Introduction
Carolina geranium (Geranium carolinianum L.) is a problematic broadleaf weed in Florida strawberry [Fragaria×ananassa (Weston) Duchesne ex Rozier (pro sp.) [chiloensis×virginiana]] plasticulture production (Webster Reference Webster2014). Geranium carolinianum emerges from the planting hole in the plastic mulch during crop establishment and escapes current control practices. While the exact cause for escape is unknown, it is likely a consequence of poor residual PRE herbicide control during the long growing season. Clopyralid is a registered POST herbicide that can be safely applied over the top of the strawberry crop (Boyd and Dittmar Reference Boyd and Dittmar2015; Figueroa and Doohan Reference Figueroa and Doohan2006; McMurray et al. Reference McMurray, Monks and Leidy1996; Sharpe et al. Reference Sharpe, Boyd, Dittmar, MacDonald and Darnell2018a). Safety concerns exist for application under higher temperatures that applicators may experience earlier in the production cycle (Sharpe et al. Reference Sharpe, Boyd, Dittmar, MacDonald and Darnell2018b). These concerns can be alleviated by reducing input volumes with custom precision application technology. Specifically in the case of strawberry production, this would involve targeted application of clopyralid to G. carolinianum using machine vision–based applicators. Weed establishment often occupies low percentages of the field and demonstrates a patchy distribution. Site-specific precision application of POST herbicides may reduce inputs by 90% compared with broadcast applications (Zijlstra et al. Reference Zijlstra, Lund, Justesen, Nicolaisen, Jensen, Bianciotto, Posta, Balestrini, Przetakiewicz, Czembor and de Zande2011).
The concept of targeted precision sprayers, particularly autonomous ones, has been well studied, and the general framework has been reviewed elsewhere (Slaughter et al. Reference Slaughter, Giles and Downey2008a). Those authors note the necessity of several subsystems that are important for development in strawberry production: machine vision, weed control, and displacement-sensing subsystems. The machine vision component is the most complex subsystem, with a variety of sensor options for species discrimination. These include cameras to capture multispectral reflectance (Slaughter et al. Reference Slaughter, Giles, Fennimore and Smith2008b), hyperspectral reflectance (Sharpe Reference Sharpe2008; Vrindts et al. Reference Vrindts, Baerdemaeker and Ramon2002), and color images (Tian et al. Reference Tian, Slaughter and Norris1997; Tillett et al. Reference Tillett, Hague, Grundy and Dedousis2008). Use of hyperspectral technology has led to highly accurate (96%) discrimination of weeds in tomatoes (Lycopersicon esculentum Mill.) (Zhang et al. Reference Zhang, Slaughter and Staab2012a, Reference Zhang, Staab, Slaughter, Giles and Downey2012b). Unfortunately, the cost of hyperspectral cameras is high compared with digital color cameras, which limits implementation (Fennimore et al. Reference Fennimore, Slaughter, Siemens, Leon and Saber2016).
One of the most promising prospects for species discrimination is digital color image–based deep convolutional neural networks (CNNs) (Schmidhuber Reference Schmidhuber2015). In 2011, a graphics processing unit (GPU)-implemented neural network using a combination of convolutional and max-pooling layers (GPU-MPCNNs) achieved recognition rates greater than 98.73% on classifying traffic signs (Cireşan et al. Reference Cireşan, Meier and Schmidhuber2011). Forward-fed GPU-MPCNNs became one of the most competition-winning deep-learning techniques employed thereafter (Schmidhuber Reference Schmidhuber2015). The success of neural networks is due to their architecture, derived from the visual perception of animals (Gu et al. Reference Gu, Wang, Kuen, Ma, Shahroudy, Shuai, Liu, Wang, Wang, Cai and Chen2017). Increased computational speed through GPU implementation and altering connectivity and operation of layers has allowed for successful development of deep, multilayered CNNs capable of classifying images through pattern and color recognition (Schmidhuber Reference Schmidhuber2015).
For GPU-enhanced CNNs, successful classification and detection is aided by filter-based pattern recognition at the convolutional layers (Gu et al. Reference Gu, Wang, Kuen, Ma, Shahroudy, Shuai, Liu, Wang, Wang, Cai and Chen2017). Convolutional layers use small, pixel-based filters to both increase the network depth and reduce the dimension of the input images (Szegedy et al. Reference Szegedy, Liu, Jia, Sermanet, Reed, Anguelov, Erhan, Vanhoucke and Rabinovich2014). Pooling layers are an essential element for CNNs, as they reduce the number of connections between convolutional layers and, in turn, reduce computational load (Gu et al. Reference Gu, Wang, Kuen, Ma, Shahroudy, Shuai, Liu, Wang, Wang, Cai and Chen2017). Finally, the output of a layer may contain an activation function, which creates nonlinearity in the network (Teimouri et al. Reference Teimouri, Dyrmann, Nielsen, Mathiassen, Somerville and Jørgensen2018). The most common type is the rectified linear unit, which equates negative outputs to zero, reducing overall computation, and increases identification of sparse targets (Gu et al. Reference Gu, Wang, Kuen, Ma, Shahroudy, Shuai, Liu, Wang, Wang, Cai and Chen2017).
The recent developments for deep CNNs make them a feasible option for using digital color cameras as a cost-effective sensor for machine vision subsystems of precision applicators. A shallow image–classification CNN (10 layers) was able to discriminate soybean [Glycine max (L.) Merr.] plants from three Chinese weeds (Convolvulus sp., Digitaria sp., and Cirsium sp.) with 93% accuracy (Tang et al. Reference Tang, Wang, Zhang, He, Xin and Xu2017). Several weed species (18 in total) were identified with class accuracy between 46% and 78%, with a 70% accuracy of estimating leaf number with the Inception-V3 architecture (Szegedy et al. Reference Szegedy, Vanhoucke, Ioffe, Shlens and Wojna2016; Teimouri et al. Reference Teimouri, Dyrmann, Nielsen, Mathiassen, Somerville and Jørgensen2018). Monocot and dicot weeds were separated from the cereal fields with an average precision of 0.76 using the object-detecting CNN SSD512 (Dyrmann et al. Reference Dyrmann, Skovsen, Laursen and Jørgensen2018; Liu et al. Reference Liu, Anguelov, Erhan, Szegedy, Reed, Fu and Berg2016). Uncategorized weeds could be detected in winter wheat (Triticum aestivum L.) during occluded conditions using the object detector CNN DetectNet with moderate success (precision=87%), but many weeds were missed (recall=46%) (Dyrmann et al. Reference Dyrmann, Jørgensen and Midtiby2017; Tao et al. Reference Tao, Barker and Sarathy2016). Which CNNs may be best adapted for use within in situ smart sprayers in Florida strawberry production is currently unknown. Therefore, the objective of the study was to determine the feasibility of use and accuracy of three CNNs in detecting G. carolinianum growing in competition with strawberry.
Materials and Methods
Images of G. carolinianum in competition with strawberry plants were taken using a digital camera (DSC-HX1, Sony® Cyber-shot Digital Still Camera, Sony, Minato, Tokyo, Japan) at a resolution of 1,920×1,080 pixels. Training images were taken at two locations: the Gulf Coast Research and Education Center in Balm, FL (27.76°N, 82.22°W), and the Florida Strawberry Growers Association field site in Dover, FL (28.02°N, 82.23°W). Validation images were taken at two commercial strawberry farms (27.93°N, 82.10°W and 27.98°N, 82.10°W) on February 23, 2018. Strawberry plants were transplanted on October 10, 2017, in Balm, October 16, 2017, in Dover, and October 6, 2017, and October 10, 2017, for the validation sites. Images were taken at Balm at 63, 70, and 120 d after transplanting (DATr). Images were taken in Dover at 58, 120, and 130 DATr. Validation images were taken at two commercial strawberry farms at 134 and 136 DATr, respectively. Strawberry plants were at early- to mid-anthesis between 58 and 63 DATr, early fruiting by 70 DATr, and full fruiting by 120 DATr. Geranium carolinianum was growing vegetatively between 63 and 120 DATr and had developed reproductive structures after 120 DATr, primarily at validation sites.
The training data set contained a total of 705 images with G. carolinianum (positive) and 570 images without G. carolinianum (negative). The validation data set contained 88 positive and 109 negative images. Negative images were not restricted to only strawberry plants and contained images of other species growing in competition with strawberry within the bed. These species included black medic (Medicago lupulina L.), goosegrass [Eleusine indica (L.) Gaertn.], crabgrass species (Digitaria spp.), livid amaranth (Amaranthus blitum L.), crowfootgrass [Dactyloctenium aegyptium (L.) Willd.], and common ragweed (Ambrosia artemisiifolia L.). Images were taken centered on a strawberry bed and contained approximately four planting holes. Camera height was 130 cm from the soil surface. Bed height was 30.5 cm. Natural sunlight was taken as the light source, with approximately 10% to 20% cloud cover, and images were taken within 2 h of solar noon.
Images were resized to 1,280×720 pixels (720p) using Irfanview (v. 4.50, Irfan Skijan, Jajce, Bosnia). The 720p image resolution was chosen to develop networks for future in situ video input on developed smart sprayers. This resulted in a ground-sampling distance of 0.2 cm pixel−1 at bed height and 0.3 cm pixel−1 at ground height. Image classification images were divided into four equal subimages through cropping, then resorted according to their classification. The resulting data set contained 3,542 negative images and 1,447 positive images.
Data were imported into the NVIDIA Deep Learning GPU Training System (DIGITS) (v. 6.0.0, NVIDIA, Santa Clara, CA) for data set compilation. Three CNNs within the DIGITS library were used for species discrimination. The DIGITS interface was of interest due to its advanced user-friendly interface, graphical layout, and ease of implementation compared with other deep-learning systems. The CNNs can be classified as either image-classification or object-detection based. Image classification uses predetermined groups of images (classes) to train the CNN to select a single classification for each unknown image. Object detection uses input images and label files containing the location of the target (G. carolinianum) in each image. These files were generated by drawing bounding boxes onto positive images using custom software compiled with Lazarus (https://www.lazarus-ide.org). Two DetectNet networks for object detection were trained by either labeling whole G. carolinianum canopies (Figure 1) or labeling individual G. carolinianum leaves (Figure 2).
Figure 1 Canopy-trained DetectNet-generated bounding boxes generated on validation images for Geranium carolinianum growing in competition with the strawberry crop in a plasticulture setting at Plant City, FL, in 2018.
Figure 2 Leaf-trained DetectNet-generated bounding boxes generated on validation images for Geranium carolinianum growing in competition with the strawberry crop in a plasticulture setting at Plant City, FL, in 2018.
The CNN training was conducted in DIGITS using the Convolutional Architecture for Fast Feature Embedding (Caffe) (v. 0.15.14, UC Berkeley EECS, Berkeley, CA) framework to train the image-classification and object-detection neural networks (Tao et al. Reference Tao, Barker and Sarathy2016). The three CNNs used for comparison were the visual geometry groups: 16-layer CNN (VGGNet) (Simonyan and Zisserman Reference Simonyan and Zisserman2015), the 22-layer CNN deep network GoogLeNet (Szegedy et al. Reference Szegedy, Liu, Jia, Sermanet, Reed, Anguelov, Erhan, Vanhoucke and Rabinovich2014), and the DIGITS network DetectNet (Tao et al. Reference Tao, Barker and Sarathy2016). DetectNet contains five parts: (1) data ingest and augmentation layers, (2) a fully convolutional neural network (GoogLeNet), (3) loss functions to measure training error, (4) a clustering function during validation for bounding box prediction, and (5) an average precision (mAP) metric to determine network performance against the validation set (Jia et al. Reference Jia, Shelhamer, Donahue, Karayev, Long, Girshick, Guadarrama and Darrell2014; Szegedy et al. Reference Szegedy, Liu, Jia, Sermanet, Reed, Anguelov, Erhan, Vanhoucke and Rabinovich2014; Tao et al. Reference Tao, Barker and Sarathy2016). All three CNNs were available through the DIGITS model store and were pretrained with the ImageNet data set (Jia et al. Reference Jia, Wei, Socher, Li-Jia, Kai and Li2009); DetectNet was also pretrained with the KITTI data set (Geiger et al. Reference Geiger, Lenz, Stiller and Urtasun2013). Network pretraining facilitates transfer learning for deep CNNs to increase performance (Ge et al. Reference Ge, McCool, Sanderson and Corke2015). These CNNs were selected due to their availability and ease of training through the DIGITS interface. Data augmentation was achieved using the augmentation layer in DetectNet and the mirroring option for VGGNet and GoogLeNet. The DetectNet augmentation layer included parameters for random cropping, flipping, rotating, hue adjustments, and desaturation of input images.
The ADADELTA solver type was selected for backpropagation of all CNNs (Zeiler Reference Zeiler2012). This was to overcome the fundamental problem with deep learning, wherein the activation function signal error during cumulative backpropagation grows or shrinks rapidly (Hochreiter et al. Reference Hochreiter, Bengio, Frasconi and Schmidhuber2001; Schmidhuber Reference Schmidhuber2015). During object-detection training, an intersection over union (IoU) method was used to evaluate whether the object detected was a true positive (tp), with a threshold of 0.7 (Tao et al. Reference Tao, Barker and Sarathy2016). IoU is the ratio of overlap between the actual and predicted bounding boxes. Training continued, with the training rate progressively reduced, until the loss function output no longer decreased or the desired parameters (maP, precision, and recall) ceased to increase over 100 epochs.
Object-detection and image-classification results for G. carolinianum were arranged in a binary-classification confusion matrix under four conditions, a true positive classification (tp), a false negative (fn), a false positive (fp), or a true negative (tn) (Table 1). For object-detection networks, validation images were considered on the networks effectiveness at detecting both individual leaves and whole plants. Two scales of effectiveness are considered: leaves and whole plants. This technology is under development for precision-sprayer implementation to target herbicide application across the strawberry plant to ensure proper coverage of G. carolinianum, so precision on a plant-level scale was considered.
Table 1 Confusion matrix demonstrating the designation terminology for network output in detecting Geranium carolinianum in competition with strawberry in Hillsborough County, FL, in 2018.
Three measures of neural network effectiveness were used: precision, recall, and Fscore (Sokolova and Lapalme Reference Sokolova and Lapalme2009). Precision is a measure of how accurate the neural network was at positive identification and was calculated by:
$$Precision{\equals}{{tp} \over {tp{\plus}fp}}$$
(Hoiem et al. Reference Hoiem, Chodpathumwan and Dai2012; Sokolova and Lapalme Reference Sokolova and Lapalme2009; Tao et al. Reference Tao, Barker and Sarathy2016). Recall is a measure of how effectively the neural network identified the target and was calculated by:
$$Recall{\equals}{{tp} \over {tp{\plus}fn}}$$
(Hoiem et al. Reference Hoiem, Chodpathumwan and Dai2012; Sokolova and Lapalme Reference Sokolova and Lapalme2009; Tao et al. Reference Tao, Barker and Sarathy2016). The Fscore is the harmonic mean of the recall and precision, gives an overall impression of the network’s positive labels, and was calculated by:
$$Fscore{\equals}{{2\,{\rm {\asterisk}}\,Precision\,{\rm {\asterisk}}\,Recall} \over {Precision{\plus}Recall}}$$
(Sokolova and Lapalme Reference Sokolova and Lapalme2009). For validation, if an image would not converge to produce bounding boxes on visible G. carolinianum, all plants present were marked as a miss (0% precision and recall).
Results and Discussion
Image Classification
GoogLeNet performed better than VGGNet for whole-image training (Table 2). GoogLeNet did not increase either precision or recall with cropped-image training. This contrasted with VGGNet, which demonstrated substantial gains in fit for cropped-image training. Validation for both GoogLeNet and VGGNet developed on whole images were not successful, and produced low values for recall and Fscores, making them unsuitable for field applications (Table 3). These networks were unable to identify most to all of the G. carolinianum present in the images, again making them unsuitable for field applications. Cropped-image training increased the validation results for both GoogLeNet and VGGNet (Table 3). VGGNet made substantial gains in detecting G. carolinianum, particularly in rejecting false positives, though the network did still struggle with missing the target (false negatives).
Table 2 Detection-training results using various convolutional neural networks (CNNs) for Geranium carolinianum growing in competition with strawberry in Balm, FL, in 2018.
a Fscore=the harmonic mean of the precision and recall.
b IC, image classification–based CNN; OD, object detection–based CNN.
Table 3 Detection validation using various convolutional neural networks (CNNs) for Geranium carolinianum growing in competition with strawberry in Balm, FL, in 2018.
a IC, image classification–based CNN; OD, object detection–based CNN.
b Fscore = the harmonic mean of the precision and recall.
c N/C, noncalculable value, no positive or false positives were generated by the neural network.
Recall was primarily the limiting factor for image classification of G. carolinianum while in competition with strawberry. This was likely due to the frame of the input image and the amount of negative space within (Figure 1). The camera field of view contained between three to four planting holes along the bed, and G. carolinianum occupied one to two planting holes. The number of G. carolinianum leaves that grew through the strawberry canopy and were visible from above were variable, ranging from 4 to more than 30.
Cropping images into four subimages substantially increased the validation Fscore of the networks for classifying images containing G. carolinianum. Improvements in the Fscore were likely due to three factors: (1) the cropped images increased the number of available training images to parameterize the CNN; (2) the overall image contained fewer pixels to analyze; and (3) due to cropping, the overall “negative” area outside the target (G. carolinianum) was reduced. The latter two points are likely important, as they increase the percentage of the image that has a positive pattern recognition potential to be amplified by the convolutional filters. The additional layers to facilitate object detection further benefited this by reducing the negative space in which classification occurs.
Object Detection
Both the leaf-trained and canopy-trained DetectNet CNNs outperformed GoogLeNet and VGGNet CNNs in both training (Table 2) and validation (Table 3). Both the canopy-trained and leaf-trained DetectNet networks fit the data very well. Canopy-based training produced a network that was precise at detecting plants at the cost of recall. As expected, canopy-based training missed more plants and many more leaves than the leaf-based training, but given the overall larger training target, made fewer false identifications. The canopy-trained CNN did produce larger bounding boxes that encompassed the strawberry leaves. Due to how precision was considered, this did not have a negative effect on the calculated accuracy, but depending on the application, this output may be undesirable.
The leaf-trained DetectNet was the best evaluated network for potential precision-sprayer implementation. The network identified all target plants, 142 in total. The smaller target had led to increased identification error (false positives), 63% of which occurred on weed species not prominent in the training data set, and particularly M. lupulina leaf clusters (Figure 3). Other false positives included strawberry flowers, immature fruit, and leaf edges. False-positive network desensitization is possible by adding false positive–rich training images for further training. To demonstrate this, an additional 309 images containing M. lupulina were taken during the same time periods as the training set and used to supplement the training data. The network was retrained and successfully desensitized to M. lupulina such that false positives no longer occurred.
Figure 3 Leaf-trained DetectNet-generated bounding boxes generated on validation images for Geranium carolinianum growing in competition with the strawberry crop at Plant City, FL in 2018. This figure demonstrates false-positive identifications.
The strawberry field used to collect validation images contained a G. carolinianum morphological variant that differed from plants in the training fields. Plants of this variant contained a more open palmate leaf compared with the more closed palmate leaf predominant in the training fields. Observed morphological variation was unexpected, but the CNN detection performance remained high, making results promising for across-site applications. Missed leaves were primarily at the edges of larger plants that were oriented toward the row middle. A combination of unusual leaf orientation and background noise of the row middle likely led to missed identification; this may be remedied with selective further training. Even so, these larger plants are not necessarily the optimal target for clopyralid application, and sprays should occur on smaller plants, so this limitation may have minimal consequence for implementation. Using a CNN trained to detect individual leaves within the strawberry canopy would increase detection of smaller plants protruding through the canopy.
The leaf-trained DetectNet outperformed the canopy-trained DetectNet in detecting leaves during validation (Table 3). Leaf detection produced the greatest Fscore for any CNN with nearly perfect precision. Similar results, namely limited recall, were observed for DetectNet trained to detect weeds in a wheat field (Dyrmann et al. Reference Dyrmann, Jørgensen and Midtiby2017). The authors had not differentiated any particular weed species or growth stage, which increased target variability and reduced network recall, along with target occlusion by the crop. A similar study using SSD512 resulted in a similarly trained network with limited recall (Dyrmann et al. Reference Dyrmann, Skovsen, Laursen and Jørgensen2018), a consequence of target selection and the necessity of a larger training data set to compensate. For weed science applications, especially scenarios where the available image quantity may be low, target selection is an important consideration. It is advisable to select a target with widespread application and reduced variability, such as a leaf.
Other object-detection networks such as SSD (Liu et al. Reference Liu, Anguelov, Erhan, Szegedy, Reed, Fu and Berg2016) and You Only Look Once (Redmon et al. Reference Redmon, Divvala, Girshick and Farhadi2016) are designed to identify multiple classes per image. The ability to identify several classes is an important network quality for weed management strategies including scouting or concurrently spraying multiple herbicides. Similar target-dependent recall shortcomings have been identified for SSD (Dyrmann et al. Reference Dyrmann, Skovsen, Laursen and Jørgensen2018). Success may further be limited due to the use of an IoU and rectangular bounding boxes for object-detection CNNs. Geranium carolinianum was an excellent test species, because leaves were fairly symmetrical and round. The variability in the target was minimal across varying angles and orientations, which led to a minimum of background noise within the bounding box. Application to Cyperaceae or Poaceae species may be difficult to implement using bounding-box techniques, due to leaf shape and potential orientations. Alternatively, image-segmentation CNNs may be a better approach for such circumstances (Milioto et al. Reference Milioto, Lottes and Stachniss2017).
In conclusion, results demonstrate a promising avenue for detection of a broadleaf species in a broadleaf crop. A leaf-trained DetectNet CNN achieved a high Fscore (0.94) in detecting G. carolinianum plants within the strawberry crop. Use of DetectNet as the decision system for a digital camera–based machine vision subsystem appears a viable option for precision control of G. carolinianum in Florida strawberry production. Future research is required to test DetectNet and other object-detection networks in situ coordinated with the proper G. carolinianum growth stage for ideal control with clopyralid. This would reduce chemical inputs and exposure, thus alleviating concerns regarding temperature-induced damage for earlier applications (Sharpe et al. Reference Sharpe, Boyd, Dittmar, MacDonald and Darnell2018a, Reference Sharpe, Boyd, Dittmar, MacDonald and Darnell2018b).
Acknowledgments
No conflicts of interest have been declared. This research received no specific grant from any funding agency or the commercial or not-for-profit sectors.
