High-Resolution 3D Crop Reconstruction and Automatic Analysis of Phenotyping Index Using Machine Learning

Yang, Myongkyoon; Cho, Seong‐In

doi:10.3390/agriculture11101010

Cited by 5 publications

(2 citation statements)

References 40 publications

(42 reference statements)

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“…In addition, stereo imaging sensors are increasingly widely used in the field of smart agriculture. The shapes and spatial structures of images can comprehensively show the growth status of crops [ 20 , 21 , 22 ], which has a competitive advantage in crop detection and classification. A forward-looking light field camera named Lytro LF could explore the process of plant growth and characteristics.…”

Section: Related Workmentioning

confidence: 99%

Detection of Green Asparagus Using Improved Mask R-CNN for Automatic Harvesting

Liu

Wang

et al. 2022

Sensors

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Advancements in deep learning and computer vision have led to the discovery of numerous effective solutions to challenging problems in the field of agricultural automation. With the aim to improve the detection precision in the autonomous harvesting process of green asparagus, in this article, we proposed the DA-Mask RCNN model, which utilizes the depth information in the region proposal network. Firstly, the deep residual network and feature pyramid network were combined to form the backbone network. Secondly, the DA-Mask RCNN model added a depth filter to aid the softmax function in anchor classification. Afterwards, the region proposals were further processed by the detection head unit. The training and test images were mainly acquired from different regions in the basin of the Yangtze River. During the capturing process, various weather and illumination conditions were taken into account, including sunny weather, sunny but overshadowed conditions, cloudy weather, and daytime greenhouse conditions as well as nighttime greenhouse conditions. Performance experiments, comparison experiments, and ablation experiments were carried out using the five constructed datasets to verify the effectiveness of the proposed model. Precision, recall, and F1-score values were applied to evaluate the performances of different approaches. The overall experimental results demonstrate that the balance of the precision and speed of the proposed DA-Mask RCNN model outperform those of existing algorithms.

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Section: Related Workmentioning

confidence: 99%

Detection of Green Asparagus Using Improved Mask R-CNN for Automatic Harvesting

Liu

Wang

et al. 2022

Sensors

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show abstract

“…Increasing numbers of such studies are being performed every day due to the capability of ML to solve complex nonlinear problems. The use of ML methods is also common in the agricultural field [14]. Some of the well-known machine learning techniques are artificial neural networks [15], support vector machines [16], and decision trees [17].…”

Section: Introductionmentioning

confidence: 99%

Novel Hybrid Statistical Learning Framework Coupled with Random Forest and Grasshopper Optimization Algorithm to Forecast Pesticide Use on Golf Courses

et al. 2022

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Golf course maintenance requires the use of several inputs, such as pesticides and fertilizers, that can be harmful to human health or the environment. Understanding the factors associated with pesticide use on golf courses may help golf-course managers reduce their reliance on these products. In this study, we used a database of about 14,000 pesticide applications in the province of Québec, Canada, to develop a novel hybrid machine learning approach to predict pesticide use on golf courses. We created this proposed model, called RF-SVM-GOA, by coupling a support vector machine (SVM) with random forest (RF) and the grasshopper optimization algorithm (GOA). We applied RF to handle the wide range of datasets and GOA to find the optimal SVM settings. We considered five different dependent variables—region, golf course ID, number of holes, year, and treated area—as input variables. The experimental results confirmed that the developed hybrid RF-SVM-GOA approach was able to estimate the active ingredient total (AIT) with a high level of accuracy (R = 0.99; MAE = 0.84; RMSE = 0.84; NRMSE = 0.04). We compared the results produced by the developed RF-SVM-GOA model with those of four tree-based techniques including M5P, random tree, reduced error pruning tree (REP tree), and RF, as well as with those of two non-tree-based techniques including the generalized structure of group method of data handling (GSGMDH) and evolutionary polynomial regression (EPR). The computational results showed that the accuracy of the proposed RF-SVM-GOA approach was higher, outperforming the other methods. We analyzed sensitivity to find the most effective variables in AIT forecasting. The results indicated that the treated area is the most effective variable in AIT forecasting. The results of the current study provide a method for increasing the sustainability of golf course management.

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Ultrafast quasi-three-dimensional imaging

Lian

Jiang

Sun

et al. 2023

Int. J. Extrem. Manuf.

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Understanding laser induced ultrafast process with complex three-dimensional (3D) geometrical and extreme property evolution offers unique platform to explore novel physical phenomena and to overcome manufacturing limitations. Ultrafast imaging has been considered as an effective tool due to their exceptional spatiotemporal resolution ability. However, the current imaging techniques with single view imaging actually projecting 3D information on two-dimensional plane, leading to great information loss, and misunderstanding the nature of ultrafast process. Here, we propose a quasi-3D imaging method to describe the 3D ultrafast process and further analyse spatial asymmetric of laser induced plasma. The vertically polarization laser pulses are adopted to illuminate reflection-transmission views, and the binarization techniques are employed to extract contours, forming the corresponding two-dimensional matrix. By rotating and multiplying the two-dimensional contour matrices obtained from the dual views, the quasi-3D image was reconstructed. It successfully unveils dual phase transition mechanisms and elucidates the diffraction phenomena occurring outside the plasma. Furthermore, the quasi-3D image confirms spatial asymmetric of plasma in picosecond plasma, which is hard to acquire in two-dimensional images. Our findings demonstrate that quasi-3D imaging not only offers a more comprehensive understanding of plasma dynamics than previous imaging methods, but also has widespread potential in various ultrafast fields such as strong-field physics, fluid dynamics, and cutting-edge manufacturing.

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High-Resolution 3D Crop Reconstruction and Automatic Analysis of Phenotyping Index Using Machine Learning

Cited by 5 publications

References 40 publications

Detection of Green Asparagus Using Improved Mask R-CNN for Automatic Harvesting

Detection of Green Asparagus Using Improved Mask R-CNN for Automatic Harvesting

Novel Hybrid Statistical Learning Framework Coupled with Random Forest and Grasshopper Optimization Algorithm to Forecast Pesticide Use on Golf Courses

Ultrafast quasi-three-dimensional imaging

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