sugarcane yield estimation is explored in this paper, which employs allometric variables indicating stalk magnitude (especially stalk height and density) rather than vegetation parameters indicating the leaf quantity of the crop. In this paper, UAV images with RGB bands were processed to create mosaic images of sugarcane fields and estimate allometric variables. Allometric equations were established using field sampling data to estimate sugarcane stalk height, diameter, and weight. Additionally, a stalk density estimation model at the pixel scale of the plot was created using visible light vegetation indices from the UAV images and ground survey data. The optimal stalk density estimation model was applied to estimate the number of plants at the pixel scale of the plot in this study. Then, the retrieved height, diameter, and density of sugarcane in the fields were combined with stalk weight data to create a model for estimating the sugarcane yield per plot. A separate dataset was used to validate the accuracy of the yield estimation. It was found that the approach presented in this study provided very accurate estimates of sugarcane yield. The average yield in the field was 93.83 Mg ha⁻¹, slightly higher than the sampling yield. The root mean square error of the estimation was 6.63 Mg ha⁻¹, which was 5.18% higher than the actual sampling yield. This study offers an alternative approach for precise sugarcane yield estimation at the field scale. Full article

(This article belongs to the Special Issue Art of Spectra: At the Crossroad of Agriculture and Remote Sensing Disciplines)

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17 pages, 3426 KiB

Open AccessArticle

Early Identification of Corn and Soybean Using Crop Growth Curve Matching Method

by Ruiqing Chen, Liang Sun, Zhongxin Chen, Deji Wuyun and Zheng Sun

Agronomy 2024, 14(1), 146; https://doi.org/10.3390/agronomy14010146 - 8 Jan 2024

Viewed by 1683

Abstract

The prompt and precise identification of corn and soybeans are essential for making informed decisions in agricultural production and ensuring food security. Nonetheless, conventional crop identification practices often occur after the completion of crop growth, lacking the timeliness required for effective agricultural management. [...] Read more.

The prompt and precise identification of corn and soybeans are essential for making informed decisions in agricultural production and ensuring food security. Nonetheless, conventional crop identification practices often occur after the completion of crop growth, lacking the timeliness required for effective agricultural management. To achieve in-season crop identification, a case study focused on corn and soybeans in the U.S. Corn Belt was conducted using a crop growth curve matching methodology. Initially, six vegetation indices datasets were derived from the publicly available HLS product, and then these datasets were integrated with known crop-type maps to extract the growth curves for both crops. Furthermore, crop-type information was acquired by assessing the similarity between time-series data and the respective growth curves. A total of 18 scenarios with varying input image numbers were arranged at approximately 10-day intervals to perform identical similarity recognition. The objective was to identify the scene that achieves an 80% recognition accuracy earliest, thereby establishing the optimal time for early crop identification. The results indicated the following: (1) The six vegetation index datasets demonstrate varying capabilities in identifying corn and soybean. Among those, the EVI index and two red-edge indices exhibit the best performance, all surpassing 90% accuracy when the entire time-series data are used as input. (2) EVI, NDPI, and REVI2 indices can achieve early identification, with an accuracy exceeding 80% around July 20, more than two months prior to the end of the crops’ growth periods. (3) Utilizing the same limited sample size, the early crop identification method based on crop growth curve matching outperforms the method based on random forest by approximately 20 days. These findings highlight the considerable potential and value of the crop growth curve matching method for early identification of corn and soybeans, especially when working with limited samples. Full article

(This article belongs to the Special Issue Art of Spectra: At the Crossroad of Agriculture and Remote Sensing Disciplines)

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20 pages, 12642 KiB

Open AccessArticle

Crop Classification in Mountainous Areas Using Object-Oriented Methods and Multi-Source Data: A Case Study of Xishui County, China

by Xiangyu Tian, Zhengchao Chen, Yixiang Li and Yongqing Bai

Agronomy 2023, 13(12), 3037; https://doi.org/10.3390/agronomy13123037 - 11 Dec 2023

Cited by 1 | Viewed by 1268

Abstract

Accurate crop mapping can represent the fundamental data for digital agriculture and ecological security. However, current crop classification methods perform poorly in mountainous areas with small cropland field parcel areas and multiple crops under cultivation. This study proposed a new object-oriented classification method [...] Read more.

Accurate crop mapping can represent the fundamental data for digital agriculture and ecological security. However, current crop classification methods perform poorly in mountainous areas with small cropland field parcel areas and multiple crops under cultivation. This study proposed a new object-oriented classification method to address this issue, using multi-source data and object features to achieve multi-crop classification in mountainous areas. Firstly, a deep learning method was employed to extract cropland field parcels in mountainous areas. Subsequently, the fusion of multi-source data was carried out based on cropland field parcels, while object features tailored for mountainous crops were designed for crop classification. Comparative analysis indicates that the proposed classification method demonstrates exceptional performance, enabling accurate mapping of various crops in mountainous regions. The F1 score and overall accuracy (OA) of the proposed method are 0.8449 and 0.8502, representing a 10% improvement over the pixel-based random forest classification results. Furthermore, qualitative analysis reveals that the proposed method exhibits higher classification accuracy for smaller plots and more precise delineation of crop boundaries. Finally, meticulous crop mapping of corn, sorghum, rice, and other crops in Xishui County, Guizhou Province, demonstrates the significant potential of the proposed method in crop classification within mountainous scenarios. Full article

(This article belongs to the Special Issue Art of Spectra: At the Crossroad of Agriculture and Remote Sensing Disciplines)

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24 pages, 17026 KiB

Open AccessEditor’s ChoiceArticle

Hyperspectral Estimation of Chlorophyll Content in Apple Tree Leaf Based on Feature Band Selection and the CatBoost Model

by Yu Zhang, Qingrui Chang, Yi Chen, Yanfu Liu, Danyao Jiang and Zijuan Zhang

Agronomy 2023, 13(8), 2075; https://doi.org/10.3390/agronomy13082075 - 7 Aug 2023

Cited by 15 | Viewed by 2331

Abstract

Leaf chlorophyll content (LCC) is a crucial indicator of nutrition in apple trees and can be applied to assess their growth status. Hyperspectral data can provide an important means for detecting the LCC in apple trees. In this study, hyperspectral data and the [...] Read more.

Leaf chlorophyll content (LCC) is a crucial indicator of nutrition in apple trees and can be applied to assess their growth status. Hyperspectral data can provide an important means for detecting the LCC in apple trees. In this study, hyperspectral data and the measured LCC were obtained. The original spectrum (OR) was pretreated using some spectral transformations. Feature bands were selected based on the competitive adaptive reweighted sampling (CARS) algorithm, random frog (RF) algorithm, elastic net (EN) algorithm, and the EN-RF and EN-CARS algorithms. Partial least squares regression (PLSR), random forest regression (RFR), and the CatBoost algorithm were used before and after grid search parameter optimization to estimate the LCC. The results revealed the following: (1) The spectrum after second derivative (SD) transformation had the highest correlation with LCC (–0.929); moreover, the SD-based model produced the highest accuracy, making SD an effective spectrum pretreatment method for apple tree LCC estimation. (2) Compared with the single band selection algorithm, the EN-RF algorithm had a better dimension reduction effect, and the modeling accuracy was generally higher. (3) CatBoost after grid search optimization had the best estimation effect, and the validation set of the SD-EN-CARS-CatBoost model after parameter optimization had the highest estimation accuracy, with the determination coefficient (R²), root mean square error (RMSE), and relative prediction deviation (RPD) reaching 0.923, 2.472, and 3.64, respectively. As such, the optimized SD-EN-CARS-CatBoost model, with its high accuracy and reliability, can be used to monitor the growth of apple trees, support the intelligent management of apple orchards, and facilitate the economic development of the fruit industry. Full article

(This article belongs to the Special Issue Art of Spectra: At the Crossroad of Agriculture and Remote Sensing Disciplines)

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17 pages, 5600 KiB

Open AccessArticle

Improving Nitrogen Status Diagnosis and Recommendation of Maize Using UAV Remote Sensing Data

by Jiaxing Liang, Wei Ren, Xiaoyang Liu, Hainie Zha, Xian Wu, Chunkang He, Junli Sun, Mimi Zhu, Guohua Mi, Fanjun Chen, Yuxin Miao and Qingchun Pan

Agronomy 2023, 13(8), 1994; https://doi.org/10.3390/agronomy13081994 - 27 Jul 2023

Cited by 3 | Viewed by 1510

Abstract

Effective in-season crop nitrogen (N) status diagnosis is important for precision crop N management, and remote sensing using an unmanned aerial vehicle (UAV) is one efficient means of conducting crop N nutrient diagnosis. Here, field experiments were conducted with six N levels and [...] Read more.

Effective in-season crop nitrogen (N) status diagnosis is important for precision crop N management, and remote sensing using an unmanned aerial vehicle (UAV) is one efficient means of conducting crop N nutrient diagnosis. Here, field experiments were conducted with six N levels and six maize hybrids to determine the nitrogen nutrition index (NNI) and yield, and to diagnose the N status of the hybrids combined with multi-spectral data. The NNI threshold values varied with hybrids and years, ranging from 0.99 to 1.17 in 2018 and 0.60 to 0.71 in 2019. A proper agronomic optimal N rate (AONR) was constructed and confirmed based on the measured NNI and yield. The NNI (R² = 0.64–0.79) and grain yield (R² = 0.70–0.73) were predicted well across hybrids using a random forest model with spectral, structural, and textural data (UAV). The AONRs calculated using the predicted NNI and yield were significantly correlated with the measured NNI (R² = 0.70 and 0.71 in 2018 and 2019, respectively) and yield (R² = 0.68 and 0.54 in 2018 and 2019, respectively). It is concluded that data fusion can improve in-season N status diagnosis for different maize hybrids compared to using only spectral data. Full article

(This article belongs to the Special Issue Art of Spectra: At the Crossroad of Agriculture and Remote Sensing Disciplines)

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14 pages, 2799 KiB

Open AccessEditor’s ChoiceArticle

Soil Organic Carbon Prediction Based on Different Combinations of Hyperspectral Feature Selection and Regression Algorithms

by Naijie Chang, Xiaowen Jing, Wenlong Zeng, Yungui Zhang, Zhihong Li, Di Chen, Daibing Jiang, Xiaoli Zhong, Guiquan Dong and Qingli Liu

Agronomy 2023, 13(7), 1806; https://doi.org/10.3390/agronomy13071806 - 7 Jul 2023

Cited by 6 | Viewed by 1469

Abstract

Cropland soil organic carbon (SOC) is crucial for global food security and mitigating the greenhouse effect. Accurate SOC prediction using hyperspectral data is essential for dynamic monitoring of soil carbon pools in croplands. However, effective methods to reduce hyperspectral data dimensionality and integrate [...] Read more.

Cropland soil organic carbon (SOC) is crucial for global food security and mitigating the greenhouse effect. Accurate SOC prediction using hyperspectral data is essential for dynamic monitoring of soil carbon pools in croplands. However, effective methods to reduce hyperspectral data dimensionality and integrate it with suitable regression algorithms for reliable prediction models are poorly understood. In this study, we analyzed 108 soil samples from Changting County, Fujian Province, China. Our objective was to evaluate the performance of various combinations of six feature selection methods and four regression algorithms for SOC prediction. Our findings are as follows: the combination of the Successive Projections Algorithm (SPA) and Partial Least Squares (PLS) yielded the most favorable results, with R² (0.61), RMSE (1.77 g/kg), and MAE (1.48 g/kg). Moreover, we determined the relative importance of variables, with the following ranking: 696 nm > 892 nm > 783 nm > 1641 nm > 1436 nm > 396 nm > 392 nm > 2239 nm > 2129 nm. Notably, 696 nm exhibited the highest importance in the SPA-PLS model, with the Variable Importance in Projection (VIP) value of 1.22. This study provides profound insights into feature selection methods and regression algorithms for SOC prediction, highlighting the superiority of SPA-PLS as the optimal combination. Full article

(This article belongs to the Special Issue Art of Spectra: At the Crossroad of Agriculture and Remote Sensing Disciplines)

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22 pages, 10080 KiB

Open AccessArticle

Effectiveness of Utilizing Remote Sensing and GIS Techniques to Estimate the Exposure to Organophosphate Pesticides Drift over Macon, Alabama

by Gamal El Afandi, Hossam Ismael, Souleymane Fall and Ramble Ankumah

Agronomy 2023, 13(7), 1759; https://doi.org/10.3390/agronomy13071759 - 29 Jun 2023

Cited by 3 | Viewed by 1836

Abstract

Farmers utilize pesticides extensively on their farms to control weeds and insects, as well as increase crop productivity. Despite these advantages, their excessive use poses a serious threat, particularly to the population living at the nexus of urban and rural areas. Exposure to [...] Read more.

Farmers utilize pesticides extensively on their farms to control weeds and insects, as well as increase crop productivity. Despite these advantages, their excessive use poses a serious threat, particularly to the population living at the nexus of urban and rural areas. Exposure to pesticide drift can be investigated using geospatial tools. Remote sensing technology and Geographic Information Systems (GIS) techniques have been used intensively and constitute trusted tools in different sectors, especially in agriculture. Remote sensing depends on processing the electromagnetic radiation reflected and emitted from the ground target and can be used to identify the main units of Land Use Land Cover (LULC), in addition to measuring crop areas exposed to pesticides. GIS has powerful tools for building a spatial geo-database of pesticide exposure drift. Therefore, the major objective of the research was to explore the effectiveness of using remote sensing and GIS techniques to estimate the exposure organophosphate pesticides drift over Macon County, Alabama. To achieve this objective, the Cropland Data Layer (CDL) dataset, the available pesticide usage data, and gridded population data were used to estimate the potential pesticide drift on the Macon County level. In addition, the AgDRIFT model was used to estimate the potential drift of pesticides from their intended targets at the field level. The results indicated that 6.6% of Macon County’s residents are considered potentially severely exposed, and the potentially affected population resides primarily in rural areas. In comparison, 23% of residents of the urban-rural interface are considered to have potentially medium to high exposure. In addition, 38% of residents living in suburban areas are considered to have potentially low-to-medium exposure. The results indicated that both GIS and remote sensing could play an effective role in estimating pesticide exposure drift at the State or County level. In addition, the AgDRIFT model was more appropriate for estimating pesticide drift at the field level. Full article

(This article belongs to the Special Issue Art of Spectra: At the Crossroad of Agriculture and Remote Sensing Disciplines)

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Graphical abstract

18 pages, 6319 KiB

Open AccessEditor’s ChoiceArticle

In-Season Crop Type Detection by Combing Sentinel-1A and Sentinel-2 Imagery Based on the CNN Model

by Mingxiang Mao, Hongwei Zhao, Gula Tang and Jianqiang Ren

Agronomy 2023, 13(7), 1723; https://doi.org/10.3390/agronomy13071723 - 27 Jun 2023

Cited by 11 | Viewed by 1753

Abstract

In-season crop-type maps are required for a variety of agricultural monitoring and decision-making applications. The earlier the crop type maps of the current growing season are obtained, the more beneficial it is for agricultural decision-making and management. With the availability of a large [...] Read more.

In-season crop-type maps are required for a variety of agricultural monitoring and decision-making applications. The earlier the crop type maps of the current growing season are obtained, the more beneficial it is for agricultural decision-making and management. With the availability of a large amount of high spatiotemporal resolution remote sensing data, different data sources are expected to increase the frequency of data acquisition, which can provide more information in the early season. To explore the potential of integrating different data sources, a Dual-1DCNN algorithm was built based on the CNN model in this study. Moreover, an incremental training method was used to attain the network on each data acquisition date and obtain the best detection date for each crop type in the early season. A case study for Hengshui City in China was conducted using time series of Sentinel-1A (S1A) and Sentinel-2 (S2) attained in 2019. To verify this method, the classical methods support vector machine (SVM), random forest (RF), and Mono-1DCNN were implemented. The input for SVM and RF was S1A and S2 data, and the input for Mono-1DCNN was S2 data. The results demonstrated the following: (1) Dual-1DCNN achieved an overall accuracy above 85% at the earliest time.; (2) all four types of models achieved high accuracy (F1s were greater than 90%) on summer maize after sowing one month later; (3) for cotton and common yam rhizomes, Dual-1DCNN performed best, with its F1 reaching 85% within 2 months after cotton sowing, 15 days, 20 days, and 45 days ahead of Mono-1DCNN, SVM, and RF, respectively, and its extraction of the common yam rhizome was achieved 1–2 months earlier than other methods within the acceptable accuracy. These results confirmed that Dual-1DCNN offered significant potential in the in-season detection of crop types. Full article

(This article belongs to the Special Issue Art of Spectra: At the Crossroad of Agriculture and Remote Sensing Disciplines)

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16 pages, 10070 KiB

Open AccessArticle

Evaluation of Field Germination of Soybean Breeding Crops Using Multispectral Data from UAV

by Rashid Kurbanov, Veronika Panarina, Andrey Polukhin, Yakov Lobachevsky, Natalia Zakharova, Maxim Litvinov, Nazih Y. Rebouh, Dmitry E. Kucher, Elena Gureeva, Ekaterina Golovina, Pavel Yatchuk, Victoria Rasulova and Abdelraouf M. Ali

Agronomy 2023, 13(5), 1348; https://doi.org/10.3390/agronomy13051348 - 11 May 2023

Cited by 4 | Viewed by 2181

Abstract

The use of multispectral aerial photography data contributes to the study of soybean plants by obtaining objective data. The evaluation of field germination of soybean crops was carried out using multispectral data (MSD). The purpose of this study was to develop ranges of [...] Read more.

The use of multispectral aerial photography data contributes to the study of soybean plants by obtaining objective data. The evaluation of field germination of soybean crops was carried out using multispectral data (MSD). The purpose of this study was to develop ranges of field germination of soybean plants according to multispectral survey data from an unmanned aerial vehicle (UAV) for three years (2020, 2021, and 2022). As part of the ground-based research, the number of plants that sprang up per unit area was calculated and expressed as a percentage of the seeds sown. A DJI Matrice 200 Series v2 unmanned aerial vehicle and a MicaSense Altum multispectral camera were used for multispectral aerial photography. The correlation between ground-based and multispectral data was 0.70–0.75. The ranges of field germination of soybean breeding crops, as well as the vegetation indices (VIs) normalized difference vegetation index (NDVI), normalized difference red edge index (NDRE), and chlorophyll index green (ClGreen) were calculated according to Sturges’ rule. The accuracy of the obtained ranges was estimated using the mean absolute percentage error (MAPE). The MAPE values did not exceed 10% for the ranges of the NDVI and ClGreen vegetation indices, and were no more than 18% for the NDRE index. The final values of the MAPE for the three years did not exceed 10%. The developed software for the automatic evaluation of the germination of soybean crops contributed to the assessment of the germination level of soybean breeding crops using multispectral aerial photography data. The software considers data of the three vegetation indices and calculated ranges, and creates an overview layer to visualize the germination level of the breeding plots. The developed method contributes to the determination of field germination for numerous breeding plots and speeds up the process of breeding new varieties. Full article

(This article belongs to the Special Issue Art of Spectra: At the Crossroad of Agriculture and Remote Sensing Disciplines)

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23 pages, 5879 KiB

Open AccessArticle

Integration Vis-NIR Spectroscopy and Artificial Intelligence to Predict Some Soil Parameters in Arid Region: A Case Study of Wadi Elkobaneyya, South Egypt

by Moatez A. El-Sayed, Alaa H. Abd-Elazem, Ali R. A. Moursy, Elsayed Said Mohamed, Dmitry E. Kucher and Mohamed E. Fadl

Agronomy 2023, 13(3), 935; https://doi.org/10.3390/agronomy13030935 - 21 Mar 2023

Cited by 7 | Viewed by 2788

Abstract

Understanding and determining soil properties is reflected in improving farm management and crop production. Soil salinity, pH and calcium carbonate are among the factors affecting the soil’s physical and chemical properties. Hence, their estimation is very important for agricultural management, especially in arid [...] Read more.

Understanding and determining soil properties is reflected in improving farm management and crop production. Soil salinity, pH and calcium carbonate are among the factors affecting the soil’s physical and chemical properties. Hence, their estimation is very important for agricultural management, especially in arid regions (Wadi Elkobaneyya valley, located in the northwest of Aswan Governorate, Upper Egypt). The study objectives were to characterize and develop prediction models for soil salinity, pH and calcium carbonate (CaCO₃) using integration soil analysis and spectral reflectance vis-NIR spectroscopy. To achieve the study objectives, three multivariate regression models: Partial Least Squares Regression (PLSR), Multivariate Adaptive Regression Splines (MARS) and Least Square-Support Vector Regression (LS-SVR)); and two machine learning algorithms, Random Forest (RF) and Artificial Neural Networks (ANN) were used. Ninety-six surface soil samples were collected from the study area at depths 0–5 cm. The data were divided into a calibration dataset (70% of the total) and a validation dataset (30% of the total dataset). The obtained results represent that the PLSR model was the best model for soil pH parameters where R² of calibration and validation predictability = 0.68 and 0.52, respectively. The LS-SVR model was the best model to predict soil Electrical Conductivity (EC) and soil Calcium Carbonate (CaCO₃) content, with R² 0.70 and 0.74 for calibration and R² 0.26 and 0.47 for validation, respectively. On the other hand, the results of the implemented machine learning algorithm model showed that RF was the best model to predict soil pH and CaCO₃, as the R² was 0.82 for calibration and 0.57 for validation, respectively. Nevertheless, the best model for predicting soil EC was ANN, with an R² of 0.96 for calibration and 64 for validation. The results show the advantages of machine learning models for predicting soil EC, pH and CaCO₃ by Vis-NIR spectroscopy. Therefore, Vis-NIR spectroscopy is considered faster and more cost-efficient and can be further used in environmental monitoring and precision farming. Full article

(This article belongs to the Special Issue Art of Spectra: At the Crossroad of Agriculture and Remote Sensing Disciplines)

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16 pages, 4088 KiB

Open AccessArticle

Fourier-Transform Infrared Spectral Inversion of Soil Available Potassium Content Based on Different Dimensionality Reduction Algorithms

by Weiyan Wang, Yungui Zhang, Zhihong Li, Qingli Liu, Wenqiang Feng, Yulan Chen, Hong Jiang, Hui Liang and Naijie Chang

Agronomy 2023, 13(3), 617; https://doi.org/10.3390/agronomy13030617 - 21 Feb 2023

Cited by 4 | Viewed by 1764

Abstract

Estimating the available potassium (AK) in soil can help improve field management and crop production. Fourier-transform infrared (FTIR) spectroscopy is one of the most promising techniques for the fast and real-time analysis of soil AK content. However, the successful estimation of soil AK [...] Read more.

Estimating the available potassium (AK) in soil can help improve field management and crop production. Fourier-transform infrared (FTIR) spectroscopy is one of the most promising techniques for the fast and real-time analysis of soil AK content. However, the successful estimation of soil AK content by FTIR depends on the proper selection of appropriate spectral dimensionality reduction techniques. To magnify the subtle spectral signals concerning AK content and improve the understanding of the characteristic FTIR wavelengths of AK content, a total of 145 soil samples were collected in an agricultural site located in the southwest part of Sichuan, China, and three typical spectral dimensionality reduction methods—the successive projections algorithm (SPA), simulated annealing algorithm (SA) and competitive adaptive reweighted sampling (CARS)—were adopted to select the appropriate spectral variable. Then, partial least squares regression (PLSR) was utilized to establish AK inversion models by incorporating the optimal set of spectral variables extracted by different dimensionality reduction algorithms. The accuracy of each inversion model was tested based on the coefficient of determination (R²), root mean square error (RMSE) and mean absolute value error (MAE), and the contribution of the inversion model variables was explored. The results show that: (1) The application of spectral dimensionality reduction is a useful technique for isolating specific components of multicomponent spectra, and as such is a powerful tool to improve and expand the predicted potential of the spectroscopy of soil AK content. Compared with the SA and CARS algorithms, the SPA was more suitable for soil AK content inversion. (2) The inversion model results showed that the characteristic wavelengths were mainly around 777 nm, 1315 nm, 1375 nm, 1635 nm, 1730 nm and 3568–3990 nm. (3) Comparing the performances of different inversion models, the SPA–PLSR model (R²= 0.49, RMSE = 22.80, MAE = 16.82) was superior to the SA–PLSR and CARS–PLSR models, which has certain guiding significance for the rapid detection of soil AK content. Full article

(This article belongs to the Special Issue Art of Spectra: At the Crossroad of Agriculture and Remote Sensing Disciplines)

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21 pages, 6284 KiB

Open AccessArticle

Soil Salinity Assessing and Mapping Using Several Statistical and Distribution Techniques in Arid and Semi-Arid Ecosystems, Egypt

by Mohamed E. Fadl, Mohamed E. M. Jalhoum, Mohamed A. E. AbdelRahman, Elsherbiny A. Ali, Wessam R. Zahra, Ahmed S. Abuzaid, Costanza Fiorentino, Paola D’Antonio, Abdelaziz A. Belal and Antonio Scopa

Agronomy 2023, 13(2), 583; https://doi.org/10.3390/agronomy13020583 - 17 Feb 2023

Cited by 9 | Viewed by 3317

Abstract

Oasis lands in Egypt are commonly described as salty soils; therefore, waterlogging and higher soil salinity are major obstacles to sustainable agricultural development. This study aims to map and assess soil salinization at El-Farafra Oasis in the Egypt Western Desert based on salinity [...] Read more.

Oasis lands in Egypt are commonly described as salty soils; therefore, waterlogging and higher soil salinity are major obstacles to sustainable agricultural development. This study aims to map and assess soil salinization at El-Farafra Oasis in the Egypt Western Desert based on salinity indices, Imaging Spectroscopy (IS), and statistical techniques. The regression model was developed to test the relationship between the electrical conductivity (EC_e) of 70 surface soil samples and seven salinity indices (SI 1, SI 2, SI 5, SI 6, SI 7, SI 8, and SI 9) to produce soil salinity maps depending on Landsat-8 (OLI) images. The investigations of soil salinization and salinity indices were validated in a studied area based on 30 soil samples; the obtained results represented that all salinity indices have shown satisfactory correlations between EC_e values for each soil sample site and salinity indices, except for the SI 5 index that present non-significant correlations with R² value of 0.2688. The SI 8 index shows a higher negative significant correlation with EC_e and an R² value of 0.6356. There is a significant positive correlation at the (p < 0.01) level between SI 9 and EC_e (r = 0.514), a non-significant correlation at the (p < 0.05) level between soil EC_e and SI 1 index (r = 0.495), and the best-verified salinity index was for SI 7 that has a low estimated RMSE error of 8.58. Finally, the highest standard error (R²) was represented as EC_e (dS m⁻¹) with an R² of 0.881, and the lowest one was SI 9 with an R² of 0.428, according to Tukey’s test analysis. Therefore, observing and investigating soil salinity are essential requirements for appropriate natural resource management plans in the future. Full article

(This article belongs to the Special Issue Art of Spectra: At the Crossroad of Agriculture and Remote Sensing Disciplines)

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15 pages, 3581 KiB

Open AccessArticle

A New Framework for Winter Wheat Yield Prediction Integrating Deep Learning and Bayesian Optimization

by Yan Di, Maofang Gao, Fukang Feng, Qiang Li and Huijie Zhang

Agronomy 2022, 12(12), 3194; https://doi.org/10.3390/agronomy12123194 - 16 Dec 2022

Cited by 20 | Viewed by 2995

Abstract

Early prediction of winter wheat yield at the regional scale is essential for food policy making and food security, especially in the context of population growth and climate change. Agricultural big data and artificial intelligence (AI) are key technologies for smart agriculture, bringing [...] Read more.

Early prediction of winter wheat yield at the regional scale is essential for food policy making and food security, especially in the context of population growth and climate change. Agricultural big data and artificial intelligence (AI) are key technologies for smart agriculture, bringing cost-effective solutions to the agricultural sector. Deep learning-based crop yield forecast has currently emerged as one of the key methods for guiding agricultural production. In this study, we proposed a Bayesian optimization-based long- and short-term memory model (BO-LSTM) to construct a multi-source data fusion-driven crop growth feature extraction algorithm for winter wheat yield prediction. The yield prediction performance of BO-LSTM, support vector machine (SVM), and least absolute shrinkage and selection operator (Lasso) was then compared with multi-source data as input variables. The results showed that effective deep learning hyperparameter optimization is made possible by Bayesian optimization. The BO-LSTM (RMSE = 177.84 kg/ha, R² = 0.82) model had the highest accuracy of yield prediction with the input combination of “GPP + Climate + LAI + VIs”. BO-LSTM and SVM (RMSE = 185.7 kg/ha, R² = 0.80) methods outperformed linear regression Lasso (RMSE = 214.5 kg/ha, R² = 0.76) for winter wheat yield estimation. There were also differences between machine learning and deep learning, BO-LSTM outperformed SVM. indicating that the BO-LSTM model was more effective at capturing data correlations. In order to further verify the robustness of the BO-LSTM method, we explored the performance estimation performance of BO-LSTM in different regions. The results demonstrated that the BO-LSTM model could obtain higher estimation accuracy in regions with concentrated distribution of winter wheat cultivation and less influence of human factors. The approach used in this study can be expected to forecast crop yields, both in regions with a deficit of data and globally; it can also simply and effectively forecast winter wheat yields in a timely way utilizing publicly available multi-source data. Full article

(This article belongs to the Special Issue Art of Spectra: At the Crossroad of Agriculture and Remote Sensing Disciplines)

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