Mobile analysis and processing device, and method, for agriculture for tilling the soil and/or for manipulating flora and fauna

The invention relates to a mobile analysis and processing device (14) for agriculture for processing the soil and/or manipulating flora and fauna. The device (14) comprises at least one sensor (62), a tool unit (52) having at least one motor-driven tool (58), an actuator (48) for moving at least the tool (58) of the tool unit (52), a motor (50) for driving the tool (58) and/or the actuator (48), a database (78), a first communication unit (60) having an interface (70a), and a first computer (46) for controlling the sensor (62), the tool unit (52) and the actuator (48) by means of generated control commands. The data captured by means of the sensor (62) are continually compared with the data stored in the database (78) in order to generate corresponding control signals for the actuator (48), the tool unit (52) and/or the motor (50). By means of said device, mobility and flexibility are created, in accordance with which flexibility the device (14) forms a unit by means of which all data can be processed in real time, control signals can be generated for the actuator (48), the tool unit (52) and/or the motor (50) and immediate operation in accordance with the control signals is possible. Thus, combination with, for example, different carriers (12), which move the device (14) over the field if necessary, is possible.

PCT/EP2019/072522, international application filing date Aug. 22, 2019 and German patent application no. 10 2018 120 753.0, filed Aug. 24, 2018 are incorporated herein by reference hereto in their entireties.

FIELD OF THE INVENTION

The invention relates to a mobile analysis and processing device for use in agriculture for tilling the soil and/or for manipulating flora and fauna and to a method for real-time control of the tilling of the soil and/or of manipulating flora and fauna by this device.

BACKGROUND OF THE INVENTION

Weed control in agriculture is a very labor-intensive task, especially in organic farming which prohibits or restricts the use of chemicals. Depending on the crop cultivated, weed control may be necessary in close proximity to the crop. Weed control measures are usually taken in the early growth stage of the crop because at this stage, both crops on the one hand and weeds on the other hand are still very small and close to each other. In order to avoid damage to the crop, it is expedient to use selective methods.

Organic farming, for example for carrots, for this purpose adopts a labor-intensive, physically stressful manual approach using so-called lay down or prone weeders comprising platforms on which seasonal workers lie on their stomachs and remove the weeds manually.

For special crops with larger plant spacing, such as sugar beets or lettuce, tractor mounted implements are known which are able to recognize individual crops and control appropriate tools in such a way that they will leave the area of the crop untilled. No selectivity is required for this task, meaning that these systems do not check the areas to be tilled, but rather control the tool “blindly” based on the known crop position. In this case, the accuracy requirements are generally defined by the distance to the crop.

A device for weed control is disclosed in DE 40 39 797 A1, in which an actuator for destroying the weeds is permanently in operation, which operation will only be briefly interrupted when a sensor detects a crop. In this case, the carrier is in the form of a trolley.

Disclosed in DE 10 2015 209 879 A1 is a device for damaging weeds which has a processing tool. This processing tool is used to damage the weeds. In addition, a classification unit is provided which either has the position data of the weeds or detects the weeds and determines the position data. A localization unit determines a relative position between the processing tool and the weeds. A manipulator unit in the form of a trolley positions the processing tool accordingly based on the determined relative positions.

A corresponding device with a pressure conveying unit and a liquid dispensing unit is disclosed in DE 10 2015 209 891 A1. In this embodiment, weeds are destroyed by spraying them with pressurized liquid. The carrier in this case is in the form of a trolley.

DE 10 2015 209 888 A1 discloses the pulsed application of liquid to weeds in order to damage them. Here, too, the carrier is in the form of a trolley.

DE 10 2013 222 776 A1 discloses a ram mounted in a trolley, which ram is arranged in a guide device for guiding the ram. In this case, the ram is positioned on the weed and subjected to pressure. The weed is destroyed by the impingement of the ram under pressure.

Agricultural robots and harvesters, which are automated and equipped with telematics technology to support agriculture, are currently breaking new ground. In many cases, engineering principles and findings from space travel, remote sensing and robotics can be used to solve problems in agriculture. However, they have to be specifically adapted to the tasks in agriculture and require new devices and procedures.

For example, the existing automated agricultural robots mentioned above are systematically designed to drive down only one row of plants at a time. They will only tackle the flora, and only serially. Checks are usually made afterwards by inspection, for example by a qualified human being.

A disadvantage of the known devices is also that they are each specially designed trolleys which only drive down one row of crops at a time and are relatively inflexible to use.

SUMMARY OF THE INVENTION

It is the object of the invention to provide a mobile analysis and processing device for agriculture for tilling the soil and/or for manipulating flora and fauna, as well as a method for the device, which method permits real-time controlled, qualified removal of the detected flora and/or fauna as well as a parallel analysis of flora and fauna. Preferably, the device is adapted to be connected to different carriers that are used to move the device to and across the site where it will be used.

The invention is based on the insight that creating a mobile device that is independent of a carrier and includes all the components required for analysis and processing, will considerably increase the flexibility of its use and the resulting possibilities.

The invention therefore relates to a mobile analysis and processing device for agriculture for tilling the soil and/or manipulating flora and fauna. The device comprises at least one sensor, a tool unit with at least one motor-driven tool, an actuator for moving at least the tool of the tool unit, a motor for driving the tool unit and/or the actuator, a database, a first communication unit with an interface and a first computer for controlling the sensor, the tool unit and/or the actuator based on generated control commands. The data acquired by the sensor are continuously compared with the data stored in the database in order to generate corresponding control signals for the sensor, the tool unit and/or the actuator. This device creates a degree of mobility and flexibility that allows the device to form a separate entity that permits real-time processing of all data, generates control signals for the sensor, the tool unit and/or the actuator and thus enables immediate operation based on the control signals generated. This opens up possibilities for its combination, for example with different carriers for moving the device across the field as needed.

Preferably, synchronization of the data determined by the sensor with the database is performed in real time, in particular with a verification and classification of the data determined by the sensor. This increases the responsiveness of the device.

According to one embodiment of the invention, the sensor is a visual detection unit with a camera. The data to be processed is thus image data which can be easily compared with data in a database.

In order to be able to connect the device, if necessary, to a carrier for moving the device, appropriate means are provided for connecting the device to a carrier.

To facilitate the exchange of individual components and thus reduce set-up times, the device is designed in two parts. A first unit thereof contains the sensor, the tool unit, the motor for driving the tool of the tool unit and/or the actuator, the actuator, the first computer and the first communication unit including an interface. The second unit thereof contains the database, a second computer and a second communication unit including an interface. For data exchange, the first and second units can be connected to each other via the interface. In addition, the two-part design also makes it possible for the two units to be arranged spatially separately from one another. This is advantageous, for example, if the weight of the moving parts of the device is to be kept as low as possible. In this case, the second unit could be arranged in a fixed central position, with the first unit being moved around the field.

In this case, it is convenient for the first unit to comprise a first housing and for the second unit to comprise a second housing which will protect the components contained in the units from external influences.

The first and second housings can be detachably connected to each other via a plug-type connection. This permits the two units to be joined together in a modular fashion, and also facilitates replacement in the case of failure of one unit.

According to one embodiment of the invention, the first and second housings have, as means for connection to the carrier as required, receptacles associated with corresponding holding means of the carrier by means of which the device can be gripped and moved by the carrier. Alternatively or additionally, the first and second housings may have, as means for connection to the carrier as required, coupling means associated with corresponding coupling means of the carrier, by means of which the device can be connected to the carrier and moved by it. This enables simple and quick connection of the device to a carrier for transporting the device.

The tool unit preferably has at least one feed unit and a rotation unit which latter cooperates with the motor. This is an easy way of expanding the operating range of the tool without having to actually move the device.

Preferably, at a distal end thereof, the rotation unit is provided with at least the one tool, in particular with a tiller or with a blade unit. Rotation of the blade unit, for example, can be used to selectively destroy small insects or weeds, for example.

In order to further reduce the weight of the device, a voltage connection is provided for an external voltage supply. The voltage connection may be provided on the first unit. In the assembled state of the first and second units, the second unit can use this voltage connection to supply both the first and second units with voltage. Preferably, the voltage source of a carrier is used for the voltage supply.

In order to enable data exchange between a carrier and the device, the device includes an additional communication interface for the carrier.

The additional communication interface can be arranged in the first unit or in the second unit. Preferably, it is provided in the second unit.

The above-mentioned object is also accomplished by a method for real-time control of the tilling of the soil and/or of manipulating flora and fauna by the device of the above mentioned type, which method comprises the following steps:continuous recording over time of data-defined voxels and/or pixels by the sensor;transmitting the recorded data to a database;storing the recorded data in the database;qualitative data comparison of the recorded data with the data stored in the database, preferably also performing a segmentation, a data reduction and/or a verification of the recorded data by the computer;evaluating the compared recorded data with existing defined data sets in the database by a classifier connected to the computer;processing and conversion of the evaluation by the computer into control data and/or control-related data for the motor, the actuator, the tool unit and/or an associated carrier.

Preferably, after the control data and/or control-related data is available, the motor, the actuator, the tool unit and/or an associated carrier are started up for tilling the soil and/or for manipulating flora and fauna.

According to a preferred method of the invention, the evaluation is performed in a computer cooperating with the classifier, in particular in the second computer, and the processing and conversion of the evaluation into control data and/or data is performed in another computer, in particular in the first computer, for which purpose the evaluation is transmitted from the one computer to the other computer. This reduces computing time, since computers are capable of running in parallel. In addition, this eliminates the need for the two computers to be located adjacent to each other. For example, the second computer with the second unit can be arranged remotely from the first unit with the first computer.

Data storage, qualitative data comparison of the recorded data with data stored in the database and/or evaluation by the classifier are preferably supported by artificial intelligence. This makes it possible to create a system that operates almost autonomously.

The device, in particular the first and second units, can be of modular design which allows them to be connected to each other, but also to other units of an overall system.

Real time in this context means the possibility of being able to perform analysis and processing operations in situ in a single operation.

In the context of the invention, a voxel is understood to be a spatial data set that is generated by the sensor or by a visual detection unit in an imaging process, discretely or continuously in time.

Preferably, the actuator comprises a mechanical system, in particular a rotating unit that is located in a holder in the housing of the first unit.

Additional advantages, features and possible applications of the present invention may be gathered from the description which follows in which reference is made to the embodiments illustrated in the drawings.

Throughout the description, the claims and the drawings, those terms and associated reference signs are used as are listed in the List of Reference Signs below.

DESCRIPTION OF THE INVENTION

FIG.1is a schematic view of a carrier system10which comprises a carrier in the form of an aerial drone12and a mobile device14for tilling the ground and for manipulating flora and fauna in agriculture. The aerial drone12includes a drive16comprising four electric motors18and propellers20driven by them, seeFIG.3. In addition, the aerial drone12has 4 feet22disposed below the electric motors18.

According to the first embodiment of the invention, the aerial drone12comprises an energy source in the form of batteries24, which provides the energy supply for the drive16as well as for the further components of the aerial drone12and the mobile device14. For this purpose, a voltage interface26ais provided on the aerial drone12and a voltage interface26bcorresponding to this voltage interface26ais provided on the mobile device14, said interfaces being connected to one another via a detachable plug connection28. In addition, a communication unit30with an antenna32and a GPS unit34is provided which latter continuously determines the location of the aerial drone12, transmits the location data of the aerial drone12to the mobile device14, for allocation to the data acquired by the mobile device14, and to a remote central processing unit (not shown here). Telemetry can be performed with the aid of the GPS unit34, the communication unit30, and the mobile device14. In addition, a control unit12bis provided which controls the drive16.

In addition to the antenna32, the communication unit30of the aerial drone12comprises a further interface36awhich is assigned to an associated interface36bof the mobile device14, which interfaces are connected to one another for data exchange by a detachable plug connection38.

The mobile device14comprises two units14a,14b, namely a first unit14ahaving a first housing40and a second unit14bhaving a second housing42. The first housing40and the second housing42are releasably connected to each other via a plug connection44to form a unit constituting the mobile device14. There is a set of different first units14aon one side and a set of different second units14bon the other side, which units can be individually configured and adapted to the respective needs by simply connecting them together.

In the first housing40, there is a first computer46, an actuator in the form of a motor-driven movable arm48, a motor50cooperating with the arm48, a tool unit52arranged on the arm48and comprising a feed unit54and a rotation unit56. A tiller58is provided as a tool on the distal end of the rotation unit56. The motor50drives both the arm48and the feed unit54, the rotation unit56and thus also the tiller58. The arm48may be of multi-part design and have various joints, which are not shown here since such motor-driven kinematic units are known. The arm48is used to move the tool unit52relative to the aerial drone12to its area of use, so that the tool unit52with the feed unit54and the rotation unit56can use the tiller58to process the plants, for example to remove weeds, and/or to till the soil.

Furthermore, a communication unit60and a visual detection unit62are arranged in the first unit14a. The visual detection unit62comprises a camera64that captures images, a segmentation and data reduction device66, a classifier68that performs classification of a plurality of pixel fields composed of pixels based on an intermediate image or intermediate data generated by the segmentation and data reduction device66, as will be described in more detail below. The visual detection unit62is connected to the communication unit60.

The first unit14ahas an interface70awhich is associated with an interface70bof the second unit14b. Communication link72is used to connect the communication unit60via interface70ato interface70b, and via this to a communication unit74in the second unit14b. Via interface36b, the communication unit74of the second unit14bis connected via the plug-type connection38to interface36aand to the communication unit30of the aerial drone12.

A second computer76and a database78are furthermore provided in the second unit14b.

Illustrated inFIG.2is a further embodiment of the carrier system10, with the design of the aerial drone12being identical to that of the first embodiment. Only the mobile device14differs by a plug-type connection80between the first unit14aand the second unit14b, which furthermore also detachably connects the communication unit60of the first unit14ato the communication unit74. By simply plugging them together, different first units14acan be combined with different second units14bto form a mobile unit14.

FIG.3is a lateral view of the aerial drone12in which only two of four electric motors18with associated propellers20are visible. Below each of the electric motors18the feet22are arranged. Two gripper arms82a,82bare provided between the feet22, which are adapted to grasp and lift the mobile device14, and to release and set it down again as required. The mobile device14comprises the two units14aand14bwhich are detachably connected to each other via the connector80. In the first unit14a, the camera64can be seen as part of the visual detection unit62as well as the tiller58at the distal end of the rotation unit56.

The mobile device14can also be equipped with several different tool units52which are provided with a common arm48and, for example, a tool turret that will bring the required tool unit52into the activation position. However, it is also conceivable for the different tool units to each have their own actuator.

The flowchart ofFIG.4shows the steps that are performed in sequence in order to use the carrier system10for tilling the soil and for manipulating flora and fauna in agriculture.

In a first step84, the carrier system10is first used to determine the measures required on the associated agricultural land. For this purpose, the carrier system10is for example brought to an agricultural area to be tilled, such as an agricultural field, or flown there directly from a central location. There the aerial drone12with the mobile device14then takes off and flies over the agricultural field. A stationary central computing unit supplies the carrier system10with the necessary data about the agricultural field to be surveyed. The central computing unit can also be a smartphone in this case. The visual detection unit62with the camera64of the mobile device14is used to capture images of the agricultural field. The images are evaluated and, after a comparison with data in the database78, the necessary measures for this agricultural field are finally determined.

In a next step86, based on the determined measures for the agricultural field or for partial areas of the agricultural field, the mobile unit14suitable for the necessary measure is then compiled from a set of first units14aand a set of different second units14b, which two units14a,14bare then connected to each other.

In a subsequent step88, the gripper arm82aand the gripper arm82b, respectively, of the aerial drone12are used to grip the mobile unit14on the side and move it upwards towards the aerial drone12into a receptacle12aof the aerial drone12. In doing so, the voltage interfaces26a,26bare connected to each other via the connector28and the interfaces36a,36bare connected to each other via the connector38. This supplies the mobile device14with voltage from the battery24of the aerial drone12, and enables data exchange via the antenna32of the communication unit30of the aerial drone12with the communication units60and74of the mobile device14on the one hand and with a central processing unit on the other hand. As stated above, the central computing unit, which is independent of the carrier system10, can also be a smartphone.

In a next step90, the determined measures are performed using the carrier system10in the agricultural field. For example, the aerial drone12flies to the area of the agricultural field to be tilled. The arm48carrying the tool unit52moves to the weed to be removed. The feed unit54displaces the tiller58towards the weed in such a way that the weed will be milled away upon activation of the rotation unit56.

In a fifth step92, the aerial drone12then flies back, and exchanges the mobile device14for another mobile device14optimized for a different action, for example a pesticide or fertilizer applicator.

Alternatively, steps86and88may also be omitted if the aerial drone12is already ready for the action to be performed.

With reference toFIG.5, the determination of the necessary measures by the carrier system10, in particular by the mobile device14, will now be explained in detail.

In a first step94, the continuous recording of data of technically defined voxels and/or pixels and/or images by the visual detection unit62of the mobile device14is performed. The voxels, pixels and images constitute recorded data which is continuously transmitted to the database78—second step96.

In a third step98, the recorded data is stored.

In a fourth step100, a qualitative data comparison of the recorded data with the data stored in the database78is performed. Here, a segmentation and data reduction of the recorded data is carried out by the segmentation and data reduction device66. In particular, verification of the recorded data may also be performed by the second computer76.

In a fifth step102, evaluation is performed by the classifier68in conjunction with the second computer76, supported by artificial intelligence, as will be detailed below.

Finally, in a sixth step104, the processing and conversion of the evaluation by the first computer46into control data for the motor50, the arm48, the tool unit52and the aerial drone12is performed.

Finally, in a seventh step106, the motor50, the arm48, and the tool unit52are started up for tilling the soil or for manipulating flora and fauna.

Where mention is made in this application of artificial intelligence, this relates to, among other things, the use of a classical convolutional neural network—CNN—of one or more convolutional layer(s) followed by a pooling layer. Basically, this sequence of convolutional and pooling layers can be repeated any number of times. Usually, the input is a two- or three-dimensional matrix, e.g. the pixels of a grayscale or color image. The neurons are arranged accordingly in the convolutional layer.

The activity of each neuron is calculated via a discrete convolution (convolutional layer). This involves intuitively moving a comparatively small convolution matrix (filter kernel) step by step over the input. The input of a neuron in the convolutional layer is calculated as the inner product of the filter kernel with the respective presently underlying image section. Accordingly, adjacent neurons in the convolutional layer will react to overlapping areas.

A neuron in this layer responds only to stimuli in a local environment of the previous layer. This follows the biological model of the receptive field. In addition, the weights for all neurons of a convolutional layer are identical (shared weights). This results in each neuron in the first convolutional layer encoding the intensity to which an edge is present in a certain local area of the input, for example. Edge detection as the first step of image recognition has high biological plausibility. It immediately follows from the shared weights that translation invariance is an inherent property of CNNs.

The input of each neuron, determined by discrete convolution, is now transformed by an activation function, for CNNs usually Rectified Linear Unit, or ReLu (f(x)=max(0, x), into the output that is supposed to model the relative firing frequency of a real neuron. Since backpropagation requires the computation of gradients, a differentiable approximation of ReLu is used in practice: f(x)=ln(1+ex). As with the visual cortex, in deeper convolutional layers there is an increase both in the size of the receptive fields and in the complexity of the recognized features.

In the subsequent step, pooling, superfluous information is discarded. For object recognition in images, for example, the exact position of an edge in the image is of negligible interest—the approximate localization of a feature being sufficient. There are different types of pooling. By far the most common type is max-pooling in which of each 2×2 square of neurons in the convolutional layer, only the activity of the most active (hence “max”) neuron is retained for further computational steps; the activity of the remaining neurons is discarded. Despite the data reduction (75% in the example), the performance of the network is usually not reduced by pooling.

The use of the convolutional neural network and the segmentation and data reduction device66is explained in more detail below with reference toFIGS.6through14.

There are various approaches for the classification of all objects in an image by the classifier68. Many approaches start by first finding the individual objects in the image and then classifying them. However, this is not always possible. Let us look at the classification of plants108in a field as an example. An example image108is shown inFIG.6.

FIG.6shows various plants108that are all to be classified by the classifier68, which classification is to be performed in real time. Real time in this case refers to the camera rate of 10 frames per second. Since, as in this example, it is not easy to distinguish where exactly a plant110ends, a different approach must be used, since the computation time is not sufficient to first distinguish the plants110and then to classify them.

The image108shown inFIG.6is made up of pixels, and each pixel can logically contain precisely one class only. Therefore, a trivial approach would be to classify the entire image108pixel by pixel. This means that pixel after pixel is each assigned to a class.

However, since a single pixel does not contain sufficient information to determine which class it belongs to, a surrounding area must be used for the classification. This area can then be classified using a convolutional neural network (CNN) as described above. The network can be of a sequence as illustrated inFIG.7.

The input image110ofFIG.7is the image ofFIG.6. The elements of the CNN are now applied to this input image110. In this example, these elements are a convolution112with the features, subsequent pooling114, another convolution with additional features, another pooling and a consolidation in the dense layer116. The output of the network then indicates which class the center pixel of the input image110, respectively a pixel of the image110ofFIG.6, belongs to.

Subsequently, a new image section, usually an image section that is shifted by one pixel, is selected and classified again using CNN. As a result of this procedure, the calculations required by the convolutional neural network must be repeated for the number of pixels to be classified. This is time consuming. Image110ofFIG.6has a resolution of 2,000×1,000 pixels. Thus, the CNN would have to be computed two million times. However, the initial problem is only the classification of the plants108per se. On average, such an image contains about 5% plant pixels, corresponding to only approx. 100,000 pixels.

By means of simple segmentation and data reduction by the segmentation and data reduction unit66, it can be determined whether a pixel is a representation of a part of a plant108or of a background118. In terms of computation, this segmentation is not as complex as a CNN and therefore faster. The segmentation and data reduction by the segmentation and data reduction unit66is performed in the same way as inFIG.8. The individual steps of this process are shown inFIG.8.

In a first step120, each image of multiple pixels transmitted to the database78is converted to the RGB (red, green, and blue) color model.

In a next step122, each pixel of the transmitted image is converted to an HSV (hue, saturation, value) color model based on the RGB color model.

In a next step124, this HSV color model is evaluated.

Each pixel based on the HSV color model is evaluated with respect to color saturation according to a threshold value, wherein, if the color saturation value exceeds a threshold value, the pixel is assigned the binary value 1, and if the color saturation value falls below a threshold value, the pixel is assigned the binary value 0.

Parallel thereto, based on the HSV color model, each pixel is evaluated with respect to the hue angle based on a predetermined range, wherein, if the hue angle is within the predetermined range, the pixel is assigned the binary value 1, and if the hue angle is outside the range, the pixel is assigned the binary value 0.

In a next step126, the binary hue angle and color saturation information is used to generate an intermediate image that contains significantly less data than the image108generated by the camera.

The segmentation illustrated inFIG.8results in the formula given below, which must be applied to each pixel. The layout of the RGB image ψ(x,y) is used to divide the segmented image S(x,y) into its three components red, green and blue. A pixel of the segmented image is then set to 1 if the minimum value of red, green or blue of a pixel divided by the green pixel is less than or equal to a threshold value (THs). Which threshold value in the 8 bit space of the image is determined by scaling using 255. If the threshold value is not reached, the pixel of the segmented image is set to 0, as in equation 1.

This results in the first optimization: before the entire image108is decomposed into two million images, the segmentation according toFIG.8is used. That is, the entire image108is analyzed pixel by pixel and a decision is made, using the above formula, as to whether or not it is a plant pixel. Firstly, the image108is segmented, that is, the background118is set to black (0), as shown inFIG.9. Secondly, if it is a plant pixel, its coordinates are written into a list. Then, only those coordinates that are also in this list are input into the CNN. The unnecessary pixels of the soil, i.e. the background118, are omitted. Thus, the CNN is called about 20 times fewer.

As a result of the segmentation, the background118is set to the value 0. The image elements that the CNN looks at now also have segmented images. Normally, in a convolution layer, the feature calculation would be applied to each pixel of the image element. However, this results in three cases128,130,132for the calculation, which are shown inFIG.10, each for a feature134of a size of 5×5 pixels.

The Red case128shows a feature calculation in which the feature is completely on the background118. Here, each element is multiplied by 0, which results in the entire calculation being 0, or the bias value. The result of this calculation is therefore already known before the calculation. Even if the background118were non-zero, i.e. contained soil, this calculation would not include any information about the plant110, so the result may simply be a constant fictitious value.

In the Yellow case130, the mean feature value is not on a plant110. This means that part of it is also a multiplication by zero. In this case, the plant110is distorted in the margin and thus made larger in the feature map.

In the Blue case132, at least the center pixel of the feature is on a plant.

After considering these three cases128,130,132, only the Yellow and Blue cases130and132need to be calculated, i.e. the cases130,132in which the feature has at least one non-zero input value. The results of all the other feature computations are known before the computation, they are zero and/or only the bias value. The coordinates in which the Blue case132occurs are known. These are the coordinates stored during the segmentation. For the Yellow case130, a computation must again be made whether this case has occurred. This requires a check of each plant pixel found in the segmentation. Since such a check is too much effort and the Yellow Case130only occurs in the border area of a plant110, this case shall be ignored.

Therefore, the calculation can be optimized in that the feature calculation and all other elements of the CNN are only applied to the plant pixels found.

FIG.11is a schematic view illustrating how two neighboring plant pixels136,138differ from each other. On the left is a plant pixel136and on the right is an adjacent plant pixel138. The Blue/Purple area140,142could be different plants that need to be classified. The Red/Purple area144,142represents the image element that the CNN is looking at in order to classify the orange pixel146.

Closer inspection shows that there is a significant overlap in the region under consideration (Red/Purple)144,142. This in turn means that both image elements136,138contain mostly the same values. If the CNN now calculates the feature in the convolution layer, the same values would also be obtained in the feature calculation.

InFIG.12, a feature148of a size of 5×5 pixels is schematically sketched in green. This feature148is located at the same coordinates within the entire image, but it is displaced within the image element (red/purple)144,142to be viewed by the CNN. However, since its location is the same throughout the image, the calculation for the center black box150would yield the same value in both the left image136and the right image138. This finding can be applied to all elements of a CNN. As a result, provided the edge region is ignored, the single feature calculation can be applied to the entire image first. Theoretically, the decomposition of the input image108only plays a decisive role at the dense layer116level. The dense layer116can, however, be computed in the same way as a convolution112. In this case, the feature size results from the interaction of input image size and the existing pooling layers in the network. This allows the classification to be further optimized; CNN elements are now applied only to the plant pixels found. The feature map calculated from the last convolution represents the classification result as shown inFIG.13. Here, pixel by pixel, all carrot plants152are classified in green and all weeds154are classified in red.

However, these optimizations also cause changes in the classification result. The pooling layers have the greatest influence here. With each pooling, information is removed from the network. However, because the image elements are no longer considered individually, local reference is lost for the pooling. This problem is illustrated inFIG.14.

InFIG.14, a picture element156is shown as a red frame158. Prior to optimization, each pixel would be run individually through the CNN in order to classify its center pixel. The right image element160is shifted one pixel further to the right. The four colors: purple, blue, yellow and green indicate the individual applications of pooling. As can be seen, they can give different results, because pooling always starts at the edge and moves one pooling element further (two fields in this case). This results in two different pooling elements from two adjacent image elements156,160. As a result, if this is to be considered in the optimization, each pooling would result in two new branches for the further calculation. Since the pooling would have to be applied once to the entire image, it [would have to have] its starting point at the top left, and another pooling [would have to have] its starting point at the top left plus one pixel. In the further calculation both pooling results would then have to be processed separately. Another second pooling would result in two new paths again, so that four separate results would have to be calculated. The result is then composed of the four results rotating pixel by pixel. If only one path is considered after pooling, the output image would be smaller after pooling twice. The length and width of the output image would then each be only ¼ as large as the input image. Considering all paths would result in approximately the input image size.

Another difference is represented by the missing edge regions of the plants. Since the features are not applied to all elements in which there is any overlap with the plant, computational differences exist here. This may also change the classification result compared to the conventional calculation.

The missing calculation of the feature values outside the plant can result in other values in that the result is given as zero, which in reality is the bias value, however.

While these three factors do affect the results, this still shows that the CNN is very robust and thus the results still meet a very high accuracy value.

The next step would be to train the network directly with these modifications, so that the network can adapt even better to its new calculation and thus compensate for any errors directly in the calculation.

The segmentation and data reduction device provides the pixels relating to the weed154with position coordinates.

LIST OF REFERENCE SIGNS