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Automatic measurement of FVC using digital images captured by mobile smart devices is a potential direction for future research on field survey methods in plant ecology, and this algorithm is crucial for accurate FVC measurement. However, there is a lack of insight into the influence of illumination on the accuracy of FVC measurements. Therefore, the main objective of this research is to assess the adaptiveness and performance of different algorithms under varying light conditions for FVC measurements and to deepen our understanding of the influence of illumination on FVC measurement.<\/jats:p><\/jats:sec>Methods and results<\/jats:title>Based on a literature survey, we selected four algorithms that have been reported to have high accuracy in automatic FVC measurements. The first algorithm (Fun01) identifies green plants based on the combination of$$R\/G$$<\/jats:tex-math>R<\/mml:mi>\/<\/mml:mo>G<\/mml:mi><\/mml:mrow><\/mml:math><\/jats:alternatives><\/jats:inline-formula>,$$B\/G$$<\/jats:tex-math>B<\/mml:mi>\/<\/mml:mo>G<\/mml:mi><\/mml:mrow><\/mml:math><\/jats:alternatives><\/jats:inline-formula>, and$$ExG$$<\/jats:tex-math>ExG<\/mml:mi><\/mml:mrow><\/mml:math><\/jats:alternatives><\/jats:inline-formula>($$R$$<\/jats:tex-math>R<\/mml:mi><\/mml:math><\/jats:alternatives><\/jats:inline-formula>,$$G$$<\/jats:tex-math>G<\/mml:mi><\/mml:math><\/jats:alternatives><\/jats:inline-formula>, and$$B$$<\/jats:tex-math>B<\/mml:mi><\/mml:math><\/jats:alternatives><\/jats:inline-formula>are the actual pixel digital numbers from the images based on each RGB channel,$$ExG$$<\/jats:tex-math>ExG<\/mml:mi><\/mml:mrow><\/mml:math><\/jats:alternatives><\/jats:inline-formula>is the abbreviation of the Excess Green index), the second algorithm (Fun02) is a decision tree that uses color properties to discriminate plants from the background, the third algorithm (Fun03) uses$$ExG-ExR$$<\/jats:tex-math>E<\/mml:mi>x<\/mml:mi>G<\/mml:mi>-<\/mml:mo>E<\/mml:mi>x<\/mml:mi>R<\/mml:mi><\/mml:mrow><\/mml:math><\/jats:alternatives><\/jats:inline-formula>($$ExR$$<\/jats:tex-math>ExR<\/mml:mi><\/mml:mrow><\/mml:math><\/jats:alternatives><\/jats:inline-formula>is the abbreviation of the Excess Red index) to recognize plants in the image, and the fourth algorithm (Fun04) uses$$ExG$$<\/jats:tex-math>ExG<\/mml:mi><\/mml:mrow><\/mml:math><\/jats:alternatives><\/jats:inline-formula>and$$O{\\text{tsu}}$$<\/jats:tex-math>O<\/mml:mi>tsu<\/mml:mtext><\/mml:mrow><\/mml:math><\/jats:alternatives><\/jats:inline-formula>to separate the plants from the background.$$Otsu$$<\/jats:tex-math>Otsu<\/mml:mi><\/mml:mrow><\/mml:math><\/jats:alternatives><\/jats:inline-formula>is an algorithm used to determine a threshold to transform the image into a binary image for the vegetation and background. We measured the FVC of several surveyed quadrats using these four algorithms under three scenarios, namely overcast sky, solar forenoon, and solar noon. FVC values obtained using the Photoshop-assisted manual identification method were used as a reference to assess the accuracy of the four algorithms selected. Results indicate that under the overcast sky scenario, Fun01 was more accurate than the other algorithms and the MAPE (mean absolute percentage error), BIAS, relBIAS (relative BIAS), RMSE (root mean square error), and relRMSE (relative RMSE) are 8.68%, 1.3, 3.97, 3.13, and 12.33%, respectively. Under the scenario of the solar forenoon, Fun02 (decision tree) was more accurate than other algorithms, and the MAPE, BIAS, relBIAS, RMSE, and relRMSE are 22.70%, \u2212\u20092.86, \u2212\u20097.70, 5.00, and 41.23%. Under the solar noon scenario, Fun02 was also more accurate than the other algorithms, and the MAPE, BIAS, relBIAS, RMSE, and relRMSE are 20.60%, \u2212\u20096.39, \u2212\u200920.67, 7.30, and 24.49%, respectively.<\/jats:p><\/jats:sec>Conclusions<\/jats:title>Given that each algorithm has its own optimal application scenario, among the four algorithms selected, Fun01 (the combination of$$R\/G$$<\/jats:tex-math>R<\/mml:mi>\/<\/mml:mo>G<\/mml:mi><\/mml:mrow><\/mml:math><\/jats:alternatives><\/jats:inline-formula>,$$B\/G$$<\/jats:tex-math>B<\/mml:mi>\/<\/mml:mo>G<\/mml:mi><\/mml:mrow><\/mml:math><\/jats:alternatives><\/jats:inline-formula>, and$$ExG$$<\/jats:tex-math>ExG<\/mml:mi><\/mml:mrow><\/mml:math><\/jats:alternatives><\/jats:inline-formula>) can be recommended for measuring FVC on cloudy days. Fun02 (decision tree) is more suitable for measuring the FVC on sunny days. However, it considerably underestimates the FVC in most cases. We expect the findings of this study to serve as a useful reference for automatic vegetation cover measurements.<\/jats:p><\/jats:sec>","DOI":"10.1186\/s12859-022-04886-6","type":"journal-article","created":{"date-parts":[[2022,8,30]],"date-time":"2022-08-30T12:02:52Z","timestamp":1661860972000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Adaptiveness of RGB-image derived algorithms in the measurement of fractional vegetation coverage"],"prefix":"10.1186","volume":"23","author":[{"given":"Chuangye","family":"Song","sequence":"first","affiliation":[]},{"given":"Jiawen","family":"Sang","sequence":"additional","affiliation":[]},{"given":"Lin","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Huiming","family":"Liu","sequence":"additional","affiliation":[]},{"given":"Dongxiu","family":"Wu","sequence":"additional","affiliation":[]},{"given":"Weiying","family":"Yuan","sequence":"additional","affiliation":[]},{"given":"Chong","family":"Huang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,8,30]]},"reference":[{"key":"4886_CR1","doi-asserted-by":"publisher","first-page":"3519","DOI":"10.1080\/014311698213795","volume":"19","author":"T Purevdorj","year":"2010","unstructured":"Purevdorj T, Tateishi R, Ishiyama T, Honda Y. 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