This portal has been archived. Explore the next generation of this technology.
High Accurate Unhealthy Leaf Detection
lib:4e5d98f83e453016 (v1.0.0)
Authors: S. Mohan Sai,G. Gopichand,C. Vikas Reddy,K. Mona Teja
ArXiv: 1908.09003
Document: PDF DOI
Abstract URL: https://arxiv.org/abs/1908.09003v1
India is an agriculture-dependent country. As we all know that farming is the backbone of our country it is our responsibility to preserve the crops. However, we cannot stop the destruction of crops by natural calamities at least we have to try to protect our crops from diseases. To, detect a plant disease we need a fast automatic way. So, this paper presents a model to identify the particular disease of plant leaves at early stages so that we can prevent or take a remedy to stop spreading of the disease. This proposed model is made into five sessions. Image preprocessing includes the enhancement of the low light image done using inception modules in CNN. Low-resolution image enhancement is done using an Adversarial Neural Network. This also includes Conversion of RGB Image to YCrCb color space. Next, this paper presents a methodology for image segmentation which is an important aspect for identifying the disease symptoms. This segmentation is done using the genetic algorithm. Due to this process the segmentation of the leaf Image this helps in detection of the leaf mage automatically and classifying. Texture extraction is done using the statistical model called GLCM and finally, the classification of the diseases is done using the SVM using Different Kernels with the high accuracy.
ArXiv: 1908.09003
Document: PDF DOI
Abstract URL: https://arxiv.org/abs/1908.09003v1
India is an agriculture-dependent country. As we all know that farming is the backbone of our country it is our responsibility to preserve the crops. However, we cannot stop the destruction of crops by natural calamities at least we have to try to protect our crops from diseases. To, detect a plant disease we need a fast automatic way. So, this paper presents a model to identify the particular disease of plant leaves at early stages so that we can prevent or take a remedy to stop spreading of the disease. This proposed model is made into five sessions. Image preprocessing includes the enhancement of the low light image done using inception modules in CNN. Low-resolution image enhancement is done using an Adversarial Neural Network. This also includes Conversion of RGB Image to YCrCb color space. Next, this paper presents a methodology for image segmentation which is an important aspect for identifying the disease symptoms. This segmentation is done using the genetic algorithm. Due to this process the segmentation of the leaf Image this helps in detection of the leaf mage automatically and classifying. Texture extraction is done using the statistical model called GLCM and finally, the classification of the diseases is done using the SVM using Different Kernels with the high accuracy.
Relevant initiatives
Related knowledge about this paper
Reproduced results (crowd-benchmarking and competitions)
Artifact and reproducibility checklists
- ACM/cTuning artifact appendix and reproducibility checklist
- NeurIPS reproducibility checklist
- SIGPLAN's checklist for empirical evaluation
- Collective Knowledge (organizing research projects based on FAIR principles)