Overall conclusions

Decay caused by Penicillium spp. fungi is among the main problems affecting postharvest and marketing processes of citrus fruit because a small number of decayed fruit can infect a whole consignment, during long-term storage or fruit shipping to export markets, thus involving enormous economic losses and the blackening of the reputation of citrus producers. Therefore, effective early detection of fungal infections and removal of infected fruit are issues of major importance in commercial packinghouses in order to prevent the spread of the infections, thus ensuring an excellent fruit quality and absolute absence of infected fruit.

In this sense, the research efforts associated with this doctoral thesis have been oriented towards addressing such an important challenge for the citrus industry as the automation of the detection of early symptoms of decay, in order to provide alternatives to manual inspection using dangerous UV illumination, thus accomplishing this detection task more efficiently and, consequently, leading to a possible reduction of the use of fungicides. As a direct consequence of the conducted research work, this doctoral thesis has advanced in the field of the automatic detection of decay

134

in citrus fruit using optical systems and machine learning methods, thus fulfilling the overall goal of this doctoral research study. In particular, three different optical techniques operating in the visible and NIR spectral regions have been investigated, including hyperspectral imaging, light backscattering imaging and spectroscopy. Since the optical systems used in this thesis have not been limited to the visible part of the electromagnetic spectrum, they have shown capabilities beyond those of the naked human eye and traditional computer vision systems based on colour cameras, this fact being of special interest for detecting hardly-visible damage in citrus fruit, such as decay at early stages. In addition, a vast number of machine learning techniques aimed at data dimensionality reduction and classification has been explored for dealing with the optical measurements of citrus fruit in order to discriminate fruit with symptoms of decay from sound fruit. Due to the different inherent nature of each of the optical techniques used in this thesis, the treatment of the obtained data, and therefore the machine learning methods, has been specific to each optical technology in order to handle the particularities of each one.

The three optical technologies, coupled with suitable machine learning methods, investigated in this doctoral thesis have provided good results in the classification of skin of citrus fruit into sound or decaying in an early stage, with a percentage of well-classified samples above 90% for both classes despite their similarity. Therefore, these results represent an improvement on those obtained in previous works using traditional computer vision systems based on colour cameras and visible lighting for decay detection in citrus fruit, with a limited success rate of only around 65% in the identification of decaying fruits (Blasco et al., 2007a). Thus, in view of the results, this doctoral thesis has laid the foundation for the future implementation of an automatic system based on whatever of the studied optical technologies capable of detecting decay in early stages.

However, for future commercial application, the knowledge of the particularities of each optical technology would be useful in order to take advantage of some favourable characteristics specific to each technology or address the associated limitations. In this respect, in order to have a better understanding of the connections between the different technologies, some characteristics of the investigated optical techniques associated with their practical application for fruit inspection have been compared and compiled from the experience acquired through the development of this doctoral thesis. These characteristics, as well as the main connections between technologies, are summarised as follows, paying special attention on the specific systems used in this thesis: a hyperspectral vision system based on LCTFs, a spectroscopy system in reflectance mode and a LLBI system.

For example, although both spectroscopy and hyperspectral imaging cover a large number of narrow spectral bands over a continuous spectral range, hyperspectral imaging acquires simultaneously spectral and spatial information from a fruit, while spectroscopy provides only

135 spectral information captured at a particular spot on the sample, since the point detector used in this technology has size limitation. Conversely, this lack of spatial information makes spectroscopy much less time-consuming and more appropriate for real-time applications than hyperspectral imaging. In fact, hyperspectral systems are so time-consuming that, in practice, they are used just as a means for selecting the particular set of wavelengths that will finally be used in multispectral systems suitable for the real-time product inspection, as done in this doctoral thesis.

Another important difference between spectroscopy and hyperspectral imaging is the way in which they use the light source. In this sense, contrary to the diffuse lighting used in hyperspectral imaging to illuminate the scene uniformly, in reflectance spectroscopy, light hits directly a fruit, partly penetrating into the tissue, and a light detector measures the reflected radiation, which contains information about the internal components of the fruit.

Similarly to reflectance spectroscopy, in backscattering imaging, the light source is also aimed towards the fruit. However, in backscattering imaging, the backscattered light is recorded by an imaging system, thus tracking spatial information of the light signal on the sample, unlike spectroscopy. In addition, in the particular case of LLBI systems, as that used in this doctoral thesis, laser light penetrates deeper into the fruit than broadband light (e.g. light generated by a halogen lamp), as that usually used in a reflectance spectroscopy system, thus obtaining more information about the fruit tissue. On the other hand, LLBI systems provide information just in a few wavelengths, unlike spectroscopy systems, which cover a large number of consecutive wavelengths. In addition, although LLBI systems can be potentially used to assess quality of fruit in a relatively cheap, simple and fast way, further advances in equipment are still required before commercial application. For example, future LLBI systems must be capable of taking images at several wavelengths simultaneously and at a faster speed than that obtained with the current LLBI systems, thus making it easier to incorporate this technology into an industry that demands real-time inspection. Moreover, instead of point lasers, perhaps line lasers should be applied on rotating fruit in order to explore the whole surface of each fruit. On the contrary, since spectroscopy is arguably the most advanced optical technology with regard to equipment and applications, such technological progress has already led to the development of spectroscopy systems with high acquisition speed used in the agro-food industry for real-time sorting of products according to their quality. However, despite such advances in spectroscopy systems, a few issues should be considered for the specific application of this technology for decay detection in citrus fruit. For example, spectrophotometers should be applied on rotating fruit in order to examine the whole fruit surface. Although rotation of fruit is also suggested for all the systems that inspect external defects in citrus fruit, this is of particular importance in spectroscopy systems for overcoming the pronounced lack of spatial information and, consequently, ensuring an exceptional fruit quality.

136