Three dimensional apple tree organs classification and yield estimation algorithm based on multifeatures fusion and support vector machine

The automatic classification of apple tree organs is of great significance for automatic pruning of apple trees,automatic picking of apple fruits,and estimation of fruit yield.How-ever,there are some problems of dense foliage,partial occlusion and clustering of apple fruits.All of the problems above would contribute to the difficulties of organs classification and yield estimation of the apple trees.In this paper a method based on Color and Shape Multi-features Fusion and Support Vector Machine(SVM)for 3D apple tree organs classifi-cation and yield estimation was proposed.The method was designed for dwarf and densely planted apple trees at the early and late maturity stages.196-dimensional feature vectors composed with Red Green Blue(RGB),Hue Saturation Value(HSV),Curvatures,Fast Point Feature Histogram(FPFH),and Spin Image were extracted firstly.And then the SVM based on linear kernel function was trained,after that the trained SVM was used for apple tree organs classification.Then the position weighted smoothing algorithm was used for clas-sified apple tree organs smoothing.Then the agglomerative hierarchical clustering algo-rithm was used to recognize single apple fruit for yield estimation.On the same training and test set the experimental results showed that the SVM based on linear kernel function outperformed the KNN algorithm and Ensemble algorithm.The Recall,Precision and F1 score of the proposed method for yield estimation were 93.75%,96.15%and 94.93%respec-tively.In summary,to solve the problems of apple tree organs classification and yield esti-mation in natural apple orchard,a novelty method based on multi-features fusion and SVM was proposed and achieve good performance.Moreover,the proposed method could pro-vide technical support for automatic apple picking,automatic pruning of fruit trees,and automatic information acquisition and management in orchards.

Broccoli seedling pest damage degree evaluation based on machine learning combined with color and shape features

The degree of pest damage evaluation on corps in the field environment is very important for precision spraying pesticides.In this paper,we proposed an image processing method to identify the wormholes in the image of broccoli seedlings,and then to evaluate the damage of the broccoli seedlings by pests.The broccoli seedlings were taken as the research object.The ratio of wormhole areas to broccoli seedling leaves areas(Rw)was used to describe the pest damage degree.An algorithm was developed to calculate the ratio of wormhole areas to broccoli seedling leaves areas.Firstly,broccoli seedling leaves were segmented from the background and the area of the leaves was obtained.There were some holes in segmentation results due to pest damage and other reasons.Then,a classifier based on machine learning was developed to classify the wormholes and other holes.Twenty-four features,including color features and shape features of the holes,were used to develop classifiers.After identifying wormholes from images,the area of the wormholes was obtained and the degree of pest damage to broccoli seedling was calculated.The determination coefficient(R2)between the algorithm calculated pest damage degree and manually labeled pest damage degree was 0.85.The root-mean-square error(d)was 0.02.Results demonstrated that the color and shape were able to effectively segment wormholes from leaves of broccoli seedlings and evaluate the degree of pest damage.This method could provide references for precision spraying pesticides.