Tea Leaf Disease Prediction Using Texture-Based Image Processing

2020 ◽  
pp. 17-25
Author(s):  
Alok Ranjan Srivastava ◽  
M. Venkatesan
Keyword(s):  
Image Processing ◽  
Disease Prediction ◽  
Leaf Disease ◽  
Tea Leaf

Identification of Cucumber Leaf Disease using Image Processing Techniques

2018 ◽  
Vol 6 (6) ◽  
pp. 1493-1499
Author(s):  
Shrutika.C.Rampure . ◽  
Dr. Vindhya .P. Malagi ◽  
Dr. Ramesh Babu D.R
Keyword(s):  
Image Processing ◽  
Image Processing Techniques ◽  
Leaf Disease ◽  
Processing Techniques

Wheat stem rust leaf disease detection using image processing

2021 ◽  
Author(s):  
Basim Khalid. Mohammed Ali Al-windi ◽  
Amel H. Abbas ◽  
Mohammed Shakir Mahmood
Keyword(s):  
Image Processing ◽  
Stem Rust ◽  
Disease Detection ◽  
Wheat Stem Rust ◽  
Leaf Disease

Detection of plant leaf disease using digital image processing

2020 ◽  
Author(s):  
Ramesh Kumar Mojjada ◽  
K. Kiran Kumar ◽  
Arvind Yadav ◽  
B.V.V. Satya Vara Prasad
Keyword(s):  
Image Processing ◽  
Digital Image Processing ◽  
Digital Image ◽  
Plant Leaf ◽  
Leaf Disease

Detection of Palm Oil Leaf Disease with Image Processing and Neural Network Classification on Mobile Device

2013 ◽  
pp. 528-532 ◽  
Author(s):  
Alham F. Aji ◽  
Qorib Munajat ◽  
Ardhi P. Pratama ◽  
Hafizh Kalamullah ◽  
Aprinaldi ◽  
...  
Keyword(s):  
Neural Network ◽  
Image Processing ◽  
Palm Oil ◽  
Mobile Device ◽  
Neural Network Classification ◽  
Leaf Disease

Detection of Plant Leaf Diseases by Applying Image Processing Schemes

2019 ◽  
Vol 16 (9) ◽  
pp. 3728-3734
Author(s):  
Navneet Kaur ◽  
V. Devendran ◽  
Sahil Verma
Keyword(s):  
Image Processing ◽  
Yield Loss ◽  
Early Stage ◽  
Crop Protection ◽  
Recognition System ◽  
Assessment System ◽  
Plant Leaf ◽  
Detection Techniques ◽  
Leaf Disease ◽  
Key Factor

Timely diagnosis of the disease is the key factor in agricultural productivity. If timely detection of the disease is not taken into account, it may lead to crop yield loss. Hence, agriculturists and agronomists face troubles to detect diseases successfully at an early stage or later stage. To support these personnels to diagnose disease syndromes in infected plants, deep learning plays an important role. The machine based recognition system based on image processing not only saves time but also is more robust and efficient in comparison to manual assessment system. It helps the growers to take timely steps involved in the judicious treatment of the concerned leaf diseases for crop protection. Maximizing the production or minimizing the production loss is the primary goal of automatic plant leaf disease recognition system. Following review presents some leaf disease detection techniques.

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