, Россия
, Россия
ГРНТИ 50.07 Теоретические основы вычислительной техники
ББК 3297 Вычислительная техника
Automated visual assessment of the state of the earth and plants, wilting and pests of leaves, plant growth indicators, using technical vision, can be used as a basis in smart (precision) agriculture (SA). This article discusses a brief review of the literature on the use of computer (technical) vision (CV) for analyzing the condition of agricultural fields and plants growing on them. The introduction of vision systems into real agricultural production practice is associated with the development of complex mathematical approaches that must be resistant to a variety of technical and weather changes. It is necessary to overcome image changes caused by atmospheric conditions and daily and seasonal variations in sunlight. An approach is proposed, which is based on an RGB image obtained using a typical digital camera. The results are given on the use of CV systems in solving individual tasks of agricultural production.
technical vision, mathematical methods, images, agriculture, image classification, unmanned aerial vehicle
1. Silva T.S., Costa M.P., Melack J.M., Novo E.M. Remotesensing of aquatic vegetation: theory and applications //Environmental Monitoring and Assessment, 2008, 140: 131-145.
2. Pantazi X.E., Moshou D., Alexandridis T., Wheton R.L.Wheat yield prediction using machine learning and advancedsensing techniques // Computers and Electronics andAgriculture, 2016, V.6, P.57–65.
3. Xue Z., Li J., Cheng L., Du P. Spectral–spatial classificationof hyperspectral data via morphological component analysisbased image separation // IEEE Transaction, 2015, V.53, N.1,P.70–84.
4. Kataev M. Yu. Opportunities for space monitoring foragriculture of the Tomsk region / M. Yu. Kataev, A. A. Skugarev,I. B. Sorokin // TUSUR reports. - 2017. - T. 20, No. 3. - S. 186–190.
5. Ide R., Oguma H. Use of digital cameras for phenologicalobservations / R. Ide, H. Oguma // Ecological Informatics. –2010. –N.5. – P.339-347.
6. Woebbecke D.M., Meyer G.E., Vonbargen K., MortensenD.A. Color Indexes for Weed Identifiation under Various Soil,Residue, and Lighting Conditions / D.M. Woebbecke, G.E.Meyer, K. Vonbargen, D.A. Mortensen // Trans. ASABE. –1995. – V.38. – P.259–269.