System for determining the effect of active ingredients on diplotene, insects and other organisms in assay plates containing wells - Patents.com

Abstract

The present invention relates to an automated system and method for determining the effect of active ingredients on organisms, such as insects, diplotane, and nematodes, in assay plates containing wells, using image acquisition and image analysis techniques. The present invention allows for the characterization of populations of organisms at multiple time points.

Description

[Technical field]

[0001] The present invention relates to a system and method for determining the effect of an active ingredient on organisms such as insects, dichotomata, and nematodes in an assay plate containing wells. [Background technology]

[0002] Many species of insects, diplotylomata, and nematodes are pests in agriculture, because they can seriously impair plant metabolism through damage to plant organs, such as leaves. Examples of insects and diplotylomata are Myzus persicae, Fall armyworm, mustard beetle larvae, and spider mites, such as Tetranychus urticae. To combat the infestation of insects and diplotylomata, various chemicals, namely insecticides and acaricides, respectively, have already been developed. However, there is a strong need to identify further active ingredients that can effectively control pests. Summary of the Invention

[0003] For such searches, it is known to use assay plates. Typically, well standard plates (MTPs), for example 12-well MTPs, are used. The wells are filled, for example, with agar, and in each well, one leaf disk of a diameter suitable for each well is placed. On the leaf disk, for example, two-spotted spider mites, also called spider mites, are present. For the size of the MTP, about 80 mm x 110 mm, the object to be detected is very small, about 0.2 mm to 0.6 mm. To illustrate this, a magnified detail of a leaf (about 20 mm) in a well is shown in FIG. 1.

[0004] The task is to automatically photograph and evaluate the assay plates before and after application of the active ingredient using image acquisition and analysis techniques.

[0005] Image acquisition is performed by acquiring an image of an object in the well, in this case the support or leaf (piece), on a camera sensor, in which the object appears exactly as large as the object itself, the imaging scale in this case being approximately 1, also described as 1:1. In the context of the present invention, the entirety of each well of the MTP is recorded for each image.

[0006] Due to the leaf disk not being absolutely flat in the MTP well and due to the imaging scale the depth of field is very shallow, typically less than 1 mm, many areas in the image are out of focus. For image analysis a focused image of the entire well is required. This is achieved by using a combination of sequential photographing of the well and image processing algorithms, the so-called "focus stacking". For this purpose what is acquired is a series of images where only the focus or shooting distance is changed, so that the images essentially only differ in the focal plane. The focus stacking method creates an image of the well where the leaf is depicted perfectly in focus and is then evaluated.

[0007] During the evaluation, it is necessary to identify individual organisms, in particular spider mites, diplodocids, insects or nematodes, ideally with the corresponding developmental stages. It should also be possible to distinguish between live and dead organisms. In other words, an automatic classification of organisms should be possible according to the criteria adult, larva, egg, N / A (undetermined), as well as the subcategorization live, dead, N / A (undetermined).

[0008] Since live animals can move very quickly and it is desirable to perform measurements quickly overall, it is desirable to have a relatively short measurement time per well, and furthermore, to allow high throughput screening, image acquisition of a complete MTP (multi-titer plate) should not take more than 50 seconds.

[0009] It is therefore an object of the present invention to provide a solution for determining the effect of active ingredients on live organisms, in particular insects, diplotane and nematodes, in assay plates containing wells. In particular, the solution according to the invention should be usable when the organisms are present on a non-flat support, for example a leaf piece, or when they move within the well. The solution should allow the effect of the active ingredient to be determined quickly and reliably, and should allow a high-throughput screening of the effect of active ingredients on live insects, diplotane and nematodes.

[0010] The object underlying the present invention is achieved by a system for determining the effect of an active ingredient on a living organism contained on a support in a well according to claim 1. Embodiments of the system can be found in the claims dependent on claim 1. The object underlying the present invention is further achieved by a method according to claim 12. Embodiments of the method can be found in the claims dependent on claim 12. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0011] The device for obtaining macroscopic images of organisms contained on a support in at least one well according to the invention comprises: a camera having an optical system and arranged above the well, and operable to acquire a number of macroscopic images from the well, with different focal planes; a device for changing the focal plane; a device for illuminating the wells using at least one light source, the device illuminating the wells uniformly from above, a device for positioning the wells and which is horizontally movable in two directions x and y.

[0012] The focal plane can be changed by changing the focus of the optical system and / or by changing the distance between the optical system and the cell culture plate.

[0013] The device according to the invention is capable of acquiring macroscopic images of other objects having complex three-dimensional structures, such as crystals, or objects at unknown distances relative to a camera preferably housed on a mount.

[0014] Preferably, the device for positioning the wells, the high resolution camera and / or the device for changing the focal plane are controllable using a computer program. Particularly preferably, all these components of the apparatus are controllable.

[0015] The device includes components for transmitting, storing and further processing the images.

[0016] Preferably, the device is part of a system including modules designed to operate respective corresponding components of the device for acquiring macroscopic images, the modules of the system being connected to the components of the device via interfaces.

[0017] Typically, for operating the respective components of the device for acquiring macroscopic images, a module designed to adjust and manipulate the device for modifying the focal plane, said module being also called focal plane module and connected to the device for modifying the focal plane; - a module for image acquisition and camera control, also called image acquisition module, connected to the camera and designed to acquire a number of images (a series of images) with a different focal plane for each well; one or more database modules designed to store a number of images, one of which is connected to the camera;

[0018] It is preferred to acquire images of a number of wells (generally also referred to as a well array) arranged in a cell culture plate. In the wells spaced apart from the cell culture plate, the organisms and the support, e.g. a leaf piece, can be in contact with the active ingredient. The cell culture plate has a bottom surface, a top surface and a side wall extending between the bottom surface and the top surface. Different wells can be filled with different active ingredients and / or different organisms. There can also be wells in which only organisms are present without containing active ingredients. A person skilled in the art will plan and carry out the structure of the relevant assay as required.

[0019] For efficient and unambiguous processing and evaluation of the acquired images, each cell culture plate usually has a readable ID, for example a barcode, which is readable and transmitted to a database together with the analysis results in a manner specific to the wells and cell culture plate. It is a priority to store information about the assay, for example active ingredient, amount of active ingredient, organism, etc., in a manner specific to the wells and cell culture plate as well. For this purpose, the system has a database module or is connected to such a database.

[0020] Preferably, the system includes a process module designed for adjusting the image acquisition, whereby a series of images of a well is acquired while the focal plane is changed between two images in the series according to a predetermined sequence.

[0021] Typically, a series of multiple images are stored in a database module in a well- and cell culture plate-specific manner.

[0022] At the end of the acquisition of a sequence of images of a well, a command is typically issued to the camera to position the next well, preferably to a device for positioning the well.

[0023] Preferably, the device for positioning the wells is horizontally movable in the x and y directions. Such a device is also called an X / Y table or a cross table. The corresponding wells are reached using an adjustable X / Y table / cross table to acquire a series of images. In a particular embodiment of the device, a motorized scanning table is used.

[0024] Preferably, this device for positioning wells is computer controllable, for this purpose the apparatus has a corresponding interface with a corresponding module, also referred to as a well positioning module, configured for operating the device for positioning wells.

[0025] In this embodiment, the well positioning module comprises: - placing the wells in a predetermined position relative to the optical system (also called alignment of the wells) by moving the cell culture plate horizontally using the device for mounting the cell culture plate, -Designed to repeat for each well until all wells have been recorded.

[0026] In a particular embodiment of the device, the components of the device are housed in a housing. In that case, preferably, the device includes a UV lamp for cleaning the housing. Preferably, the microorganism removal for positioning the wells can be fed into and withdrawn from the housing. Preferably, the well positioning module is then designed to automatically feed / withdraw the device for positioning the wells into and out of the housing.

[0027] The organisms are depicted by using a camera, preferably comprising a sensor that allows for a 1:1 imaging scale with respect to the wells on the sensor.

[0028] To obtain optimal images, preferably a well with an inner diameter of 22 mm and a square sensor with a width of 23 mm or a sensor surface of 5120 x 5120 pixels (25 MPix) is used.

[0029] Typically, a colour image is acquired, preferably in the visible spectrum.

[0030] As the animals' fast movements necessitate relatively short measurement times, an Allied Vision Bonito Pro camera was chosen, with a CoaxExpress interface between the camera and the control device, which allows the acquisition of images at up to 60 FPS (frames per second).

[0031] The optical system is constructed by combining a lens featuring an imaging scale of, for example, 1:10 with a bellows or intermediate ring to achieve an imaging scale of 1:1 or greater between the well and the sensor surface.

[0032] Usually, because the support is not flat in the well, and because the depth of field is very shallow due to the imaging scale, aperture and working distance, many areas in the image are not in focus in the case of single frame acquisition. For image analysis, an image of each well is required in which the entire image is in focus. For this purpose, the series of images acquired by the camera is preferably processed. This is achieved by using a combination of continuous photography (series of images) and digital image processing techniques, so-called "focus stacking" / focus shifting. For this purpose, a sequence of images / series of images is used in which only the focal length or shooting distance is changed, so that the images differ substantially only in the focal plane. In this case, a series of images of the entire well can be acquired within a maximum of one second. In high-throughput screening, this is a clear advantage compared to the prior art.

[0033] Up to now, the prior art has only acquired a series of images (focus stacking) with focus movement at a suitable resolution (i.e. number of pixels per organism) for the identification of small objects and for the combination of a large number of series of images (focus stacking images) for each well, from only a part of the well. This method has the drawbacks of being time-consuming per well, having to align the images with each other in a complicated manner, and of the risk that moving organisms are recorded twice or not at all.

[0034] The change of the focal plane is achieved using a controllable component for changing the focal plane, which can be achieved by focusing with a lens, or by changing the distance between the camera and the object, or between the lens and the object (in this case the support), along a vertical axis (z-axis).

[0035] In a first embodiment of the invention, the optical system consists of a fixed focus lens and a bellows. In this embodiment, the focal plane is changed by adjusting the bellows. Preferably, the bellows is adjustable using a focal plane module via an interface, which is designed to adjust the bellows. In this embodiment, the distance between the lens and the cell culture plate is typically defined by a fixed mounting of the camera relative to a device for positioning the wells.

[0036] In a preferred embodiment of the invention, the optical system consists of a fixed focus lens, for example with an imaging scale of 1:10, and one or more intermediate rings (see FIG. 2). The configuration of the fixed focus lens is preferably calculated so that an imaging scale of 1:1 is achieved between the sensor and the wells. For example, the one used for the above-mentioned sensor and wells from a standard 12-well MTP is a QIOPTIQ INSPEC.XL 105 / 5,6-1,0X precision lens for an image circle of up to 82 mm, optimized for the desired magnification by assembling it with a lens barrel of about 120 mm. In this embodiment, the focal plane is usually set only once and is not changed afterwards. The focal plane is changed by changing the shooting distance, defined as the distance between the object and the lens. This change can be achieved by moving the camera or the wells along the vertical axis (also called the z-axis). Preferably, the camera is mounted on a device for moving the camera, which allows a precise movement of the camera along the vertical axis. For example, the camera is precisely adjustable and movable using a motorized z-axis. Preferably, the device is controllable for precise adjustment and movement.

[0037] Preferably, the device for moving the camera has an interface to a focal plane module, the focal plane module being configured to set and vary the focal plane.

[0038] In certain embodiments of the present invention, the focal plane module comprises: - to adjust the camera or to set the focal plane by aligning the camera along the vertical axis (z direction); and - calculating / setting the distance between the focal planes for two images of the sequence of images, It is designed to change the focal plane by moving the camera according to a given distance.

[0039] Moving the camera relative to the well for image capture is also referred to as camera movement.

[0040] Typically, a process module controls the image acquisition for each well. Usually, up to 50 individual images are acquired for each well in rapid succession and with different focal planes during camera movement. The typical distance between the focal planes of two images is 0.25 mm.

[0041] The design of the optical system means that the amount of light is low, and very bright illumination is required. Preferably, a light output of 250W to 350W is required. To meet this requirement, preference is given to using components for coaxial illumination, including a light source and a semi-transparent mirror attached for coaxial illumination. The light intensity was achieved by using an LED array as a light source. For example, a light source consisting of 16 white high-power LEDs was used, achieving a light output of about 300W (with a flash). In order to obtain a uniform and bright illumination, the components for coaxial illumination preferably have a diffuser between the light source and the mirror. It may be advantageous to cool the light source, for example this is achieved using components for passive cooling. The principle of coaxial illumination is known to the person skilled in the art and is described, for example, at http: / / www.effilux.fr.

[0042] The components for coaxial illumination are preferably designed as flash illumination with short and very bright flashes in order to acquire images in rapid succession during the movement of the camera / lens combination and the animal. For this purpose, the light source preferably operates in flash mode, for which purpose an illumination module is typically connected to the camera. When using the above-mentioned light source, flash times of 40 μs to 1500 μs can be achieved. Typically, the control device is configured to control the illumination device in flash mode.

[0043] Typically, the coaxial illumination element is sized to uniformly illuminate the entire assay plate, or at least the wells located beneath the camera. Preferably, the coaxial illumination element is sized to illuminate approximately 120% of the area of ​​the wells to be recorded.

[0044] Typically, an assay plate has a fixed number of X wells per row and Y rows arranged side by side, resulting in a total of X x Y individual wells. For the solution according to the invention, a cell culture plate with a total of 30 wells (3 x 4 grid) is preferred, although alternatively a 4 x 6 or 6 x 8 or 8 x 12 grid could be used as well.

[0045] A method for determining the effect of an active ingredient on organisms such as insects, diplotane and nematodes in an assay plate containing wells envisages first filling at least one well of a cell culture plate with a leaf on agar, the organisms and the active ingredient, after which the cell culture plate is placed in an apparatus as described above.

[0046] Image acquisition may be performed using a method for image acquisition. a) positioning a well or well array in a device for positioning wells to a predetermined position relative to a camera by moving the well or well array horizontally using the device for positioning wells; b) setting the focal plane by operating a device for changing the focal plane; c) acquiring an image using a camera; d) storing the images in a database module; e) changing the focal plane by operating a device for changing the focal plane; f) repeating steps c) and d) to obtain a predetermined number of images in the series of images; g) repeating steps a) through f) for the next well until all wells have been recorded.

[0047] In a particular embodiment of the method according to the invention, the sequence of images is processed using focus stacking techniques to obtain an image that is as completely in focus as possible, i.e. an image with an increased depth of field.

[0048] Preferably, the system according to the invention comprises a module, also called image processing module, designed to process a sequence of macroscopic images using the focus stacking method, the system according to the invention being designed such that it is possible to provide a sequence of images from a database module, to perform the focus stacking method in the image processing module, and to store in the database module the images with increased depth of field generated using the focus stacking method.

[0049] Image processing using focus stacking typically involves: - providing a sequence of a plurality of images to a module for image processing; - performing an analysis of image focus for each image; - extracting from the different images the areas that are in the best focus in each case; - carrying out an image transformation to optimally overlay the images before actually assembling the individual images, since the scale of the images may change when changing the focus, and since objects in the images may be slightly misaligned; - then combining the most in-focus regions of each image to form a single image with increased depth of field.

[0050] Using this method, at least one image with increased depth of field is provided for each well.

[0051] Typically, images that are fully in focus are stored in a database module and / or transferred to a module configured for image analysis (also referred to as an image analysis module).

[0052] It is obvious to a person skilled in the art that the selection and design of the modules of the system can be adapted to the controllable devices of the apparatus according to the invention for acquiring macroscopic images, and the accommodation of the image processing and image analysis modules can also be designed as desired according to requirements.

[0053] Below are the automated analysis steps on a macroscopic image, preferably one that is fully in focus. If a fully in focus image does not exist, the analysis can be performed on the part of the macroscopic image that is in focus.

[0054] The analysis is performed by a piece of image recognition software that is configured to examine the image for the presence of certain (distinctive) features. In the present invention, the organisms on the support are identified by object recognition and classified according to predefined characteristics.

[0055] In a preferred embodiment of the analysis, organisms are automatically classified according to criteria also referred to as stage: adult, larva, egg, N / A (undeterminable), and / or according to criteria also referred to as status: alive, dead, N / A (undeterminable).

[0056] Typically, the location and size of the creature within the image, and its corresponding status and / or stage, respectively, are determined and output.

[0057] For the machine analysis of the images, a model is used that contains the connections between the image features and the above-mentioned classifications. For the analysis, one or more neural networks for image analysis are used that need to be trained.

[0058] Preferably, supervised learning is used for training one or more neural networks. This means that the neural network receives and is trained with images (training and validation images) in which all the organisms are annotated by the knowledge of experts. The annotation includes assigning a position (also called object position) for each organism, assigning an object size, and assigning a stage and / or status. The annotated images are typically created by an expert marking the organisms visually recognized in the image via a user interface. For example, each organism is individually surrounded by a RoI (region of interest, e.g. in the form of a rectangle / bounding box) and annotated with the desired classification (status and stage). This information can be stored in a text file together with the image file name. What is typically stored in the text file is the position of the RoI and its size, or alternatively the pixel coordinates assigned to the organism and the classification for each organism.

[0059] Preferably, object sizes are also automatically collected and stored. In a further step, an expert may be requested, via a user interface, to identify each object as an animate object and further annotate it accordingly.

[0060] If such annotated organisms exist, the expert may be requested to assign one or more predefined classifications to each annotated organism (e.g., a developmental stage of adult, larva, egg, or N / A (undeterminable), and / or a distinction of living, dead, or N / A).

[0061] A set of annotated images (the training input) is then used to train the neural network. For example, the image set used had approximately 16,000 annotations.

[0062] Newly acquired images can then be analyzed using these trained algorithms (neural networks).

[0063] The above method, which is implemented by a special type of neural network, is therefore divided into object recognition (location and size) and object classification.

[0064] For the application of the method according to the invention in the context of screening of small living organisms, a priority is given to the selection of a neural network for object detection having the required accuracy and speed.

[0065] For example, region-based convolutional neural networks (also called R-CNN) are used. In particular, a two-module solution, Faster R-CNN, is used, as described for example by Ren et al. (Ren, Shaoqing; He, Kaiming; Girshick, Ross; Sun, Jian: Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks. In: Advances in Neural Information Processing Systems (NIPS), Curran Associates, Inc., 2015, pages 91-99, https: / / arxiv.org / pdf / 1506.01497.pdf). The first module is a deep fully convolutional network that proposes regions, also called the Region Proposal Network (RPN). After the RPN, "RoI pooling" is performed to obtain a fixed size RoI. In the second module, the result of "RoI pooling" is classified using the Fast R-CNN detector (R. Girshick, "Fast R-CNN," in IEEE International Conference on Computer Vision (ICCV), 2015 - DOI: 10.1109 / ICCV.2015.169). For training Faster R-CNN, the "four-step alternating" training method proposed by Ren et al. was chosen.

[0066] More precisely, the Faster R-CNN algorithm first examines the image for potential objects, and object recognition and its location determination are typically achieved using a region proposal network (RPN), where each point in the image is assigned to either an object (in this case, a suspected life form) or to the background or support. Priority is given to determining the probability that an object exists at a particular point in the image, also called the "score", which is then filtered using an adjustable threshold. For example, a deep residual network with 101 layers (also called ResNet 101) was used as the RPN (Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun, Deep Residual Learning for Image Recognition, CVPR paper - http: / / openaccess.thecvf.com / content_cvpr_2016 / papers / He_Deep_Residual_Learning_CVPR_2016_paper.pdf). For example, we used Faster R-CNN from the tensorflow object detection API with ResNet 101.

[0067] Automatic assignment of classification can also be achieved using a properly trained Faster R-CNN algorithm.

[0068] As an alternative to Faster R-CNN, other deep learning multi-object detection algorithms can be used, such as, for example, YOLO, YOLOv2, YOLOv3, Retina Net, SSD, R-FCN, Fast R-CNN, R-CNN, etc.

[0069] In one example, found objects are automatically assigned to a stage and / or status only if the presence of a life form is deemed reliable. A value regarding the validity of the classification for the corresponding stage and / or status assignment can be calculated and output as well.

[0070] In one example, a test dataset of annotated images is used to determine the reliability of the machine-implemented analysis results, and an error rate, e.g., in the form of a confusion matrix, can then be determined.

[0071] The results of the corresponding analysis, ie the location and classification of the organisms, are preferably stored in a database module and / or output in a well- and culture plate-specific manner.

[0072] In a particular embodiment of the method, for the visualization of the results, each organism is marked differently on the acquired images according to its classification by stage and status, according to predefined rules. For example, organisms of a certain stage are surrounded by a frame with a defined shape, and the status of the organism can be highlighted by coloring the frame. Also text marking is possible. The choice of marking is made according to need. One possible marking is illustrated in FIG. 5.

[0073] Based on the calculated number of organisms per stage and / or status, it is possible to create a phenotypic time series (FIG. 6) of the population, i.e., the phenology of the organisms, which describes the proliferation of the organisms and their phenotypic changes over time.

[0074] It should be noted that the methods for image analysis may also be used for the analysis of images that were not provided using the methods for image acquisition and processing, provided that the images are of suitable quality.

[0075] Without wishing to limit the invention to the features or combinations thereof described and illustrated, the invention will now be described in detail with reference to the drawings and examples. [Brief description of the drawings]

[0076] The invention will be elucidated more particularly using the exemplary embodiments illustrated in the drawings, in which: [Figure 1] Figure 1 shows spider mites on leaf discs in a well. [Diagram 2] FIG. 2 shows an exemplary embodiment of an apparatus according to the invention for acquiring macroscopic images in a cross-section. [Diagram 3] Figure 3 shows the system diagram. [Figure 4] FIG. 4 shows an example of an image with labels for organisms and their properties. [Diagram 5] Figure 5 shows an example of a population phenotypic time series. [Explanation of symbols]

[0077] List of reference numbers 1. Equipment for acquiring macroscopic images 2. System 10. Housing 11 Camera 12 Optical system 13 Lens 14 Intermediate Ring 15 Devices for coaxial lighting 16 Cross table, i.e. a device movable horizontally in two directions, x and y, for mounting a cell culture plate (30) with multiple wells. 20 Device for moving the camera 21 Motor for moving the camera along the z-axis 22 Camera Mount 23 z-axis (vertical axis) 30 Cell Culture Plates 31 Wells of cell culture plate (30) 40 Process Module 41 A focal plane module for controlling a device for moving the camera (20) 42 Image acquisition module for controlling image acquisition 43 Database Module 44 Well positioning module, or module for controlling the cross table (16) 45 Interface 46 Image processing module designed to process macroscopic images using focus stacking 47 User Interface 50 Image Analysis Module 51 Database Module

[0078] FIG. 2 shows a schematic representation of an apparatus for acquiring macroscopic images in cross section. The apparatus 1 comprises a housing 10 in which a camera 11 with a lens 13 constituting the lens 13 and an intermediate ring 14 are arranged. Furthermore, a cross table for positioning the wells 31 of the cell culture plate 30 is provided in the housing 10. The apparatus comprises a device 15 for coaxial illumination of the cell culture plate 30, which is not shown in detail since it is known from the prior art. The apparatus 10 is able to create a number of chronologically consecutive digital images of the wells 31 with the camera 11 at a predefined change in the focal position, the images being acquired from the upper surface 33 of the wells. The camera 11 is therefore arranged above the wells 31. To change the focal position, the apparatus 1 comprises a device for moving the camera 20 and comprises a camera mount 22, a z-axis 23 and a motor for moving the camera 21 along the z-axis. The camera 11 can move continuously and record the wells 31 located at a predefined distance. Alternatively, the camera 11 can be stopped for recording and can proceed. Figure 3 shows a diagram of a system according to the invention, whose components allow for the automation of image acquisition, image processing and image analysis. Image acquisition is performed by a focal plane module 41 and an image acquisition module 42 according to a predefined sequence. A series of images is automatically acquired. Once a series of images has been acquired, the next well 31 is placed under the camera 11 using the cross table 16 and a new series of images is acquired. To control the cross table 16, the system includes a well positioning module 44. The sequence of image acquisition for one or more wells is specified by a process module 40, which is configured to acquire images using a method. From the series of images, an image processing module 46 generates an image in full focus using a focus stacking method. This image is provided to an image analysis module 50, which analyzes the image.The results of this analysis are stored together with the image in the database module 51 and displayed via the user interface 47 .

Claims

1. 1. A system for determining the effect of an active ingredient on an organism contained on a support in at least one well, comprising: - a device for acquiring a plurality of images of at least one well containing an organism contained on a support, said device comprising the following components: a camera having an optical system, said camera being arranged above said well and configured to acquire a number of images of a scene in which at least one well is present, said images being acquired at different focal planes; a device for modifying said focal plane; a device for illuminating said wells using at least one light source, said device illuminating said wells uniformly from above; An apparatus comprising: - it is a control device and contains the following modules: a process module configured to control the device for acquiring said images, the process module being designed such that a series of images with different focal planes for each well is acquired according to a predefined sequence; a focal plane module for adjusting and manipulating said device to modify said focal plane; an image acquisition module configured to operate said camera; at least one database module designed to store said images and / or a sequence of said images, and a control device including: The image acquisition module and the database module for camera control are connected to the camera via an interface; the process module is connected to the image acquisition module and to the focal plane module via an interface; moreover, an image analysis module designed to perform object recognition on life forms in images and to perform classification of said life forms using a neural network trained on a number of annotated images containing classified life forms; Equipped with the camera for acquiring the plurality of images comprises a sensor, the sensor allowing a 1:1 or greater imaging scale with respect to a well on the sensor; The image acquisition module is configured to process the images at different focal planes into an image of the well with an increased depth of field using focus stacking, and the image acquisition module is configured to perform the object recognition and classification of living organisms on the image with increased depth of field; the device for acquiring the plurality of images is configured to acquire the plurality of images using a plate having at least one well, the plate having at least one well filled with a non-planar support, live organisms present or moving on the non-planar support or within the well, the live organisms being insects, diplotane, or nematode adults, larvae, and / or eggs, and an active ingredient; system.

2. - Adults, larvae, eggs, and / or -Alive or dead, The system of claim 1 , wherein the classification is performed into at least one of a list of classes:

3. 1. A method for determining the effect of an active ingredient on an organism contained on a support in at least one well, comprising: a) positioning a well or an array of wells containing an organism contained on a support in the system of claim 1; b) positioning a well in a predetermined position relative to a camera by horizontally moving the well or the array of wells using a device for positioning the well so that a number of images of a scene in which the organism is contained on a support are obtained; c) setting said focal plane by operating a device for changing the focal plane; d) acquiring images of the wells using said camera; e) storing said images in a database module; f) varying said focal plane by manipulating said device for varying said focal plane; g) repeating steps c) to f) to acquire a predetermined number of images at different focal planes; h) repeating steps a) to g) for the next well until a plurality of images at different focal planes have been acquired for all wells; Including, The multiple images of the well at different focal planes are processed using focus stacking into an image of the well with increased depth of field, and performing object recognition and classification of the organisms on the image with increased depth of field using a neural network trained on the multiple annotated images containing the classified organisms. method.

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