Determination of wheat spike and spikelet architecture and grain traits using X-ray Computed Tomography imaging

Zhou, Hu; Riche, A. B.; Hawkesford, Malcolm J.; Whalley, W. R.; Atkinson, Brian S.; Sturrock, Craig J.; Mooney, Sacha J.

doi:10.1186/s13007-021-00726-5

Cited by 32 publications

(26 citation statements)

References 19 publications

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“…Therefore, maintaining a high and stable wheat yield is essential to improving people’s living standards, maintaining social stability, and promoting the development of the national economy [ 4 ]. The number of wheat ears is an important factor that directly affects wheat yield [ 5 , 6 , 7 ]. Therefore, rapid and accurate identification and statistics of wheat ears are fundamental for crop growth monitoring and yield estimation [ 8 ].…”

Section: Introductionmentioning

confidence: 99%

“…The current application of drones combined with deep learning technology has greatly promoted the development of precision agriculture. In recent years, some meaningful research [ 7 , 8 , 9 , 15 , 27 , 28 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 ] has emerged. These studies have used RGB (red, green, blue), multispectral, hyperspectral, and thermal infrared data acquired by UAV and CNN to evaluate the phenotypic characteristics of citrus crops [ 38 ], obtain key points of plants/plant leaves [ 39 ], plant stress analysis and plant disease identification [ 40 , 41 ].…”

Section: Introductionmentioning

confidence: 99%

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Wheat Ear Recognition Based on RetinaNet and Transfer Learning

Fei

et al. 2021

Sensors

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The number of wheat ears is an essential indicator for wheat production and yield estimation, but accurately obtaining wheat ears requires expensive manual cost and labor time. Meanwhile, the characteristics of wheat ears provide less information, and the color is consistent with the background, which can be challenging to obtain the number of wheat ears required. In this paper, the performance of Faster regions with convolutional neural networks (Faster R-CNN) and RetinaNet to predict the number of wheat ears for wheat at different growth stages under different conditions is investigated. The results show that using the Global WHEAT dataset for recognition, the RetinaNet method, and the Faster R-CNN method achieve an average accuracy of 0.82 and 0.72, with the RetinaNet method obtaining the highest recognition accuracy. Secondly, using the collected image data for recognition, the R2 of RetinaNet and Faster R-CNN after transfer learning is 0.9722 and 0.8702, respectively, indicating that the recognition accuracy of the RetinaNet method is higher on different data sets. We also tested wheat ears at both the filling and maturity stages; our proposed method has proven to be very robust (the R2 is above 90). This study provides technical support and a reference for automatic wheat ear recognition and yield estimation.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Wheat Ear Recognition Based on RetinaNet and Transfer Learning

Fei

et al. 2021

Sensors

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“…In such cases the "virtual shelling” enabled by X-ray technology could largely offset these hurdles. X-ray scanning is also suitable for evaluation of whole panicles (cereals) or pods (legumes) where it can offset the laborious process of grain threshing as shown before [ 20 , 21 , 25 , 34 , 35 , 42 ]. Also X-ray can be used for non-destructive evaluation of inner structures and physical properties of the grains, tubers etc.…”

Section: Discussionmentioning

confidence: 99%

X-ray driven peanut trait estimation: computer vision aided agri-system transformation

et al. 2022

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Background In India, raw peanuts are obtained by aggregators from smallholder farms in the form of whole pods and the price is based on a manual estimation of basic peanut pod and kernel characteristics. These methods of raw produce evaluation are slow and can result in procurement irregularities. The procurement delays combined with the lack of storage facilities lead to fungal contaminations and pose a serious threat to food safety in many regions. To address this gap, we investigated whether X-ray technology could be used for the rapid assessment of the key peanut qualities that are important for price estimation. Results We generated 1752 individual peanut pod 2D X-ray projections using a computed tomography (CT) system (CTportable160.90). Out of these projections we predicted the kernel weight and shell weight, which are important indicators of the produce price. Two methods for the feature prediction were tested: (i) X-ray image transformation (XRT) and (ii) a trained convolutional neural network (CNN). The prediction power of these methods was tested against the gravimetric measurements of kernel weight and shell weight in diverse peanut pod varieties1. Both methods predicted the kernel mass with R2 > 0.93 (XRT: R2 = 0.93 and mean error estimate (MAE) = 0.17, CNN: R2 = 0.95 and MAE = 0.14). While the shell weight was predicted more accurately by CNN (R2 = 0.91, MAE = 0.09) compared to XRT (R2 = 0.78; MAE = 0.08). Conclusion Our study demonstrated that the X-ray based system is a relevant technology option for the estimation of key peanut produce indicators (Figure 1). The obtained results justify further research to adapt the existing X-ray system for the rapid, accurate and objective peanut procurement process. Fast and accurate estimates of produce value are a necessary pre-requisite to avoid post-harvest losses due to fungal contamination and, at the same time, allow the fair payment to farmers. Additionally, the same technology could also assist crop improvement programs in selecting and developing peanut cultivars with enhanced economic value in a high-throughput manner by skipping the shelling of the pods completely. This study demonstrated the technical feasibility of the approach and is a first step to realize a technology-driven peanut production system transformation of the future.

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“…Furthermore, X-ray and CT imaging have been explored to non-destructively measure phenotypic traits of wheat spikes. Both X-ray and CT imaging can measure the inner structures of spikes and acquire information on grains (Duan et al, 2011 ; Xiong et al, 2019a ; Yu et al, 2021 ), which can be further used to distinguish and count the filled spikelets (Zhou et al, 2021 ). As for CT imaging, it can reconstruct spikes in three dimensions as well.…”

Section: Introductionmentioning

confidence: 99%

Automatic Detection and Counting of Wheat Spikelet Using Semi-Automatic Labeling and Deep Learning

Zhang

2022

Front. Plant Sci.

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The number of wheat spikelets is an important phenotypic trait and can be used to assess the grain yield of the wheat crop. However, manual counting of spikelets is time-consuming and labor-intensive. To develop a cost-effective and highly efficient phenotyping system for counting the number of spikelets under laboratory conditions, methods based on imaging processing techniques and deep learning were proposed to accurately detect and count spikelets from color images of wheat spikes captured at the grain filling stage. An unsupervised learning-based method was first developed to automatically detect and label spikelets from spike color images and build the datasets for the model training. Based on the constructed datasets, a deep convolutional neural network model was retrained using transfer learning to detect the spikelets. Testing results showed that the root mean squared errors, relative root mean squared errors, and the coefficients of determination between the automatic and manual counted spikelets for four wheat lines were 0.62, 0.58, 0.54, and 0.77; 3.96, 3.73, 3.34, and 4.94%; and 0.73, 0.78, 0.84, and 0.67, respectively. We demonstrated that the proposed methods can effectively estimate the number of wheat spikelets, which improves the counting efficiency of wheat spikelets and contributes to the analysis of the developmental characteristics of wheat spikes.

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Determination of wheat spike and spikelet architecture and grain traits using X-ray Computed Tomography imaging

Cited by 32 publications

References 19 publications

Wheat Ear Recognition Based on RetinaNet and Transfer Learning

Wheat Ear Recognition Based on RetinaNet and Transfer Learning

X-ray driven peanut trait estimation: computer vision aided agri-system transformation

Automatic Detection and Counting of Wheat Spikelet Using Semi-Automatic Labeling and Deep Learning

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