DocumentCode :
2886550
Title :
Overview of image processing approach for nutrient deficiencies detection in Elaeis Guineensis
Author :
Hairuddin, Muhammad Asraf ; Tahir, Nooritawati Md ; Baki, Shah Rizam Shah
Author_Institution :
Fac. of Electr. Eng., UniversitiTeknologi MARA, Shah Alam, Malaysia
fYear :
2011
fDate :
27-28 June 2011
Firstpage :
116
Lastpage :
120
Abstract :
The most common problems occurred in Elaeis Guineensis or widely known as oil palm are plant diseases and pest outbreaks. The diseased oil palm plants normally shows a range of symptoms such as coloured spots or streaks that will occur on the leaves, stems, and seeds of the plant. At present, in the agricultural sectors, diagnosing the type disease of plants are based on human expert, which is alongside with the conventional method applied using test device and performing laboratory test. Therefore, the needs in new approach to classify type of diseases are preferable. Hence, the aim of this paper is to focus on an innovative method based on image processing technique for classifying the lack of nutritional disease occurred in oil palm leaves by analyzing the leave surface only. The result is usable as a guide for fertilization since the trees respond rapidly to the applied fertilizers. The main important concern is to ensure the sufficient amount of fertilizer since excessive intake of fertilizers will cause toxicity to trees and indirectly increase cost of fertilizers. Images of oil palm leaves will be captured using high-end digital imaging device to analyse the leaves surface. Further, feature extraction algorithms also will develop based on shape, texture, and colour of the disease type. The feature vectors will be attained acting as inputs to fuzzy classifier. Overall, the proposed method will benefit the oil palm industries to fulfill the industry demand.
Keywords :
agricultural engineering; agriculture; biology computing; botany; feature extraction; fertilisers; image classification; image colour analysis; image texture; object detection; vegetation; Elaeis Guineensis; feature extraction algorithms; high-end digital imaging device; image colour analysis; image processing approach; image texture; leaves surface analysis; nutrient deficiencies detection; nutritional disease classification; oil palm leaves; oil palm plants; plant diseases; trees toxicity; Diseases; Feature extraction; Fertilizers; Image color analysis; Nitrogen; Vegetation; elaeis guineensis; fertilizers; image processing; macronutrients; nutrient deficiencies;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
System Engineering and Technology (ICSET), 2011 IEEE International Conference on
Conference_Location :
Shah Alam
Print_ISBN :
978-1-4577-1256-2
Type :
conf
DOI :
10.1109/ICSEngT.2011.5993432
Filename :
5993432
Link To Document :
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