Weed Identification in Maize, Sunflower, and Potatoes with the Aid of Convolutional Neural Networks

Peteinatos, Gerassimos G.; Reichel, Philipp; Karouta, Jeremy; Andújar, Dionisio; Gerhards, Roland

doi:10.3390/rs12244185

Cited by 60 publications

(75 citation statements)

References 52 publications

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“…Thus, differences should be taken into account when comparing model performance with other studies. In general, the optimized image-classifier of this study performed with 94% overall classification accuracy, well in the range of studies aiming for classifying mixed weed plants [33][34][35]48,49]. In comparison with Pflanz et al [11], a higher overall accuracy could be obtained on the same data set.…”

Section: Discussionsupporting

confidence: 58%

“…It can be assumed that a further gain in speed will be achieved when shifting entirely to integer-based computation on the embedded board [45], which was not tested in this study. Area performance could also be increased with higher camera resolution to become more practical, as Peteinatos et al [35] pointed out. However, another approach to enhance area performance could be sparse mapping.…”

Section: Discussionmentioning

confidence: 99%

“…They concluded that one advantage of DCNNs is their independence in the choice of an appropriate feature extractor. More recently, Peteintos et al [35] tested three different DCNN architectures, including VGG16 [36], Inception, and ResNet-50, for the classification of weeds in maize, sunflower, and potato crops with images taken from a ground-based vehicle, in which the VGG16 was outperformed by the other two DCNNs. They also concluded that data sets for weed classification by DCNNs needs to be more robust, usable, and diverse.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Optimized Deep Learning Model as a Basis for Fast UAV Mapping of Weed Species in Winter Wheat Crops

et al. 2021

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Weed maps should be available quickly, reliably, and with high detail to be useful for site-specific management in crop protection and to promote more sustainable agriculture by reducing pesticide use. Here, the optimization of a deep residual convolutional neural network (ResNet-18) for the classification of weed and crop plants in UAV imagery is proposed. The target was to reach sufficient performance on an embedded system by maintaining the same features of the ResNet-18 model as a basis for fast UAV mapping. This would enable online recognition and subsequent mapping of weeds during UAV flying operation. Optimization was achieved mainly by avoiding redundant computations that arise when a classification model is applied on overlapping tiles in a larger input image. The model was trained and tested with imagery obtained from a UAV flight campaign at low altitude over a winter wheat field, and classification was performed on species level with the weed species Matricaria chamomilla L., Papaver rhoeas L., Veronica hederifolia L., and Viola arvensis ssp. arvensis observed in that field. The ResNet-18 model with the optimized image-level prediction pipeline reached a performance of 2.2 frames per second with an NVIDIA Jetson AGX Xavier on the full resolution UAV image, which would amount to about 1.78 ha h−1 area output for continuous field mapping. The overall accuracy for determining crop, soil, and weed species was 94%. There were some limitations in the detection of species unknown to the model. When shifting from 16-bit to 32-bit model precision, no improvement in classification accuracy was observed, but a strong decline in speed performance, especially when a higher number of filters was used in the ResNet-18 model. Future work should be directed towards the integration of the mapping process on UAV platforms, guiding UAVs autonomously for mapping purpose, and ensuring the transferability of the models to other crop fields.

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Section: Discussionsupporting

confidence: 58%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Optimized Deep Learning Model as a Basis for Fast UAV Mapping of Weed Species in Winter Wheat Crops

et al. 2021

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“…CNN frameworks, such as AlexNet [ 19 ], ResNet [ 131 , 132 ], VGG [ 133 ], Google [ 134 ], U-Net, MobileNets, and DenseNet [ 135 ], are also widely used in weed detection. These methods stand out from other conventional index-based methods.…”

Section: Weed Detection and Identification Methods Based On Deep Learningmentioning

confidence: 99%

Review of Weed Detection Methods Based on Computer Vision

Chen

Zhao

et al. 2021

Sensors

171

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Weeds are one of the most important factors affecting agricultural production. The waste and pollution of farmland ecological environment caused by full-coverage chemical herbicide spraying are becoming increasingly evident. With the continuous improvement in the agricultural production level, accurately distinguishing crops from weeds and achieving precise spraying only for weeds are important. However, precise spraying depends on accurately identifying and locating weeds and crops. In recent years, some scholars have used various computer vision methods to achieve this purpose. This review elaborates the two aspects of using traditional image-processing methods and deep learning-based methods to solve weed detection problems. It provides an overview of various methods for weed detection in recent years, analyzes the advantages and disadvantages of existing methods, and introduces several related plant leaves, weed datasets, and weeding machinery. Lastly, the problems and difficulties of the existing weed detection methods are analyzed, and the development trend of future research is prospected.

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“…When given sufficient data, the deep learning algorithm can generate and extrapolate new features without having to be explicitly told which features should be utilized and how they can be extracted [12][13][14]. CNNs (convolutional neural networks) are another variety of algorithm belonging to deep learning technology, which can provide insights into image-related datasets that we have not yet understood, achieving identification accuracies that sometimes surpass the human-level performance [15][16][17]. One of the most important characteristics of utilizing CNNs in object detection is that the CNN can obtain essential features by itself.…”

Section: Introductionmentioning

confidence: 99%

A Real-Time Apple Targets Detection Method for Picking Robot Based on Improved YOLOv5

et al. 2021

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The apple target recognition algorithm is one of the core technologies of the apple picking robot. However, most of the existing apple detection algorithms cannot distinguish between the apples that are occluded by tree branches and occluded by other apples. The apples, grasping end-effector and mechanical picking arm of the robot are very likely to be damaged if the algorithm is directly applied to the picking robot. Based on this practical problem, in order to automatically recognize the graspable and ungraspable apples in an apple tree image, a light-weight apple targets detection method was proposed for picking robot using improved YOLOv5s. Firstly, BottleneckCSP module was improved designed to BottleneckCSP-2 module which was used to replace the BottleneckCSP module in backbone architecture of original YOLOv5s network. Secondly, SE module, which belonged to the visual attention mechanism network, was inserted to the proposed improved backbone network. Thirdly, the bonding fusion mode of feature maps, which were inputs to the target detection layer of medium size in the original YOLOv5s network, were improved. Finally, the initial anchor box size of the original network was improved. The experimental results indicated that the graspable apples, which were unoccluded or only occluded by tree leaves, and the ungraspable apples, which were occluded by tree branches or occluded by other fruits, could be identified effectively using the proposed improved network model in this study. Specifically, the recognition recall, precision, mAP and F1 were 91.48%, 83.83%, 86.75% and 87.49%, respectively. The average recognition time was 0.015 s per image. Contrasted with original YOLOv5s, YOLOv3, YOLOv4 and EfficientDet-D0 model, the mAP of the proposed improved YOLOv5s model increased by 5.05%, 14.95%, 4.74% and 6.75% respectively, the size of the model compressed by 9.29%, 94.6%, 94.8% and 15.3% respectively. The average recognition speeds per image of the proposed improved YOLOv5s model were 2.53, 1.13 and 3.53 times of EfficientDet-D0, YOLOv4 and YOLOv3 and model, respectively. The proposed method can provide technical support for the real-time accurate detection of multiple fruit targets for the apple picking robot.

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Weed Identification in Maize, Sunflower, and Potatoes with the Aid of Convolutional Neural Networks

Cited by 60 publications

References 52 publications

Optimized Deep Learning Model as a Basis for Fast UAV Mapping of Weed Species in Winter Wheat Crops

Optimized Deep Learning Model as a Basis for Fast UAV Mapping of Weed Species in Winter Wheat Crops

Review of Weed Detection Methods Based on Computer Vision

A Real-Time Apple Targets Detection Method for Picking Robot Based on Improved YOLOv5

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