Plant Leaf Disease Prediction Using Deep Dense Net Slice Fragmentation and Segmentation Feature Selection Using Convolution Neural Network


  • S. Jana Department of ECE, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Chennai, India
  • S. D. Thilagavathy Department of Information Technology, Adhiyamaan College of Engineering Hosur, Tamil Nadu 635109, India
  • S. T. Shenbagavalli Department of Computer Science and Engineering, PSNA College of Engineering and Technology Kothandaraman Nagar, Tamil Nadu 624622, India
  • G. Srividhya Assistant Professor, Department of Electronics and Communication Engineering, Panimalar engineering college, Poonamallee, Chennai, Tamil Nadu 600123
  • V. S. Gowtham Prasad Associate Professor, Department of ECE, ,Sree Vidyanikethan Engineering College (Autonomous), Tirupati,Chittoor (District),Andhra Pradesh-517102
  • R. Hemavathy Department of electronics and Communication engineering, P.S.R college of engineering, Sivakasi-626140.Tamil Nadu,India


Plant leaf disease, feature selection, segmentation, classification, Dn CNN, early identification, deep learning


Plant disease affects the agriculture production seasonally on variety of ways. Identifying and early detection of disease is an important factor for production management to improve the economic growth. Most commonly the disease symptoms are identified by observing the disease in the plant leaf region. Existing machine learning models use images with improper and high dimensional features that lead to inaccurate classification of plant leaf disease.  To tackle this issues, we introduces a deep dense net slice fragmentation and segmentation feature selection and classification through optimized convolution neural network. Initially the wavelet Filters features are applied to enhance the image through structure normalization model. The slice fragment segmentation is applied to segment the disease covered region by identifying the realistic variation based on spectral histogram feature difference. Then cascaded edges and features are extracted and trained using deep Densenet Convolution Neural Network (DnCNN) to identify the plant disease effectively. The proposed system achieves best result compared to the other existing approaches in terms of precision rate, recall, f-measure and also superior due to the fact that the diseases are identified at an earlier stage.


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Proposed: DNCNN architecture diagram




How to Cite

S. . Jana, S. D. . Thilagavathy, S. T. . Shenbagavalli, G. . Srividhya, V. S. G. . Prasad, and R. . Hemavathy, “Plant Leaf Disease Prediction Using Deep Dense Net Slice Fragmentation and Segmentation Feature Selection Using Convolution Neural Network”, Int J Intell Syst Appl Eng, vol. 11, no. 6s, pp. 76–85, May 2023.



Research Article