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	Provedor de dados CIGR Journal (2) Autor Alimardani, Reza (1) Azizpanah, Amir (1) Kheiralipour, Kamran (1) Mohammadi, Vahid (1) Rajabipour, Ali (1) yakkundimath, rajesh siddaramayya (1) Mais... Palavra-chave Artificial Neural Network (2) Agriculture engineering Fungal disease (1) Computer science (1) Drift (1) Image processing (1) K-Nearest Neighbor (1) Local Binary Patterns (1) Sprayer (1) Vegetable crops (1) Mais... Tipo do documento Info:eu-repo/semantics/article (2) Ano 2015 (1) 2014 (1) País China (2) Idioma Inglês (2)		Ordenar por:  Relevância Autor Título Ano Registros recuperados: 2 Primeira ... 1 ... Última Neuro-kNN classification system for detecting fungal disease found on vegetable crops using local binary patterns CIGR Journal yakkundimath, rajesh siddaramayya. This paper describes the behavior of classifiers for identification and classification of fungal disease symptoms found on vegetable crops. Symptoms of fungal disease, namely, anthracnose, powdery mildew, rust, downey mildew, early blight, and late blight found on specific type of vegetable crop are considered for recognition and classification. The way the disease analysis is done considering both sides (front and back portions) of the leaves has been addressed. The analysis of the fungal disease present on the leaves of vegetable crops is detected in the early stage before it damages the whole leaf and subsequently the plant. The Local Binary Patterns(LBP) extracted from disease affected leaves are used as input to the classifiers. An integrated... Tipo: Info:eu-repo/semantics/article Palavras-chave: Computer science; Agriculture engineering Fungal disease; Vegetable crops; Local Binary Patterns; Artificial Neural Network; K-Nearest Neighbor. Ano: 2014 URL: http://www.cigrjournal.org/index.php/Ejounral/article/view/2965 Precision spray modeling using image processing and artificial neural network CIGR Journal Azizpanah, Amir; Rajabipour, Ali; Alimardani, Reza; Kheiralipour, Kamran; Mohammadi, Vahid. This study employed artificial neural network method for predicting the sprayer drift under different conditions using image processing technique. A wind tunnel was used for providing air flow in different velocities. Water Sensitive Paper (WSP) was used to absorb spray droplets and an automatic algorithm processed the images of WSPs for measuring droplet properties including volume median diameter (Dv0.5) and Surface Coverage Percent (SCP). Four Levenberg-Marqurdt models were developed to correlate the sprayer drift (output parameter) to the input parameters (height, pressure, wind velocity and Dv0.5). The ANN models were capable of predicting the output variables in different conditions of spraying with a high performance. Both models predicted the... Tipo: Info:eu-repo/semantics/article Palavras-chave: Sprayer; Drift; Image processing; Artificial Neural Network. Ano: 2015 URL: http://www.cigrjournal.org/index.php/Ejounral/article/view/3261 Registros recuperados: 2 Primeira ... 1 ... Última

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