IMAGE RECOGNITION OF AGRICULTURAL CROPS, PLANTS AND FORESTS
Keywords:
Image recognition, recognition of images of fruits, deep learning, computer vision, landscape recognition, hyperspectral analysis, remote monitoring
Abstract
The paper provides an overview of some studies on the recognition of images of crops,
plants and forests. These image recognition systems use various methods of pre-processing, computer
vision, and deep learning. Recently recognition systems based on mobile devices are increasing,
which increases their availability and wide distribution. The articles on recognition,
classification of fruits and fruits in orchards, the creation of a data bank of these agricultural
products (apples, pears, kiwi) to assess ripening and yield are considered. The works devoted to
the automation of harvesting grain crops are described on the example of the work of a combine
harvester using machine vision. Crop production plays an important role in providing feed for
animal husbandry; articles on the recognition of agricultural plants based on leaf images are
analyzed. Also, by the condition of the leaves of potato bushes, you can determine their disease,
assess the condition of the soil. The work on the development of mobile systems for monitoring and
recognition of the process of growing mushrooms based on the "green house" technology for
farms is presented. Using remote diagnostics, you can analyze and monitor the state of the surface
of land and seas. For remote environmental monitoring of the landscape of the earth's surface,
work is described on the recognition, classification of forests, water resources using hyperspectral
analysis of satellite images.
References
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based on convolutional neural networks, Trans. Chin. Soc. Agric. Eng., 2018, No. 34 (2),
pp. 205-211.
7. Liu D., Shen J., Yang H. et al. Recognition and localization of actinidia arguta based on image
recognition, J Image Video Proc., 2019, No. 21 (2019). Available at: https://doi.org/10.1186/
s13640-019-0419-6.
8. Shinners K.J., Boettcher G.C., Holfman D.S., et al. Single-pass harvest of corn grain and stover:
performance of three harvester con – figurations[J].Transactions of the ASABE, 2009, Vol.
52 (1), pp. 51-60.
9. Song Xiangwen, Cao Shukun, Wang Chong, Xu Xiangqian, Han Tao and Guo Hejia. Automatic
9 Row Corn Harvester Based on Labview Image Recognition IOP Conf. Series: Journal of
Physics: Conf. Series 1098 (2018) 012016. DOI: 10.1088/1742-6596/1098/1/012016.
10. Sun Y., Liu Y., Wan, G., Zhang H. Deep Learning for Plant Identification in Natural Environment,
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11. Shaikh N.A., Mallah G.A., Karaş,İ.R., Akay A.E. Using mobile image recognition system for
nonwood species identification in the field, Journal of Innovative Science and Engineering,
2018. No. 2 (2), pp. 40-50. DOI: 10.38088/jise.498910.
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plant recognition through image analysis approach, In Proceedings of the 2017 International
Conference on Inventive Communication and Computational Technologies (ICICCT), Coimbatore,
India, 10–11 March 2017, pp. 145-149.
15. Munisami T., Ramsurn M., Kishnah S., Pudaruth S. Plant Leaf Recognition Using Shape Features
and Colour Histogram with K-nearest Neighbour Classifiers, Procedia Comput. Sci.,
2015, Vol. 58, pp. 740-747.
16. Sapate N.M., Deshmukh R.R. Spectral and Numerical Analysis of Hyper spectral Data using
Vegetation Indices, International Journal of Engineering and Advanced Technology, August
2019, Vol. 8, Issue-6. DOI: 10.35940/ijeat.F8578.088619.
17. Gaikwad C.M., Kakarwal S.N., Ghule A.N., Deshmukh R.R., Kurmude D.V. Use of spectral
reflectance for sensitive waveband determination for soil organic matter, International Journal
of Scientific and Technology Research, 2019, No. 8 (10), pp. 1071-1075.
18. Wu W.-H., Zhou L., Chen J., Qiu Z., He Y., GainT. A measurement system of thousand kernel
weight based on the android platform, Agronomy, 2018, No. 8, pp. 178.
19. Lu C.-P., Liaw J.-J., Wu T.-C., Hung T.-F. Development of a Mushroom Growth Measurement
System Applying Deep Learning for Image Recognition, Agronomy, 2019, No. 9, 32, 21 p.
DOI: 10.3390/agronomy9010032.
20. Ulbricht K.A, Heckendorff W.D. Satellite images for recognition of landscape and landuse
changes, ISPRS Journal of Photogrammetry and Remote Sensing, 1998, Vol. 53, Issue 4,
pp. 235-243, https://doi.org/10.1016/S0924-2716(98)00006-9
21. Teotia H.S., Ulbricht K.A., Civco D.L. The integration of remote sensing and GIS technologies
for land development and irrigation potential in the state of Ceara, Brazil. In: Proceedings of
the XVII ISPRS Congress, Washington D.C. Int. Arch. Photogramm. Remote Sensing. 1992.
29 (B7), pp. 466-472.
22. Bazi Y., Melgani F., Bruzzone L., Vernazza G. A genetic expectation-maximization method for
unsupervised change detection inmultitemporal SAR imagery, International Journal of Remote
Sensing, 2009, Vol. 30, No. 24, pp. 6591-6610.
23. Yang Ming-Der, Yeh-Fen Yang, Tung-Ching Su, and Kai-Siang Huang. Hindawi Publishing
Corporation, The Scientific World Journal, Vol. 2014, Article ID 264512, 12 p. Available at:
http://dx.doi.org/10.1155/2014/264512.
24. Huang B., Zhao B., Song Y. Urban land-use mapping using a deep convolutional neural network
with high spatial resolution multispectral remote sensing imagery, Remote Sens. Environ.,
2018, Vol. 214, pp. 73-86.
25. Zhang X., Han L., Han L., Zhu L. How Well Do Deep Learning-Based Methods for Land Cover
Classification and Object Detection Perform on High Resolution Remote Sensing Imagery?
Remote Sens., 2020, No. 12, 417, 29 p. Available at: https://doi.org/10.3390/rs12030417.
26. Popescu D., Ichim L. Image Recognition in UAV Application Based on Texture Analysis. In:
Battiato S., Blanc-Talon J., Gallo G., Philips W., Popescu D., Scheunders P. (eds) Advanced
Concepts for Intelligent Vision Systems. ACIVS 2015. Lecture Notes in Computer Science,
Vol. 9386. Springer, 2015. DOI: 10.1007/978-3-319-25903-1_60.
27. Abbasov I.B., Deshmukh R.R. Methods of remote diagnostic of water surface, In the Proceeding:
“Computer and information technologies in science, engineering and management "ComTech-
2019": Materials of the Russian scientific and technical conference with international participation,
2019, pp. 477-481. Available at: https://elibrary.ru/item.asp?id=41333265.
28. Kozoderov V.V., Kondranin T.V., Dmitriev E.V. Comparative analysis of recognition algorithms
for forest cover objects on hyperspectral aerospace images, Earth exploration from
space, 2016, No. 6, pp. 45-55. DOI: 10.7868/S020596141606004X.
29. Kozoderov V.V., Dmitriev E.V. Patterns of recognition and assessment of the state of forest
vegetation from hyperspectral data of remote sensing, Research of the Earth from Space, 2017,
No. 6, pp. 75-88. DOI: 10.7868/S0205961417060070.
30. Kozoderov, V.V., Kondranin T.V., Dmitriev E.V. Hyperspectral remote sensing imagery processing
focused on forest applications, International Review of Aerospace Engineering, October
2017, Vol. 10, Issue 5, pp. 267-276.
31. Kozoderov V.V., Egorov V.D. Recognition of types of forest vegetation by hyperspectral aircraft
and multi-channel satellite data of high spatial resolution. Comparison of results and assessment
of their accuracy, Research of the Earth from Space, 2019, No. 6, pp. 89-102
https://doi.org/10.31857/S0205-96142019689-102
32. Li X., Chen W.Y., Sanesi G., Lafortezza R. Remote Sensing in Urban Forestry: Recent Applications
and Future Directions, Remote Sens., 2019, No. 11 (10), 1144; 20 p. Available at:
https://doi.org/10.3390/rs11101144.
33. Roman L.A., Pearsall H., Eisenman T.S., Conway T.M., Fahey R.T., Landry S., Vogt J., van
Doorn N.S., Grove J.M., Locke D.H., et al. Human and biophysical legacies shape contemporary
urban forests: A literature synthesis, Urban For. Urban Green, 2018, Vol. 31, pp. 157-168.
34. Govender M., Chetty K., Bulcock H. A review of hyperspectral remote sensing and its application
invegetation and water resource studies, Water SA, 2017, Vol. 33, pp. 145-152.
35. Khan M.J., Khan H.S., Yousaf A., Khurshid K., Abbas A. Modern trends in hyperspectral image
analysis: A review, IEEE Access, 2018, No. 6, pp. 14118014129.
36. Stuart M.B., McGonigle A.J.S., Willmott J.R. Hyperspectral Imaging in Environmental Monitoring:
A Review of Recent Developments and Technological Advances in Compact Field Deployable
Systems, Sensors, 2019, No. 19, 3071, 17 p. DOI: 10.3390/s19143071.
in Agriculture: A Review, Sensors, 2020, Vol. 20, 1520. 33 p. DOI: 10.3390/s20051520.
2. Bechar A., Vigneault C. Agricultural robots for field operations: concepts and components,
Biosyst. Eng., 2016, Vol. 149, pp. 94-111. DOI:10.1016/j.biosystemseng.2016.06.014.
3. Kelman E.E., Linker R. Vision-based localisation of mature apples in tree images using convexity,
Biosyst. Eng., 2014, Vol. 118, pp. 174-185. DOI: 10.1016/j.biosystemseng.2013.11.007.
4. Bresilla K., Perulli G.D., Boini A., Morandi B., Corelli Grappadelli L., Manfrini L. Single-
Shot Convolution Neural Networks for Real-Time Fruit Detection Within the Tree//Front,
Plant Sci., 2019, No. 10, pp. 611. Available at: https://doi.org/10.3389/fpls.2019.00611.
5. Yongjie C. Recognition and feature extraction of kiwifruit in natural environment based on
machine vision, Trans. Chin. Soc. Agric., Mach. 2013, Vol. 44 (5), pp. 247-252.
6. Fu L., Feng Y., Elkamil T. et al. Image recognition method of multi-cluster kiwifruit in field
based on convolutional neural networks, Trans. Chin. Soc. Agric. Eng., 2018, No. 34 (2),
pp. 205-211.
7. Liu D., Shen J., Yang H. et al. Recognition and localization of actinidia arguta based on image
recognition, J Image Video Proc., 2019, No. 21 (2019). Available at: https://doi.org/10.1186/
s13640-019-0419-6.
8. Shinners K.J., Boettcher G.C., Holfman D.S., et al. Single-pass harvest of corn grain and stover:
performance of three harvester con – figurations[J].Transactions of the ASABE, 2009, Vol.
52 (1), pp. 51-60.
9. Song Xiangwen, Cao Shukun, Wang Chong, Xu Xiangqian, Han Tao and Guo Hejia. Automatic
9 Row Corn Harvester Based on Labview Image Recognition IOP Conf. Series: Journal of
Physics: Conf. Series 1098 (2018) 012016. DOI: 10.1088/1742-6596/1098/1/012016.
10. Sun Y., Liu Y., Wan, G., Zhang H. Deep Learning for Plant Identification in Natural Environment,
Computational Intelligence and Neuroscience, Vol. 2017, Article ID 7361042, 6 p.
Available at: https://doi.org/10.1155/2017/7361042.
11. Shaikh N.A., Mallah G.A., Karaş,İ.R., Akay A.E. Using mobile image recognition system for
nonwood species identification in the field, Journal of Innovative Science and Engineering,
2018. No. 2 (2), pp. 40-50. DOI: 10.38088/jise.498910.
12. Le T., Tran D., Hoang V. Fully automatic leaf-based plant identification, application for Vietnamese
medicinal plant search. SoICT ’14, December 04–05, 2014, Hanoi, Vietnam. Available
at: https://doi.org/10.1145/2676585.2676592.
13. Azlah M.A.F., Chua L.S., Rahmad F.R., Abdullah F.I., Wan Alwi S.R. Review on Techniques
for Plant Leaf Classification and Recognition, Computers, 2019, No. 8, 77, 22 p.
14. Sabu A., Sreekumar K. Literature review of image features and classifiers used in leaf based
plant recognition through image analysis approach, In Proceedings of the 2017 International
Conference on Inventive Communication and Computational Technologies (ICICCT), Coimbatore,
India, 10–11 March 2017, pp. 145-149.
15. Munisami T., Ramsurn M., Kishnah S., Pudaruth S. Plant Leaf Recognition Using Shape Features
and Colour Histogram with K-nearest Neighbour Classifiers, Procedia Comput. Sci.,
2015, Vol. 58, pp. 740-747.
16. Sapate N.M., Deshmukh R.R. Spectral and Numerical Analysis of Hyper spectral Data using
Vegetation Indices, International Journal of Engineering and Advanced Technology, August
2019, Vol. 8, Issue-6. DOI: 10.35940/ijeat.F8578.088619.
17. Gaikwad C.M., Kakarwal S.N., Ghule A.N., Deshmukh R.R., Kurmude D.V. Use of spectral
reflectance for sensitive waveband determination for soil organic matter, International Journal
of Scientific and Technology Research, 2019, No. 8 (10), pp. 1071-1075.
18. Wu W.-H., Zhou L., Chen J., Qiu Z., He Y., GainT. A measurement system of thousand kernel
weight based on the android platform, Agronomy, 2018, No. 8, pp. 178.
19. Lu C.-P., Liaw J.-J., Wu T.-C., Hung T.-F. Development of a Mushroom Growth Measurement
System Applying Deep Learning for Image Recognition, Agronomy, 2019, No. 9, 32, 21 p.
DOI: 10.3390/agronomy9010032.
20. Ulbricht K.A, Heckendorff W.D. Satellite images for recognition of landscape and landuse
changes, ISPRS Journal of Photogrammetry and Remote Sensing, 1998, Vol. 53, Issue 4,
pp. 235-243, https://doi.org/10.1016/S0924-2716(98)00006-9
21. Teotia H.S., Ulbricht K.A., Civco D.L. The integration of remote sensing and GIS technologies
for land development and irrigation potential in the state of Ceara, Brazil. In: Proceedings of
the XVII ISPRS Congress, Washington D.C. Int. Arch. Photogramm. Remote Sensing. 1992.
29 (B7), pp. 466-472.
22. Bazi Y., Melgani F., Bruzzone L., Vernazza G. A genetic expectation-maximization method for
unsupervised change detection inmultitemporal SAR imagery, International Journal of Remote
Sensing, 2009, Vol. 30, No. 24, pp. 6591-6610.
23. Yang Ming-Der, Yeh-Fen Yang, Tung-Ching Su, and Kai-Siang Huang. Hindawi Publishing
Corporation, The Scientific World Journal, Vol. 2014, Article ID 264512, 12 p. Available at:
http://dx.doi.org/10.1155/2014/264512.
24. Huang B., Zhao B., Song Y. Urban land-use mapping using a deep convolutional neural network
with high spatial resolution multispectral remote sensing imagery, Remote Sens. Environ.,
2018, Vol. 214, pp. 73-86.
25. Zhang X., Han L., Han L., Zhu L. How Well Do Deep Learning-Based Methods for Land Cover
Classification and Object Detection Perform on High Resolution Remote Sensing Imagery?
Remote Sens., 2020, No. 12, 417, 29 p. Available at: https://doi.org/10.3390/rs12030417.
26. Popescu D., Ichim L. Image Recognition in UAV Application Based on Texture Analysis. In:
Battiato S., Blanc-Talon J., Gallo G., Philips W., Popescu D., Scheunders P. (eds) Advanced
Concepts for Intelligent Vision Systems. ACIVS 2015. Lecture Notes in Computer Science,
Vol. 9386. Springer, 2015. DOI: 10.1007/978-3-319-25903-1_60.
27. Abbasov I.B., Deshmukh R.R. Methods of remote diagnostic of water surface, In the Proceeding:
“Computer and information technologies in science, engineering and management "ComTech-
2019": Materials of the Russian scientific and technical conference with international participation,
2019, pp. 477-481. Available at: https://elibrary.ru/item.asp?id=41333265.
28. Kozoderov V.V., Kondranin T.V., Dmitriev E.V. Comparative analysis of recognition algorithms
for forest cover objects on hyperspectral aerospace images, Earth exploration from
space, 2016, No. 6, pp. 45-55. DOI: 10.7868/S020596141606004X.
29. Kozoderov V.V., Dmitriev E.V. Patterns of recognition and assessment of the state of forest
vegetation from hyperspectral data of remote sensing, Research of the Earth from Space, 2017,
No. 6, pp. 75-88. DOI: 10.7868/S0205961417060070.
30. Kozoderov, V.V., Kondranin T.V., Dmitriev E.V. Hyperspectral remote sensing imagery processing
focused on forest applications, International Review of Aerospace Engineering, October
2017, Vol. 10, Issue 5, pp. 267-276.
31. Kozoderov V.V., Egorov V.D. Recognition of types of forest vegetation by hyperspectral aircraft
and multi-channel satellite data of high spatial resolution. Comparison of results and assessment
of their accuracy, Research of the Earth from Space, 2019, No. 6, pp. 89-102
https://doi.org/10.31857/S0205-96142019689-102
32. Li X., Chen W.Y., Sanesi G., Lafortezza R. Remote Sensing in Urban Forestry: Recent Applications
and Future Directions, Remote Sens., 2019, No. 11 (10), 1144; 20 p. Available at:
https://doi.org/10.3390/rs11101144.
33. Roman L.A., Pearsall H., Eisenman T.S., Conway T.M., Fahey R.T., Landry S., Vogt J., van
Doorn N.S., Grove J.M., Locke D.H., et al. Human and biophysical legacies shape contemporary
urban forests: A literature synthesis, Urban For. Urban Green, 2018, Vol. 31, pp. 157-168.
34. Govender M., Chetty K., Bulcock H. A review of hyperspectral remote sensing and its application
invegetation and water resource studies, Water SA, 2017, Vol. 33, pp. 145-152.
35. Khan M.J., Khan H.S., Yousaf A., Khurshid K., Abbas A. Modern trends in hyperspectral image
analysis: A review, IEEE Access, 2018, No. 6, pp. 14118014129.
36. Stuart M.B., McGonigle A.J.S., Willmott J.R. Hyperspectral Imaging in Environmental Monitoring:
A Review of Recent Developments and Technological Advances in Compact Field Deployable
Systems, Sensors, 2019, No. 19, 3071, 17 p. DOI: 10.3390/s19143071.
Published
2020-10-11
Issue
Section
SECTION IV. IMAGE ANALYSIS AND RECOGNITION
