Evaluation of image processing technique in identifying rice blast disease in field conditions based on KNN algorithm improvement by K-means
ABSTARCT :
Nowadays, rice farming is affected by various diseases that are economically significant and worthy of attention. One of these diseases is blast. Rice blast is one of the most important limiting factors in rice yield. The purpose of this study is the timely and rapid diagnosis of rice blast based on the image processing technique in field conditions. To do so, color images were prepared using image processing technique and improved KNN algorithm by K-means was used to classify the images in Lab color space to detect disease spots on rice leaves. Squared classification was based on Euclidean distance, and the Otsu method was used to perform an automatic thresh-old histogram of images based on shape or to reduce the gray level in binary images. Finally, to determine the efficiency of the designed algorithm, sensitivity, specificity, and overall accuracy were examined. The classification results showed that the sensitivity and specificity of the designed algorithm were 92% and 91.7%, respectively, in the determination of the number of disease spots, and 96% and 95.65% in deter-mining the quality of disease spots. The overall accuracy of the designed algorithm was 94%. Generally, the results obtained showed that the above method has a great potential for timely diagnosis of rice blast.
EXISTING SYSTEM :
Hyper spectral technology can sometimes be considered as a part of spectroscopy. The electromagnetic spectrum ranges of hyperspectral sensors mainly concentrate on VIS–NIR (400–1000 nm) and sometimes contain a short wave infrared range (SWIR, 1000–2500 nm). These sensors could acquire spectral information from hundreds of narrow spectral bands . These narrow wavebands have high sensitivity to the subtle plant changes caused by diseases and make it possible to distinguish different disease types and perform early asymptomatic detection.
DISADVANTAGE :
? Rapid development of computer processing technologies and creation of related software makes it possible for us to benefit from the advantages of artificial intelligence.
? One example of these technologies is the application of artificial neural networks and other algorithms that to some extent copy human brain functions, to solve problems in systems and modeling processes
? The results of this study showed that the dynamic range of gray surfaces should be increased to de-termine the damaged parts of the rice leaf.
PROPOSED SYSTEM :
The framework based on image spectral calibration and performed normalization using white and dark reference images, covering the image to grayscale, image binarization, and the application of threshold segmentation. facing the frequent global climate change and disasters, increasingly more countries and organizations have proposed the special hyperspectral RS missions and speed up these trends.
ADVANTAGE :
In modern agriculture, quick methods reviews, automated, cheap and accurate methods for diagnosing plant diseases are important. Timely and accurate diagnosis of disease in farms is one of the most important factors in controlling plant disease the use of a method that can manage the whole farm to be online is very import-ant. Therefore, the purpose of this research is to online management of rice fields using a quadcopter (helicopter) and image processing technique to identify rice blast disease in field conditions.
One of these rice diseases is blast. Blast is one of the most important limiting factors in plant performance. The maximum sensitivity to the blast disease is observed at the sprouting stage, that is when the rice plant starts to appear above the surface of the ground
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