A Convolutional Neural Network-Based Method for Corn Stand Counting in the Field

Wang, Le; Xiang, Lirong; Tang, Lie; Jiang, Huanyu

doi:10.3390/s21020507

Cited by 32 publications

(28 citation statements)

References 52 publications

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“…In this work, the combination of laser data with crop sensors’ data was used to detect plant stress and to map the vegetative state of plants. In [ 16 ], the authors proposed a deep learning-based method for counting corn stands in agricultural fields. A handheld platform was used to mount and test the hardware and software pipeline, which the authors claim that can be easily mounted on carts, tractors or field robotic systems.…”

Section: Introductionmentioning

confidence: 99%

Visible and Thermal Image-Based Trunk Detection with Deep Learning for Forestry Mobile Robotics

2021

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Mobile robotics in forests is currently a hugely important topic due to the recurring appearance of forest wildfires. Thus, in-site management of forest inventory and biomass is required. To tackle this issue, this work presents a study on detection at the ground level of forest tree trunks in visible and thermal images using deep learning-based object detection methods. For this purpose, a forestry dataset composed of 2895 images was built and made publicly available. Using this dataset, five models were trained and benchmarked to detect the tree trunks. The selected models were SSD MobileNetV2, SSD Inception-v2, SSD ResNet50, SSDLite MobileDet and YOLOv4 Tiny. Promising results were obtained; for instance, YOLOv4 Tiny was the best model that achieved the highest AP (90%) and F1 score (89%). The inference time was also evaluated, for these models, on CPU and GPU. The results showed that YOLOv4 Tiny was the fastest detector running on GPU (8 ms). This work will enhance the development of vision perception systems for smarter forestry robots.

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Section: Introductionmentioning

confidence: 99%

Visible and Thermal Image-Based Trunk Detection with Deep Learning for Forestry Mobile Robotics

2021

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“…The overlap ratio is the ratio of two overlaps between the prediction frames [18,19]. It is an important indicator affecting how many prediction frames are available, which was reflected in the sorghum head detection performance.…”

Section: Designing the Experimentsmentioning

confidence: 99%

“…To circumvent this, most of the current crop plant counting studies are based on RGB and multispectral sensors [5,12,[15][16][17][18], of which RGB sensors have a lower cost and higher spatial resolution. They also meet the requirements of crop plant counting using computer vision [4,8,[19][20][21][22], which mainly uses some computer algorithms to simulate human visual functions to extract feature information from images, process, understand, and finally achieve counting of crop targets.…”

Section: Introductionmentioning

confidence: 99%

Comparison of Deep Learning Methods for Detecting and Counting Sorghum Heads in UAV Imagery

Wang

Huang

2022

Remote Sensing

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With the rapid development of remote sensing with small, lightweight unmanned aerial vehicles (UAV), efficient and accurate crop spike counting, and yield estimation methods based on deep learning (DL) methods have begun to emerge, greatly reducing labor costs and enabling fast and accurate counting of sorghum spikes. However, there has not been a systematic, comprehensive evaluation of their applicability in cereal crop spike identification in UAV images, especially in sorghum head counting. To this end, this paper conducts a comparative study of the performance of three common DL algorithms, EfficientDet, Single Shot MultiBox Detector (SSD), and You Only Look Once (YOLOv4), for sorghum head detection based on lightweight UAV remote sensing data. The paper explores the effects of overlap ratio, confidence, and intersection over union (IoU) parameters, using the evaluation metrics of precision P, recall R, average precision AP, F1 score, computational efficiency, and the number of detected positive/negative samples (Objects detected consistent/inconsistent with real samples). The experiment results show the following. (1) The detection results of the three methods under dense coverage conditions were better than those under medium and sparse conditions. YOLOv4 had the most accurate detection under different coverage conditions; on the contrary, EfficientDet was the worst. While SSD obtained better detection results under dense conditions, the number of over-detections was larger. (2) It was concluded that although EfficientDet had a good positive sample detection rate, it detected the fewest samples, had the smallest R and F1, and its actual precision was poor, while its training time, although medium, had the lowest detection efficiency, and the detection time per image was 2.82-times that of SSD. SSD had medium values for P, AP, and the number of detected samples, but had the highest training and detection efficiency. YOLOv4 detected the largest number of positive samples, and its values for R, AP, and F1 were the highest among the three methods. Although the training time was the slowest, the detection efficiency was better than EfficientDet. (3) With an increase in the overlap ratios, both positive and negative samples tended to increase, and when the threshold value was 0.3, all three methods had better detection results. With an increase in the confidence value, the number of positive and negative samples significantly decreased, and when the threshold value was 0.3, it balanced the numbers for sample detection and detection accuracy. An increase in IoU was accompanied by a gradual decrease in the number of positive samples and a gradual increase in the number of negative samples. When the threshold value was 0.3, better detection was achieved. The research findings can provide a methodological basis for accurately detecting and counting sorghum heads using UAV.

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“…As an example, the orchard’s robot navigation can be introduced, where the mobile robot needs to detect the path autonomously (e.g., defined as the position in the middle of the row) to accomplish their task (recollection, spraying, crops stand counting, plant phenotyping, etc.) [ 3 , 4 , 5 , 6 ]. In agricultural applications, the path detection and navigation strongly depend on the robotic task goals.…”

Section: Introductionmentioning

confidence: 99%

“…Obstacle detection systems can include a large number of sensors and techniques ranging from ultrasonic sensors and lidars to more complex solutions relying on computer vision systems. Due to their flexibility, cameras are universal and widely applicable in tasks where a complex perception system is required [1,5,6]. In this context, physical camera characteristics, such as resolution, field of view, etc., are only part of the overall computer vision system properties.…”

Section: Introductionmentioning

confidence: 99%

Obstacle Detection System for Agricultural Mobile Robot Application Using RGB-D Cameras

Skoczeń

Ochman

Spyra³

et al. 2021

Sensors

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Mobile robots designed for agricultural tasks need to deal with challenging outdoor unstructured environments that usually have dynamic and static obstacles. This assumption significantly limits the number of mapping, path planning, and navigation algorithms to be used in this application. As a representative case, the autonomous lawn mowing robot considered in this work is required to determine the working area and to detect obstacles simultaneously, which is a key feature for its working efficiency and safety. In this context, RGB-D cameras are the optimal solution, providing a scene image including depth data with a compromise between precision and sensor cost. For this reason, the obstacle detection effectiveness and precision depend significantly on the sensors used, and the information processing approach has an impact on the avoidance performance. The study presented in this work aims to determine the obstacle mapping accuracy considering both hardware- and information processing-related uncertainties. The proposed evaluation is based on artificial and real data to compute the accuracy-related performance metrics. The results show that the proposed image and depth data processing pipeline introduces an additional distortion of 38 cm.

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A Convolutional Neural Network-Based Method for Corn Stand Counting in the Field

Cited by 32 publications

References 52 publications

Visible and Thermal Image-Based Trunk Detection with Deep Learning for Forestry Mobile Robotics

Visible and Thermal Image-Based Trunk Detection with Deep Learning for Forestry Mobile Robotics

Comparison of Deep Learning Methods for Detecting and Counting Sorghum Heads in UAV Imagery

Obstacle Detection System for Agricultural Mobile Robot Application Using RGB-D Cameras

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