Techniques for Plant Disease Diagnosis Evaluated on a Windows Phone Platform

Author(s):  
Nikos Petrellis
Author(s):  
Karen K. Baker ◽  
David L. Roberts

Plant disease diagnosis is most often accomplished by examination of symptoms and observation or isolation of causal organisms. Occasionally, diseases of unknown etiology occur and are difficult or impossible to accurately diagnose by the usual means. In 1980, such a disease was observed on Agrostis palustris Huds. c.v. Toronto (creeping bentgrass) putting greens at the Butler National Golf Course in Oak Brook, IL.The wilting symptoms of the disease and the irregular nature of its spread through affected areas suggested that an infectious agent was involved. However, normal isolation procedures did not yield any organism known to infect turf grass. TEM was employed in order to aid in the possible diagnosis of the disease.Crown, root and leaf tissue of both infected and symptomless plants were fixed in cold 5% glutaraldehyde in 0.1 M phosphate buffer, post-fixed in buffered 1% osmium tetroxide, dehydrated in ethanol and embedded in a 1:1 mixture of Spurrs and epon-araldite epoxy resins.


Nanoagronomy ◽  
2020 ◽  
pp. 101-123 ◽  
Author(s):  
Afifa Younas ◽  
Zubaida Yousaf ◽  
Madiha Rashid ◽  
Nadia Riaz ◽  
Sajid Fiaz ◽  
...  

Author(s):  
Swapnil Sapre ◽  
Iti Gontia-Mishra ◽  
Vishwa Vijay Thakur ◽  
Sumana Sikdar ◽  
Sharad Tiwari

2021 ◽  
Vol 2062 (1) ◽  
pp. 012009
Author(s):  
Sushreeta Tripathy

Abstract In the area of research, diagnosis of disease symptoms in the plants duly applying image processing methods is a matter of big concern. The need of the hour is to prepare an efficient plant disease diagnosis system that can help the farmers in their cultivation and farming. This work is an attempt to prepare a framework of plant disease diagnosis system by using the cotton plant leaves. The digital pictures of cotton leaves are obtained to undergo a set of image processing techniques. Thresholding based segmentation techniques are used to remove the region of interest (ROI) i.e., infected part from the enhanced images. Consequently, diseases are detected from the region of interest by using an accurate set of visual texture features. At last treatment actions are taken to supervise the diseases found in the plants. This work will help the farmer’s society to take effective measures to protect their crops from diseases.


2021 ◽  
Vol 12 (4) ◽  
pp. 1084-1092
Author(s):  
N. V. Megha Chandra Reddy ◽  
K. Ashish Reddy ◽  
Sushanth G ◽  
Sujatha S.

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