Automated Counting Grains on the Rice Panicle Based on Deep Learning Method

Deng, Ruoling; Tao, Ming; Huang, Xiaoping; Bangura, Kemoh; Qian, Jiang; Yu, Jiguo; Qi, Long

doi:10.3390/s21010281

Cited by 19 publications

(9 citation statements)

References 22 publications

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“…The use of ImageJ® software makes it possible to compute the number of particles, cells or contrasting elements in the images (Passoni et al, 2014;Gautier and Ginsberg, 2021;Tsuzuki et al, 2021), as well as seeds in a photograph (Mussadiq et al, 2015). Counting rice and wheat grains by means of images was satisfactory and adequate to determine the number of seeds present in a sample, highly correlated with manual counting (Acosta et al, 2017;Deng et al, 2021). Therefore, seed counting by images can be an alternative for the seed analyst and seedling producer.…”

Section: Resultsmentioning

confidence: 99%

“…Moreover, the manual or visual counting of seeds is tiring, repetitive, inefficient and increases the chances of human error, which can be overcome with the use of image processing techniques (Mussadiq et al, 2015;Acosta et al, 2017;Deng et al, 2021). ImageJ® is a free access software with several applications in image analysis in agrarian and biological sciences (Noronha et al, 2019;Freitag et al, 2020;Medeiros et al, 2020;Silva et al, 2020;Oliveira et al, 2021;Ribeiro et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

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Thousand-seed weight determination in forest species by image analysis

et al. 2021

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Thousand-seed weight (TSW) is used to calculate the sowing density, number of seeds present in a sample, and predict seed size. Our aim was to establish an alternative methodology for thousand-seed weight determination in forest species using image analysis techniques. The traditional methodology was compared with the alternative one, testing them on seeds from 16 forest species. The traditional method was performed by manually counting eight repetitions of 100 seeds, weighing of samples, and computation of thousand-seed weight. The alternative methodology was performed by counting seed samples captured by images and processing in ImageJ® software, followed by weighing a single sample, and computation. All steps were timed, and each methodology was repeated ten times per species. The TSW obtained by the alternative methodology was similar to that obtained by the traditional one, and the average execution time of the activities was reduced by 62%. The proposed thousand-seed weight determination by image analysis is an efficient and optimized alternative to the traditional method. Thousand-seed weight determination from image analysis reduces execution time for the seed analyst and forest seedling producer.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Thousand-seed weight determination in forest species by image analysis

et al. 2021

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“…An object detection method which integrates the feature pyramid network (FPN) into the Faster R-CNN network has been successfully used for counting rice spikes [40]. Li et al [41] investigated the performance of Faster R-CNN and RetinaNet in predicting the number of wheat spikes at different growth stages.…”

Section: Crop Organ Detection and Countingmentioning

confidence: 99%

Convolutional Neural Networks in Computer Vision for Grain Crop Phenotyping: A Review

Wang

2022

Agronomy

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Computer vision (CV) combined with a deep convolutional neural network (CNN) has emerged as a reliable analytical method to effectively characterize and quantify high-throughput phenotyping of different grain crops, including rice, wheat, corn, and soybean. In addition to the ability to rapidly obtain information on plant organs and abiotic stresses, and the ability to segment crops from weeds, such techniques have been used to detect pests and plant diseases and to identify grain varieties. The development of corresponding imaging systems to assess the phenotypic parameters, yield, and quality of crop plants will increase the confidence of stakeholders in grain crop cultivation, thereby bringing technical and economic benefits to advanced agriculture. Therefore, this paper provides a comprehensive review of CNNs in computer vision for grain crop phenotyping. It is meaningful to provide a review as a roadmap for future research in such a thriving research area. The CNN models (e.g., VGG, YOLO, and Faster R-CNN) used CV tasks including image classification, object detection, semantic segmentation, and instance segmentation, and the main results of recent studies on crop phenotype detection are discussed and summarized. Additionally, the challenges and future trends of the phenotyping techniques in grain crops are presented.

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“…To assess the performance of the proposed method, we compared it with the current existing state-of-the-art object detection techniques such as Yolo architectures and Faster R-CNN network [ 53 , 54 ]. For the YOLO architecture, we adapted the recently released YOLOv5, which has made significant improvements over its predecessors [ 17 ], while for Faster R-CNN architecture, we used the implementation available in the detectron2 framework for training with our custom dataset [ 18 ].…”

Section: Experimental Workmentioning

confidence: 99%

Automated Extraction of Phenotypic Leaf Traits of Individual Intact Herbarium Leaves from Herbarium Specimen Images Using Deep Learning Based Semantic Segmentation

Malik

Ong

Slik

2021

Sensors

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With the increase in the digitization efforts of herbarium collections worldwide, dataset repositories such as iDigBio and GBIF now have hundreds of thousands of herbarium sheet images ready for exploration. Although this serves as a new source of plant leaves data, herbarium datasets have an inherent challenge to deal with the sheets containing other non-plant objects such as color charts, barcodes, and labels. Even for the plant part itself, a combination of different overlapping, damaged, and intact individual leaves exist together with other plant organs such as stems and fruits, which increases the complexity of leaf trait extraction and analysis. Focusing on segmentation and trait extraction on individual intact herbarium leaves, this study proposes a pipeline consisting of deep learning semantic segmentation model (DeepLabv3+), connected component analysis, and a single-leaf classifier trained on binary images to automate the extraction of an intact individual leaf with phenotypic traits. The proposed method achieved a higher F1-score for both the in-house dataset (96%) and on a publicly available herbarium dataset (93%) compared to object detection-based approaches including Faster R-CNN and YOLOv5. Furthermore, using the proposed approach, the phenotypic measurements extracted from the segmented individual leaves were closer to the ground truth measurements, which suggests the importance of the segmentation process in handling background noise. Compared to the object detection-based approaches, the proposed method showed a promising direction toward an autonomous tool for the extraction of individual leaves together with their trait data directly from herbarium specimen images.

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Automated Counting Grains on the Rice Panicle Based on Deep Learning Method

Cited by 19 publications

References 22 publications

Thousand-seed weight determination in forest species by image analysis

Thousand-seed weight determination in forest species by image analysis

Convolutional Neural Networks in Computer Vision for Grain Crop Phenotyping: A Review

Automated Extraction of Phenotypic Leaf Traits of Individual Intact Herbarium Leaves from Herbarium Specimen Images Using Deep Learning Based Semantic Segmentation

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