Detection of plant leaf diseases using image segmentation and soft computing techniques

被引:346
作者
Singh V. [1 ]
Misra A.K. [2 ]
机构
[1] Computer Science Department, IMS Engineering College, Ghaziabad, UP
[2] Computer Science & Engg. Department, MNNIT Allahabad, UP
关键词
Classification; Genetic algorithm; Image processing; Plant disease detection;
D O I
10.1016/j.inpa.2016.10.005
中图分类号
学科分类号
摘要
Agricultural productivity is something on which economy highly depends. This is the one of the reasons that disease detection in plants plays an important role in agriculture field, as having disease in plants are quite natural. If proper care is not taken in this area then it causes serious effects on plants and due to which respective product quality, quantity or productivity is affected. For instance a disease named little leaf disease is a hazardous disease found in pine trees in United States. Detection of plant disease through some automatic technique is beneficial as it reduces a large work of monitoring in big farms of crops, and at very early stage itself it detects the symptoms of diseases i.e. when they appear on plant leaves. This paper presents an algorithm for image segmentation technique which is used for automatic detection and classification of plant leaf diseases. It also covers survey on different diseases classification techniques that can be used for plant leaf disease detection. Image segmentation, which is an important aspect for disease detection in plant leaf disease, is done by using genetic algorithm. © 2017 China Agricultural University
引用
收藏
页码:41 / 49
页数:8
相关论文
共 25 条
[11]  
Beucher S., Meyer F., The morphological approach to segmentation: the watershed transforms, Mathematical morphology image processing, 12, pp. 433-481, (1993)
[12]  
Bhanu B., Lee S., Ming J., Adaptive image segmentation using a genetic algorithm, IEEE Trans Syst Man Cybern, 25, pp. 1543-1567, (1995)
[13]  
Bhanu B., Peng J., Adaptive integrated image segmentation and object recognition, IEEE Trans Syst Man Cybern Part C, 30, pp. 427-441, (2000)
[14]  
Woods K., Genetic algorithms: colour image segmentation literature review, (2007)
[15]  
Vijayaraghavan V., Garg A., Wong C.H., Tail K., Bhalerao Y., Predicting the mechanical characteristics of hydrogen functionalized graphene sheets using artificial neural network approach, J Nanostruct Chem, 3, (2013)
[16]  
Garg A., Garg A., Tai K., A multi-gene genetic programming model for estimating stress-dependent soil water retention curves, Comput Geosci, pp. 1-12, (2014)
[17]  
Garg A., Garg A., Tai K., Sreedeep S., An integrated SRM-multi-gene genetic programming approach for prediction of factor of safety of 3-D soil nailed slopes, Eng Appl Artif Intell, 30, pp. 30-40, (2014)
[18]  
Vijayaraghavan V., Garg A., Wong C.H., Tai K., Estimation of mechanical properties of nanomaterials using artificial intelligence methods, Appl Phys A, pp. 1-9, (2013)
[19]  
Garg A., Vijayaraghavan V., Wonga C.H., Tai K., Measurement of properties of graphene sheets subjected to drilling operation using computer simulation, Measurement, (2014)
[20]  
Bernardes, Alexandre A., Et al., Identification of foliar diseases in cotton crop, Topics in medical image processing and computational vision, Lecture Notes in Computational Vision and Biomechanics, pp. 67-85, (2013)