Artificial neural network to predict leaf population chlorophyll content from cotton plant images

Document Type



Leaf population chlorophyll content in a population of crops, if obtained in a timely manner, served as a key indicator for growth management and diseases diagnosis. In this paper, a three-layer multilayer perceptron (MLP) artificial neural network (ANN) based prediction system was presented for predicting the leaf population chlorophyll content from the cotton plant images. As the training of this prediction system relied heavily on how well those leaf green pixels were separated from background noises in cotton plant images, a global thresholding algorithm and an omnidirectional scan noise filtering coupled with the hue histogram statistic method were designed for leaf green pixel extraction. With the obtained leaf green pixels, the system training was carried out by applying a back propagation algorithm. The proposed system was tested to predict the chlorophyll content from the cotton plant images. The results using the proposed system were in sound agreement with those obtained by the destructive method. The average prediction relative error for the chlorophyll density (μg cm−2) in the 17 testing images was 8.41%.


Architectural Engineering | Electrical and Computer Engineering | Other Electrical and Computer Engineering


Use Find in Your Library, contact the author, or interlibrary loan to garner a copy of the item. Publisher policy does not allow archiving the final published version. If a post-print (author's peer-reviewed manuscript) is allowed and available, or publisher policy changes, the item will be deposited.