Early Identification of Corn and Soybean Using Crop Growth Curve Matching Method
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 e...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-01-01
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| Series: | Agronomy |
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| Online Access: | https://www.mdpi.com/2073-4395/14/1/146 |
| _version_ | 1827372962348531712 |
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| author | Ruiqing Chen Liang Sun Zhongxin Chen Deji Wuyun Zheng Sun |
| author_facet | Ruiqing Chen Liang Sun Zhongxin Chen Deji Wuyun Zheng Sun |
| author_sort | Ruiqing Chen |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-08T11:08:54Z |
| format | Article |
| id | doaj.art-e5217d37c5714139a82522f9aa2b2805 |
| institution | Directory Open Access Journal |
| issn | 2073-4395 |
| language | English |
| last_indexed | 2024-03-08T11:08:54Z |
| publishDate | 2024-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Agronomy |
| spelling | doaj.art-e5217d37c5714139a82522f9aa2b28052024-01-26T14:25:59ZengMDPI AGAgronomy2073-43952024-01-0114114610.3390/agronomy14010146Early Identification of Corn and Soybean Using Crop Growth Curve Matching MethodRuiqing Chen0Liang Sun1Zhongxin Chen2Deji Wuyun3Zheng Sun4State Key Laboratory of Efficient Utilization of Arid and Semi-Arid Arable Land in Northern China, The Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081, ChinaState Key Laboratory of Efficient Utilization of Arid and Semi-Arid Arable Land in Northern China, The Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081, ChinaDigitization and Informatics Division, Food and Agriculture Organization of the United Nations, 00153 Rome, ItalyState Key Laboratory of Efficient Utilization of Arid and Semi-Arid Arable Land in Northern China, The Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081, ChinaState Key Laboratory of Efficient Utilization of Arid and Semi-Arid Arable Land in Northern China, The Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081, ChinaThe 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.https://www.mdpi.com/2073-4395/14/1/146early identificationcrop growth curvecornsoybeancrop-type classification |
| spellingShingle | Ruiqing Chen Liang Sun Zhongxin Chen Deji Wuyun Zheng Sun Early Identification of Corn and Soybean Using Crop Growth Curve Matching Method Agronomy early identification crop growth curve corn soybean crop-type classification |
| title | Early Identification of Corn and Soybean Using Crop Growth Curve Matching Method |
| title_full | Early Identification of Corn and Soybean Using Crop Growth Curve Matching Method |
| title_fullStr | Early Identification of Corn and Soybean Using Crop Growth Curve Matching Method |
| title_full_unstemmed | Early Identification of Corn and Soybean Using Crop Growth Curve Matching Method |
| title_short | Early Identification of Corn and Soybean Using Crop Growth Curve Matching Method |
| title_sort | early identification of corn and soybean using crop growth curve matching method |
| topic | early identification crop growth curve corn soybean crop-type classification |
| url | https://www.mdpi.com/2073-4395/14/1/146 |
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