Visual detection of green mangoes by an unmanned aerial vehicle in orchards based on a deep learning method

Abstract

In this paper, a visual detection method by a UAV (unmanned aerial vehicle) was proposed to detect green mangoes on the surface of the tree crown rapidly and meet the need of estimating the number of mango fruits in orchards. The YOLOv2 model was used for quick detection of mango images captured by a UAV. First, mango images were collected by a UAV. These images were marked manually and used to build a training set and a test set. The parameters of the model were determined by experiments. The resolution of the images was 544 × 544 pixels. The batch size was 64, and the initial learning rate was 0.01. The mAP (mean average precision) of the trained model on the training set was 86.4%. Good detection results were achieved on images containing different fruit numbers and different lighting conditions with a precision of 96.1% and a recall rate of 89.0%. Finally, an experiment was conducted to estimate the actual number of green mango fruits. A method of generating an image of the whole mango tree was designed. The fruit numbers estimation model for green mango was obtained by linear fitting between the actual number and the detected number of mangoes. From the comparison of the fruit numbers of 10 mango trees determined by manual calculation and model prediction, an estimation error rate of 1.1% was achieved. The result demonstrated that the algorithm was effective for green mango detection and provided a methodological reference for quick estimation of the number of green mango fruits in orchards.