6533b862fe1ef96bd12c6bc2

RESEARCH PRODUCT

Estimation of Leaf Area in Bell Pepper Plant using Image Processing techniques and Artificial Neural Networks

Mohammad Hadi KhoshtaghazaSaeid MinaeiAli Reza MahdavianVahid MohammadiPierre Gouton

subject

WatershedArtificial neural networkbusiness.industryQuantitative Biology::Tissues and OrgansImage processingPattern recognitionStereo imagingGradient magnitudeComputer Science::Computer Vision and Pattern RecognitionMultilayer perceptronPepperRGB color modelArtificial intelligencebusinessMathematics

description

Measurement and estimation of physical properties of plant leaves have always been considered as important requirements for monitoring and optimizing of plant growth. This study aimed at utilization of image processing and artificial intelligence techniques for non-invasive and non-destructive estimation of bell pepper leaves properties in the first month of growth. Physical properties of bell pepper plant leaves were extracted from RGB images. The algorithm makes use of gradient magnitude and watershed image. Leaf area as the most important index of growth was estimated as a function of other physical parameters including leaf length, width, perimeter etc. Using stereo imaging, the leaf distance from the camera was measured and applied in pixel-wise calculations. Artificial neural networks (ANN) were trained based on a database of actual values of leaf properties (i.e. 311 bell-pepper plant leaves). The success rate of the developed algorithm for detection and separation of leaves was 84.32%. The Multilayer Perceptron (MLP) network could successfully estimate the leaf area values with a validation performance of 0.912.

yearjournalcountryeditionlanguage
2021-09-132021 IEEE International Conference on Signal and Image Processing Applications (ICSIPA)
https://doi.org/10.1109/icsipa52582.2021.9576778