Modeling Water Stress as an Indicator of Red Palm Weevil Infestation Using Field Sampling, Worldview-3 Reflectance, and Laboratory Analysis

Bannari, A.; Almansoori, Thuraya; Mohamed, A. M.; El-Battay, A.; Hameid, N.

doi:10.1109/igarss.2018.8518687

Cited by 1 publication

(1 citation statement)

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“…Another promising method for detecting RPW is hyperspectral imaging. Although it is a complex and expensive method but it shows very high accuracy detection as some studies showed [29], [30], [31] and deserves consideration. Also, swarm intelligence algorithms can be used to enhance and optimize both SVM and CNN.…”

Section: Future Workmentioning

confidence: 99%

An Intelligent Approach for Detecting Palm Trees Diseases using Image Processing and Machine Learning

Alaa¹,

Waleed²,

Samir³

et al. 2020

IJACSA

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Today's palm trees diseases which cause a huge loss in production are extremely hard to detect either because these diseases are hidden inside the texture of the palm itself and cannot be seen by naked eyes or because it appears on its leaves which are hardly examined due to how far they really are from the ground. In this paper we're interested in detecting three of the most common diseases threatening palms today, Leaf Spots, Blight Spots and Red Palm Weevil. Diagnosis of these diseases are done by capturing normal and thermal images of palm trees then, image processing techniques were applied to the acquired images. Two classifiers were used, CNN to differentiate between Leaf Spots and Blight Spots diseases and SVM for Red Palm Weevil pest. The results for CNN and SVM algorithms showed a success rate of accuracy ratio 97.9% and 92.8% respectively, these results are considered to be the best results in this domain as far as we know. The paper also includes the first gathered thermal images dataset for palms infected with Red Palm Weevil and healthy palms as well.

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Section: Future Workmentioning

confidence: 99%