## 1 Introduction  
The term biospeckles (or bio-speckles) is often used interchangeably with dynamic speckles [1], boiling speckles [2], dynamic laser speckles or biological laser speckles [3]. Biospeckles (BS) are dynamic patterns appearing as the effect of backscattering of coherent light directed at the surface of biological material. The illuminated surface reflects radiation that differs in phase, thus generating the phenomenon of interference. Consequently, radiation back-scattered from the surface forms an image of spots with an intensity distribution that may change overtime. Non-active samples generate time-invariant images whereas biological samples generate patterns consisting of both time-stable and time-variable speckles [4]. Such patterns may be registered as a movie by means of fast cameras and may be analysed as an image sequence or stack of time-varying images. The dynamics of the biospeckle phenomenon result from the physiological state of the sample, which may vary due to movement of molecules in cells, such as transport of substances across cell membranes or cytoplasm movements [5]. The dependence of biospeckle activity level on sample temperature has also been reported [6]. Due to simplicity of use and great suitability for monitoring subtle changes occurring in biological samples, BS has many applications in agriculture, mainly for pre and post-harvest quality monitoring of agricultural products including detection of damage and diseases [7; 8; 9; 10; 11; 12; 13]. The literature on using BS for detecting fruit infestation by insects is very scarce. Biospeckle imaging is considered a noninvasive technique since up to now no harmful effects of low-power laser light on plant tissues have been reported. Different techniques for measuring biospeckle activity (BA) are generally classified as quantitative or qualitative. The quantitative approach aims to describe the biospeckle activity of the material by means of comprehensive indicators that are calculated from the temporal variability of images (frames) in a sequence. Such indicators use first-order or second-order statistics, ignoring the spatial variation in speckle intensities in consecutive frames, and are often called biospeckle activity indices [14; 15] or global measures [16]. On the other hand, the same calculation apparatus may be applied to measure the temporal changes of intensity of each pixel separately, producing a kind of activity map which presents the spatial variation of BA. The former approach is recommended for homogeneous samples, while the latter is considered more appropriate for heterogeneous material wherein spatial variation in biospeckle activity is highly probable [16]. _D. suzukii_ infests mostly in soft fruits such as highbush blueberry, raspberry, blackberry, strawberry, currant, apricot, peach, cherry, plum and even wine grapes [17; 18]. Since its first detection in Europe in 2008 (Spain and Italy), this insect has spread extremely quickly across the continent, causing economic losses amounting to millions of US dollars [19]. Estimated revenue losses caused by _D. suzukii_ in strawberry, raspberry, blueberry and blackberry production in 2011-2013 in the Italian province of Trento were around 3 million euros per year (including partial budgeting). The additional costs related to implementation of an integrated control strategy (mass trapping, sanitation, application of insecticides) have been estimated at over 1 million euros per year [20]. Annual costs sustained due to the occurrence of _Drosophila suzukii_ in sweet cherry production areas of Switzerland under four scenarios of increasing fruit infestation ranged between 2000 and 71,000 CHF/ha [21]. Potential losses in peach and fig production in Brazil due to _D. suzukii_ (without considering any control strategy and growers' adaptation) were estimated at 30 million USD [22]. _D. suzukii_ shows an incredible ability to adapt in various climate conditions.