CANOPY PRUNING GRADE CLASSIFICATION BASED ON FAST FOURIER TRANSFORM AND ARTIFICIAL NEURAL NETWORK

Canopy architecture optimization and pruning management are agricultural operations crucial for plant growth and fruit production. Classifying pruning grade and optimizing the tree canopy accordingly are essential for these operations. If the work is done properly, it can result in higher yields of quality fruit. In this article, we present a method utilizing fast Fourier transform (FFT) and a back-propagation artificial neural network (BP-ANN) to classify, different pruning grades in cherry orchards with an upright fruiting offshoots (UFO) training system to illustrate our approach. The approach was implemented automatically by first using a discrete FFT to extract frequency information from images of a cherry tree canopy and then applying a band filter to digitize the 2D FFT spectrum to a 1D array. A BP-ANN model was then used to classify the pruning grade of the trees. By combining image processing and ANN-based classification techniques, our approach was resilient to details, such as specific leaf shapes and leaf vein structure, and achieved an accuracy rate of over 80% in classifying pruning grades for UFO cherry trees with respect to human expert grading. Principal component analysis (PCA) was also applied to simplify the prediction model complexity while maintaining a similar prediction accuracy rate with a much less complicated input data set. Experimental results showed that our method could provide real-time classification of pruning grades for UFO cherry trees with reasonable prediction accuracy.