Agriculture

Research

14 pages, 9455 KiB

Open AccessArticle

Drought Damage Assessment for Crop Insurance Based on Vegetation Index by Unmanned Aerial Vehicle (UAV) Multispectral Images of Paddy Fields in Indonesia

by Yu Iwahashi, Gunardi Sigit, Budi Utoyo, Iskandar Lubis, Ahmad Junaedi, Bambang Hendro Trisasongko, I Made Anom Sutrisna Wijaya, Masayasu Maki, Chiharu Hongo and Koki Homma

Agriculture 2023, 13(1), 113; https://doi.org/10.3390/agriculture13010113 - 30 Dec 2022

Cited by 6 | Viewed by 2867

Abstract

Drought is increasingly threatening smallholder farmers in Southeast Asia. The crop insurance system is one of the promising countermeasures that was implemented in Indonesia in 2015. Because the damage assessment in the present system is conducted through direct investigations based on appearance, it [...] Read more.

Drought is increasingly threatening smallholder farmers in Southeast Asia. The crop insurance system is one of the promising countermeasures that was implemented in Indonesia in 2015. Because the damage assessment in the present system is conducted through direct investigations based on appearance, it is not objective and needs a long time to cover large areas. In this study, we investigated a rapid assessment method for paddy fields using a vegetation index (VI) taken by an unmanned aerial vehicle (UAV) with a multispectral camera in 2019 and 2021. Then, two ways of assessment for drought damage were tested: linear regression (LR) based on a visually assessed drought level (DL), and k-means clustering without an assessed DL. As a result, EVI2 could represent the damage level, showing the tendency of the decrease in the value along with the increasing DL. The estimated DL by both methods mostly coincided with the assessed DL, but the concordance rates varied depending on the locations and the number of assessed fields. Differences in the growth stage and rice cultivars also affected the results. This study revealed the feasibility of the UAV-based rapid and objective assessment method. Further data collection and analysis would be required for implementation in the future. Full article

(This article belongs to the Special Issue Remote-Sensing-Based Technologies for Crop Monitoring)

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15 pages, 4104 KiB

Open AccessArticle

Retrieval of Harmonized LAI Product of Agricultural Crops from Landsat OLI and Sentinel-2 MSI Time Series

by Jiří Tomíček, Jan Mišurec, Petr Lukeš and Markéta Potůčková

Agriculture 2022, 12(12), 2080; https://doi.org/10.3390/agriculture12122080 - 4 Dec 2022

Cited by 3 | Viewed by 2231

Abstract

In this study, an approach for the harmonized calculation of the Leaf Area Indices (LAIs) for agronomic crops from Sentinel-2 MSI and Landsat OLI multispectral satellite data is proposed in order to obtain a dense seasonal trajectory. It was developed and tested on [...] Read more.

In this study, an approach for the harmonized calculation of the Leaf Area Indices (LAIs) for agronomic crops from Sentinel-2 MSI and Landsat OLI multispectral satellite data is proposed in order to obtain a dense seasonal trajectory. It was developed and tested on dominant crops grown in the Czech Republic, including winter wheat, spring barley, winter rapeseed, alfalfa, sugar beet, and corn. The two-step procedure harmonizing Sentinel-2 MSI and Landsat OLI spectral data began with deriving NDVI, MSAVI, and NDWI_1610 vegetation indices (VIs) as proxy indicators of green biomass and foliage water content, the parameters contributing most to a stand’s spectral response. Second, a simple linear transformation was applied to the resulting VI values. The regression model itself was built on an artificial neural network, then trained on PROSAIL simulations data. The LAI estimates were validated using an extensive dataset of in situ measurements collected during 2017 and 2018 in the lowlands of the Central Bohemia Region. Very strong agreement was observed between LAI estimates from both Sentinel-2 MSI and Landsat OLI data and independent ground-based measurements (r between 0.7 and 0.98). Very good results were also achieved in the mutual comparison of Sentinel-2 and Landsat-based LAI datasets (rRMSE < 20%, r between 0.75 and 0.99). Using data from all currently available Sentinel-2 (A/B) and Landsat (8/9) satellites, a dense harmonized LAI time series can be created with high potential for use in precision agriculture. Full article

(This article belongs to the Special Issue Remote-Sensing-Based Technologies for Crop Monitoring)

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23 pages, 3966 KiB

Open AccessArticle

Prediction of Corn Yield in the USA Corn Belt Using Satellite Data and Machine Learning: From an Evapotranspiration Perspective

by Zhonglin Ji, Yaozhong Pan, Xiufang Zhu, Dujuan Zhang and Jiajia Dai

Agriculture 2022, 12(8), 1263; https://doi.org/10.3390/agriculture12081263 - 19 Aug 2022

Cited by 10 | Viewed by 3376

Abstract

The reliable prediction of corn yield for the United States of America is essential for effective food and energy management of the world. Three satellite-derived variables were selected, namely enhanced vegetation index (EVI), leaf area index (LAI) and land surface temperature (LST). The [...] Read more.

The reliable prediction of corn yield for the United States of America is essential for effective food and energy management of the world. Three satellite-derived variables were selected, namely enhanced vegetation index (EVI), leaf area index (LAI) and land surface temperature (LST). The least absolute shrinkage and selection operator (LASSO) was used for regression, while random forest (RF), support vector regression (SVR) and long short-term memory (LSTM) methods were selected for machine learning. The three variables serve as inputs to these methods, and their efficacy in predicting corn yield was assessed in relation to evapotranspiration (ET). The results confirmed that a high level of performance can be achieved for yield prediction (mean predicted R² = 0.63) by combining EVI + LAI + LST with the four methods. Among them, the best results were obtained by using LSTM (mean predicted R² = 0.67). EVI and LST provided extra and unique information in peak and early growth stages for corn yield, respectively, and the usefulness of including LAI was not readily apparent across the whole season, which was consistent with the field growing conditions affecting the ET of corn. The satellite-derived data and the methods used in this study could be used for predicting the yields of other crops in different regions. Full article

(This article belongs to the Special Issue Remote-Sensing-Based Technologies for Crop Monitoring)

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16 pages, 5652 KiB

Open AccessArticle

Image Segmentation of UAV Fruit Tree Canopy in a Natural Illumination Environment

by Zhongao Lu, Lijun Qi, Hao Zhang, Junjie Wan and Jiarui Zhou

Agriculture 2022, 12(7), 1039; https://doi.org/10.3390/agriculture12071039 - 16 Jul 2022

Cited by 9 | Viewed by 2679

Abstract

Obtaining canopy area, crown width, position, and other information from UAV aerial images and adjusting spray parameters in real-time according to this information is an important way to achieve precise pesticide application in orchards. However, the natural illumination environment in the orchard makes [...] Read more.

Obtaining canopy area, crown width, position, and other information from UAV aerial images and adjusting spray parameters in real-time according to this information is an important way to achieve precise pesticide application in orchards. However, the natural illumination environment in the orchard makes extracting the fruit tree canopy difficult. Hereto, an effective unsupervised image segmentation method is developed in this paper for fast fruit tree canopy acquisition from UAV images under natural illumination conditions. Firstly, the image is preprocessed using the shadow region luminance compensation method (SRLCM) that is proposed in this paper to reduce the interference of shadow areas. Then, use Naive Bayes to obtain multiple high-quality color features from 10 color models was combined with ensemble clustering to complete image segmentation. The segmentation experiments were performed on the collected apple tree images. The results show that the proposed method’s average precision rate, recall rate, and F1-score are 95.30%, 84.45%, and 89.53%, respectively, and the segmentation quality is significantly better than ordinary K-means and GMM algorithms. Full article

(This article belongs to the Special Issue Remote-Sensing-Based Technologies for Crop Monitoring)

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16 pages, 18148 KiB

Open AccessArticle

Identification of Male and Female Parents for Hybrid Rice Seed Production Using UAV-Based Multispectral Imagery

by Hanchao Liu, Yuan Qi, Wenwei Xiao, Haoxin Tian, Dehua Zhao, Ke Zhang, Junqi Xiao, Xiaoyang Lu, Yubin Lan and Yali Zhang

Agriculture 2022, 12(7), 1005; https://doi.org/10.3390/agriculture12071005 - 11 Jul 2022

Cited by 4 | Viewed by 3450

Abstract

Identifying and extracting male and female parent of hybrid rice and then accurately judging the spikelet flowering of male parents is the basis of hybrid rice pollination. Currently, male parent flowering information extraction for hybrid rice is basically obtained by manual recognition. In [...] Read more.

Identifying and extracting male and female parent of hybrid rice and then accurately judging the spikelet flowering of male parents is the basis of hybrid rice pollination. Currently, male parent flowering information extraction for hybrid rice is basically obtained by manual recognition. In this study, remote sensing images of parental rice fields were obtained with a multispectral camera carried by a UAV (Umanned Aerial Vehicle). Six kinds of visible light vegetation indices and four kinds of multispectral vegetation indices, together with two classification methods, pixel-based supervised classification and sample-based object-oriented classification, were applied to identify the male and female parents of hybrid rice, after which the accuracies of the methods were compared. The results showed that the visible vegetation index had a better effect in pixel-based supervised classification. The kappa coefficient of ExGR (Excess Green minus Excess Red index) classification was 0.9256 and the total accuracy was 0.9552. The extraction accuracy was higher than that of the other vegetation indices and object-oriented classification. In pixel-based supervised classification, the maximum likelihood method achieved the highest identification accuracy and shortest calculation time. Taking the remote sensing images obtained with a UAV as a data source, maximum likelihood supervised classification based on ExGR index can more effectively and quickly identify the field information of male and female parents of hybrid rice so as to provide a reference for determining optimal pollination timing for hybrid rice in large-scale seed production farms. Full article

(This article belongs to the Special Issue Remote-Sensing-Based Technologies for Crop Monitoring)

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14 pages, 79383 KiB

Open AccessFeature PaperArticle

Crop Growth Monitoring System in Vertical Farms Based on Region-of-Interest Prediction

by Yujin Hwang, Seunghyeon Lee, Taejoo Kim, Kyeonghoon Baik and Yukyung Choi

Agriculture 2022, 12(5), 656; https://doi.org/10.3390/agriculture12050656 - 30 Apr 2022

Cited by 10 | Viewed by 4629

Abstract

Vertical farms are to be considered the future of agriculture given that they not only use space and resources efficiently but can also consistently produce large yields. Recently, artificial intelligence has been introduced for use in vertical farms to boost crop yields, and [...] Read more.

Vertical farms are to be considered the future of agriculture given that they not only use space and resources efficiently but can also consistently produce large yields. Recently, artificial intelligence has been introduced for use in vertical farms to boost crop yields, and crop growth monitoring is an essential example of the type of automation necessary to manage a vertical farm system. Region of interest predictions are generally used to find crop regions from the color images captured by a camera for the monitoring of growth. However, most deep learning-based prediction approaches are associated with performance degradation issues in the event of high crop densities or when different types of crops are grown together. To address this problem, we introduce a novel method, termed pseudo crop mixing, a model training strategy that targets vertical farms. With a small amount of labeled crop data, the proposed method can achieve optimal performance. This is particularly advantageous for crops with a long growth period, and it also reduces the cost of constructing a dataset that must be frequently updated to support the various crops in existing systems. Additionally, the proposed method demonstrates robustness with new data that were not introduced during the learning process. This advantage can be used for vertical farms that can be efficiently installed and operated in a variety of environments, and because no transfer learning was required, the construction time for container-type vertical farms can be reduced. In experiments, we show that the proposed model achieved a performance of 76.9%, which is 12.5% better than the existing method with a dataset obtained from a container-type indoor vertical farm. Our codes and dataset will be available publicly. Full article

(This article belongs to the Special Issue Remote-Sensing-Based Technologies for Crop Monitoring)

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17 pages, 3908 KiB

Open AccessFeature PaperArticle

Agricultural Drought Monitoring System in Poland—Farmers’ Assessments vs. Monitoring Results (2021)

by Anna Jędrejek, Piotr Koza, Andrzej Doroszewski and Rafał Pudełko

Agriculture 2022, 12(4), 536; https://doi.org/10.3390/agriculture12040536 - 9 Apr 2022

Cited by 7 | Viewed by 2539

Abstract

The aim of this study is to compare the farmers’ viewpoint on agricultural drought with the results generated by the national Agricultural Drought Monitoring System (ADMS) in 2021. The authors attempted also to indicate effective methods of validating these results, which could serve [...] Read more.

The aim of this study is to compare the farmers’ viewpoint on agricultural drought with the results generated by the national Agricultural Drought Monitoring System (ADMS) in 2021. The authors attempted also to indicate effective methods of validating these results, which could serve as an objective tool of appeal made available to farmers as a part of an administrative procedure or directly included in the drought monitoring system, which, apart from soil and meteorological conditions, would take into account the actual condition of crops in the field. An analysis comparing farmers’ assessments with the ADMS results was presented for all (27,580 parcels) claims for compensation for losses in winter wheat crops submitted in the country. A detailed assessment of the impact of drought on yields was carried out for two pilot regions in the area most affected by agricultural drought in Poland (West Pomeranian Voivodeship, NUTS-2 PL42 region). The paper demonstrates a subjective assessment of incurred losses, performed by the farmers themselves. The difference between the “potential drought”—resulting from the meteorological and soil conditions—and the actual losses, which are also influenced by agro-technical factors, was indicated. The grounds for further development of the Agricultural Drought Monitoring System were the need to establish a method of estimating the impact of drought on crops, which will be based on unambiguous criteria and using high-resolution (temporal and spatial) remote sensing data. Full article

(This article belongs to the Special Issue Remote-Sensing-Based Technologies for Crop Monitoring)

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19 pages, 6055 KiB

Open AccessEditor’s ChoiceArticle

Comparative Evaluation of Land Surface Temperature Images from Unmanned Aerial Vehicle and Satellite Observation for Agricultural Areas Using In Situ Data

by Muhammad Awais, Wei Li, Sajjad Hussain, Muhammad Jehanzeb Masud Cheema, Weiguo Li, Rui Song and Chenchen Liu

Agriculture 2022, 12(2), 184; https://doi.org/10.3390/agriculture12020184 - 27 Jan 2022

Cited by 24 | Viewed by 3967

Abstract

Remotely-sensed data are a source of rich information and are valuable for precision agricultural tasks such as soil quality, plant disease analysis, crop stress assessment, and allowing for better management. It is necessary to validate the accuracy of land surface temperature (LST) that [...] Read more.

Remotely-sensed data are a source of rich information and are valuable for precision agricultural tasks such as soil quality, plant disease analysis, crop stress assessment, and allowing for better management. It is necessary to validate the accuracy of land surface temperature (LST) that is acquired from an unmanned aerial vehicle (UAV) and satellite-based remote sensing and verify these data by a comparison with in situ LST. Comprehensive studies at the field scale are still needed to understand the suitability of UAV imagery and resolution, for which ground measurement is used as a reference. In this study, we examined the accuracy of surface temperature data that were obtained from a thermal infrared (TIR) sensor placed on a UAV. Accordingly, we evaluated the LST from the Landsat 8 satellite for the same specific periods. We used contact thermometers to measure LSTs in situ for comparison and evaluation. Between 18 August and 2 September 2020, UAV imagery and in situ measurements were carried out. The effectiveness of high-resolution UAVs imagery and of Landsat 8 imagery was evaluated by considering a regression and correlation coefficient analysis. The data from the satellite photography was compared to the UAV imagery using statistical metrics after it had been pre-processed. Ground control points (GCPs) were collected to create a rigorous geo-referenced dataset of UAV imagery that could be compared to the geo-referenced satellite and aerial imagery. The UAV TIR LST showed higher accuracy (R² 0.89, 0.90, root-mean-square error (RMSE) 1.07, 0.70 °C) than the Landsat LST accuracy (R² 0.70, 0.73, (RMSE) 0.78 °C). The relationship between LST and the available soil water content (SWC) was also observed. The results suggested that the UAV-SMC correlation was negative (−0.85) for the image of DOY 230, while this value remains approximately constant (−0.86) for the DOY 245. Our results showed that satellite imagery that was coherent and correlated with UAV images could be useful to assess the general conditions of the field while the UAV favors localized circumscribed areas that the lowest resolution of satellites missed. Accordingly, our results could help with urban area and environmental planning decisions that take into account the thermal environment. Full article

(This article belongs to the Special Issue Remote-Sensing-Based Technologies for Crop Monitoring)

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22 pages, 40418 KiB

Open AccessArticle

Effectiveness of Common Preprocessing Methods of Time Series for Monitoring Crop Distribution in Kenya

by Rui Ni, Xiaohui Zhu, Yuping Lei, Xiaoxin Li, Wenxu Dong, Chuang Zhang, Tuo Chen, David M. Mburu and Chunsheng Hu

Agriculture 2022, 12(1), 79; https://doi.org/10.3390/agriculture12010079 - 7 Jan 2022

Cited by 2 | Viewed by 3675

Abstract

Accurate crop identification and spatial distribution mapping are important for crop production estimation and famine early warning, especially for food-deficit African agricultural countries. By evaluating existing preprocessing methods for classification using satellite image time series (SITS) in Kenya, this study aimed to provide [...] Read more.

Accurate crop identification and spatial distribution mapping are important for crop production estimation and famine early warning, especially for food-deficit African agricultural countries. By evaluating existing preprocessing methods for classification using satellite image time series (SITS) in Kenya, this study aimed to provide a low-cost method for cultivated land monitoring in sub-Saharan Africa that lacks financial support. SITS were composed of a set of MODIS Vegetation Indices (MOD13Q1) in 2018, and the classification method included the Support Vector Machine (SVM) and Random Forest (RF) classifier. Eight datasets obtained at three levels of preprocessing from MOD13Q1 were used in the classification: (1) raw SITS of vegetation indices (R-NDVI, R-EVI, and R-NDVI + R-EVI); (2) smoothed SITS of vegetation indices (S-NDVI); and (3) vegetation phenological data (P-NDVI, P-EVI, R-NDVI + P-NDVI, and P-NDVI-1). Both SVM and RF classification results showed that the “R-NDVI + R-EVI” dataset achieved the highest performance, while the three pure phenological datasets produced the lowest accuracy. Correlation analysis between variable importance and rainfall time series demonstrated that the vegetation index SITS during rainfall periods showed higher importance in RF classifiers, thus revealing the potential of saving computational costs. Considering the preprocessing cost of SITS and its negative impact on the classification accuracy, we recommend overlaying the original NDVI with the original EVI time series to map the crop distribution in Kenya. Full article

(This article belongs to the Special Issue Remote-Sensing-Based Technologies for Crop Monitoring)

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18 pages, 4732 KiB

Open AccessArticle

Seeding Crop Detection Framework Using Prototypical Network Method in UAV Images

by Di Zhang, Feng Pan, Qi Diao, Xiaoxue Feng, Weixing Li and Jiacheng Wang

Agriculture 2022, 12(1), 26; https://doi.org/10.3390/agriculture12010026 - 27 Dec 2021

Cited by 9 | Viewed by 2891

Abstract

With the development of unmanned aerial vehicle (UAV), obtaining high-resolution aerial images has become easier. Identifying and locating specific crops from aerial images is a valuable task. The location and quantity of crops are important for agricultural insurance businesses. In this paper, the [...] Read more.

With the development of unmanned aerial vehicle (UAV), obtaining high-resolution aerial images has become easier. Identifying and locating specific crops from aerial images is a valuable task. The location and quantity of crops are important for agricultural insurance businesses. In this paper, the problem of locating chili seedling crops in large-field UAV images is processed. Two problems are encountered in the location process: a small number of samples and objects in UAV images are similar on a small scale, which increases the location difficulty. A detection framework based on a prototypical network to detect crops in UAV aerial images is proposed. In particular, a method of subcategory slicing is applied to solve the problem, in which objects in aerial images have similarities at a smaller scale. The detection framework is divided into two parts: training and detection. In the training process, crop images are sliced into subcategories, and then these subcategory patch images and background category images are used to train the prototype network. In the detection process, a simple linear iterative clustering superpixel segmentation method is used to generate candidate regions in the UAV image. The location method uses a prototypical network to recognize nine patch images extracted simultaneously. To train and evaluate the proposed method, we construct an evaluation dataset by collecting the images of chilies in a seedling stage by an UAV. We achieve a location accuracy of 96.46%. This study proposes a seedling crop detection framework based on few-shot learning that does not require the use of labeled boxes. It reduces the workload of manual annotation and meets the location needs of seedling crops. Full article

(This article belongs to the Special Issue Remote-Sensing-Based Technologies for Crop Monitoring)

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