Three-level neural network for data clusterization on images of infected crop field

• Autorzy: Doudkin A. A. , Inyutin A. V. , Petrovsky A. I. , Vatkin M. E. , Sobkowiak B.
• Języki publikacji: EN

Abstrakty

EN

The objective of this research was to use neural network approach for segmentation problem of agricultural landed-fields in remote sensing data. A neural network clusterization algorithm for segmentation of the color images of crop field infected by diseases that change usual color of agricultural plants is proposed. It can be applied for cartography of fields infected by plant diseases to reduce the use of plant protection products.

Słowa kluczowe

EN

neural network; crop field; plant protection; image

• Wydawca: Sieć Badawcza Łukasiewicz - Poznański Instytut Technologiczny
• Czasopismo: Journal of Research and Applications in Agricultural Engineering
• Rocznik: 2007
• Tom: Vol. 52, nr 1
• Strony: 5--7
• Opis fizyczny: Bibliogr. 10 poz., rys.

Twórcy

autor

Doudkin A. A.

autor

Inyutin A. V.

autor

Petrovsky A. I.

autor

Vatkin M. E.

autor

Sobkowiak B.

• United Institute of Informatics Problem, National Academy of Science of Belarus, Surganov St., 6, 220012, Minsk, Belarus; Industrial Institute of Agricultural Engineering, Starolecka St., 31, 60963, Poznan, Poland,

Bibliografia

• [1] Boleslaw Sobkowiak, Tadeusz Pawlowsky. Analiza obrazów kolorowych zainfekowanych pól uprawnych. Część 1. Przeprowadzenie testu I ocena wyników badań // Wybrane zagadnienia ekologiczne we współczesnym rolnictwie. –PIMR, Poznań, 2005.- P. 113-117.
• [2] Margarita Torre, Petia Radeva. "Agricultural-Field Extraction on Aerial Images by Region Competition Algorithm," icpr, vol. 01, no. 1, p. 1313, 15th 2000.
• [3] G. A. Carpenter, S. Gopal, and C. E. Woodcock, "A neural network method for efficient vegetation mapping," Remote Sensing Environment, vol. 70, no. 3, pp. 326--338, Dec. 1999.
• [4] A.V. Inyutin. The algorithm of image segmentation by grayscale pseudo-skeleton. Proceedings of the III International Conferences on Neural Networks and Artificial Intelligence (ICNNAI 2003), November 12-14, Minsk. Belarus, p.263-265.
• [5] Natalia Kussul, Michael Kussul, Anatoly Sachenko, George Markowsky, Sergiy Ganzha. Remote Sensing of Vegetation Using Modular Neural Networks. Proceedings of the III International Conferences on Neural Networks and Artificial Intelligence (ICNNAI 2003), November 12-14, Minsk. Belarus, p.232-234.
• [6] Yang, C.C.; Prasher, S.O. ; Landry, J.A. ; Perrett, J., Ramaswamy, H.S. 2000. Recognition of Weeds with Image Processing and their use with Fuzzy Logic for Precision Farming. Canadian Agricultural Engineering. 42(4) : 195-200
• [7] Tseng, Y.H., P.H. Hsu, and Y.H. Chen, 1998. Automatic Detecting Rice Fields by Using Multispectral Satellite Images, Land-parcel Data and Domain Knowledge, Proceedings of the 19th Asian Conference on Remote Sensing, Manila, Philippines, 16-20 November, pp. R-1-1~R-1-7.
• [8] Apan, Armando and Kelly, Rob and Jensen, Troy and Butler, David and Strong, Wayne and Basnet, Badri (2002) Spectral Discrimination And Separability Analysis Of Agricultural Crops And Soil Attributes Using Aster Imagery. In 11th Australasian Remote Sensing and Photogrammetry Conference, 2-6 September, pages 396-411, Brisbane, Queensland.
• [9] Analysis of colour images of infected crop field. Part 2. Two-stage algorithm of segmentation and improvement of color images of infected crop field / Alexander A. Doudkin et.// Wybrane zagadnienia ekologiczne we wspolczesnym rolnictwie – PIRM, Poznan, 2005.- P. 118-122.
• [10] Ваткин М.Е., Ганченко В.В., Дудкин А.А., Петровский А.И., Собковяк Б. Выделение информативных признаков болезни картофеля по цветовым характеристикам листьев