A Detection Method for Tomato Fruit Common Physiological Diseases Based on YOLOv2

The detection of tomato fruit physiological diseases can increase tomato yields and facilitate tomato fruit quality control. In this paper, a convolutional neural network called YOLOv2 was applied on the detection of healthy tomato fruits and tomato fruits with common physiological diseases. YOLOv2 is a target detection algorithm based on regression model, which has fast detection speed and good accuracy. In order to improve the network detection performance, the following improvements were made: First, image datasets were extended by data augmentation methods to decrease the chance of overfitting, and the augmented datasets containing 1000 tomato fruit images were obtained. Second, a grayscale processing module and a foreground extraction module were constructed to investigate the importance of image data type. Third, k-means clustering algorithm was applied in order to reduce model training time as well as improve detection effects. The mAP (mean Average Precision) of YOLOv2 tomato fruit detection network was 97.24%. Experimental results showed that the proposed method was effective on the detection of healthy tomato fruits and tomato fruits with common physiological diseases.

I. Introduction

Tomato is an important cash crop, and disease is one of the key problems that restrict the output and quality of it [1]. The process of artificial disease recognition is time consuming and subjective. Incorrect diagnosis and pesticide usage often occur in cultivation [2]. The effective automatic detection method for healthy and diseased tomatoes is of great significance for the development of tomato automatic harvesting, targeted drug application and many other intelligent farm machinery technologies.