# Detecting Green Mold Pathogens on Lemons Using Hyperspectral Images
## 1 Introduction  
As the volume of global production of fruits increases, the use of a spectroscopic method for the purposes of monitoring surface condition and controlling the quality of the fruits in food and agricultural industries is continuously increasing, especially in determining the ripening status [1]. This is because the method is rapid, simple, and accurate for the above-mentioned purposes, as the visual colors of the fruit skin are directly related to the wavelengths of the light reflected from objects' surfaces, especially in the visible and near infrared spectral ranges. In addition, the spectroscopic method is noninvasive. As the most fruits are soft, any form of physical forces affecting their skin, such as collisions among them and shocks from outside causes bruises and scars of the skin. The bruises and scars can induce changes in the skin color of the fruits. Moreover, the juicy components emitted to the skin in the process of forming the bruises and scars can cause various diseases in fruits. These diseases are causing further changes in the skin color of fruits. Therefore, the noninvasive method is preferable for the inspection of fruits. Among the diseases, fungal infections induce such serious problems as asthma, cancer, and other diseases to human health [2; 3; 4; 5; 6]. As the fungi spores widely spread in the environment are graduallyaccumulated in the human body, the long-term exposure to them can induce damages to internal organs. Therefore, they are a significant threat to human health. In fact, it is known that more than a billion people suffer from the diseases, and more than 1.5 million people die each year by the diseases [7]. Among the fungi, the pathogen _Penicillium digitatum_ (green mold) is the fungus that is dominantly found in the skins of citrus fruits, especially in lemons, and is very abundant in our environment [8; 9]. The diseases in fruits outbreak when the biological pathogens infiltrate the fruits' surfaces. As a consequence, the fruits' skins change their colors, and other healthy fruits near the infected ones are contaminated. Thus, the infected fruits should be separated from the healthy ones. For this purpose, a quick and accurate method of identifying the infected fruits, i.e., inspecting the large amount of fruits in real-time and quantifying the degree of the infections should be devised. In this regard, the spectral imaging, which photographs an object or scene with the use of a spectral filter, has been recognized as a very promising inspection method of the qualities of agricultural products and foods [10; 11; 12; 13; 14; 15; 16]. This is because the imaging can reveal physical, chemical, geometrical, and optical parameters of the objects being inspected, whereas the spectroscopic method can provide the optical parameter only, because it can measure only the spectral intensity. However, the imaging has not been developed so far for the quantifying the degree of the infections. The separation between the fresh and infected fruits should be based on the quantification to prevent from discarding good ones with only insignificant amount of the infection. This is why the harvested citrus fruits are going through some forms of chemical treatments to protect the fruit skins from the pathogen infection [17; 18; 19; 20]. In addition to the chemical treatments, there are several other ways of treating the fruit surfaces: Covering the surface with plant extracts and essential oils, and exposing to hot water and ultraviolet light [21]. However, these treatments are costly and can be harmful to people, especially the chemicals used for the chemical treatment. Besides, the fruits can also be damaged on the way of transporting them. This requires another separation method. This means that the surface treatment methods are convenient but costly and risky for health. The spectral imaging is classified into three regimes such as multispectral, hyperspectral, and ultraspectral ones, depending on the transmitting spectral bandwidth of the spectral filter.