A Benchmarking of Learning Strategies for Pest Detection and Identification on Tomato Plants for Autonomous Scouting Robots Using Internal Databases

Table 2

Computer vision functions for pest detection.

IMAGE PREPROCESSING FUNCTIONS
Check image quality	Checks the image quality level in order to determine whether the image is processed or a new one is requested. Different functions or filters are applied depending on the image quality level.
Emphasize image	Enhances the contrast of the image.
Gauss filter	Smoothens an image using discrete Gauss functions.
Illuminate	Very dark parts of the image are illuminated more strongly, and very light ones are darkened.
Image enhancement	Modifies the image to improve its visual appearance. Sharpening and magnifying algorithms will accentuate pictures features.
Image restoration	Removes blur and noises from images.
BACKGROUND SUBSTRACTION FUNCTIONS
Decompose RGB	Converts a three-channel image into three one-channel images with the same definition domain.
RGB to HSV	Transforms an image from the RGB colour space to an HSV (Hue, Saturation, and Value). HSV is defined in a way that is similar to how humans perceive colours.
Reduce image domain	Reduces the definition domain of the given image to the indicated region. It subtracts a region to a specific image.
Region segmentation	Segments images into regions of the same intensity.
Threshold image	Segments an image using a local threshold. It selects those regions in which the pixels fulfill a threshold condition.
Automatic threshold	Segments an image using thresholds determined from its histogram.
Edge detection	Detects edges using filters such as Deriche, Lanser, Shen, Canny, and Sobel.
FEATURE EXTRACTION FUNCTIONS
Get region features	Gets different features related to colour, texture, and shape.
Connected regions	Determines the connected components of the input regions.
Select specific shape	Chooses regions according to shape feature values such as area, width, and circularity.
Count and crop regions	Counts and crops the possible regions with pests. It generates the input for the machine learning algorithm for pest classification.