Row Crop's Identication Through Hough Transform using Images Segmented by Robust Fuzzy Possibilistic C-Means

The Hough transform (HT) is a widely used method for line detection and recognition, due to its robustness. But its performance is strongly dependent on the applied segmentation technique. On the other hand, Fuzzy C-Means (FCM) has been widely used in image segmentation because it has a good performance in a large class of images. However, it is not good for noisy images, so that to overcome this weakness several modications to FCM have been proposed, like Robust Fuzzy Possibilistic C-Means (RFPCM). In this paper, we propose to use the RFPCM algorithm for the segmentation of crops images in order to apply the HT to detect lines in row crops for navigation purposes. The proposed method gives better results compared with techniques based on visible spectral-index or Specic threshold-based approaches

Multi-feature machine learning model for automatic segmentation of green fractional vegetation cover for high-throughput field phenotyping

Background Accurately segmenting vegetation from the background within digital images is both a fundamental and a challenging task in phenotyping. The performance of traditional methods is satisfactory in homogeneous environments, however, performance decreases when applied to images acquired in dynamic field environments. Results In this paper, a multi-feature learning method is proposed to quantify vegetation growth in outdoor field conditions. The introduced technique is compared with the state-of the-art and other learning methods on digital images. All methods are compared and evaluated with different environmental conditions and the following criteria: (1) comparison with ground-truth images, (2) variation along a day with changes in ambient illumination, (3) comparison with manual measurements and (4) an estimation of performance along the full life cycle of a wheat canopy. Conclusion The method described is capable of coping with the environmental challenges faced in field conditions, with high levels of adaptiveness and without the need for adjusting a threshold for each digital image. The proposed method is also an ideal candidate to process a time series of phenotypic information throughout the crop growth acquired in the field. Moreover, the introduced method has an advantage that it is not limited to growth measurements only but can be applied on other applications such as identifying weeds, diseases, stress, etc

Detección de malezas mediante el análisis de imágenes tomadas desde un vehículo aéreo no tripulado

En este trabajo se propone un método para la detección de malezas en imágenes aéreas de campos agrícolas. Las imágenes fueron obtenidas desde un vehículo aéreo no tripulado con una cámara RGB en un campo de frijol. El objetivo final fue la obtención de un mapa georeferenciado de densidad de malezas a partir de las imágenes obtenidas. El método propuesto consiste en cuatro pasos principales: 1) segmentación de la vegetación, 2) estimación de la orientación media de las filas de cultivos, 3) identificación de las filas de cultivo, y 4) segmentación de las malezas y generación del mapa de densidad de malezas. La detección de malezas se llevó a cabo de manera completamente autónoma, empleando un árbol de decisión como algoritmo de clasificación en la etapa final, pero sin requerir la selección manual de muestras para el entrenamiento. Los resultados obtenidos en la evaluación del desempeño del método propuesto fueron satisfactorios. El modelo de regresión lineal entre las densidades de maleza estimadas y observadas arrojó un coeficiente de determinación de 0.987 y un error cuadrático medio de 0.075. Del ´area total del campo de estudio, se estimó un 84% con menos del 1% de cobertura malezas, lo cual indica un alto potencial para la reducción del volumen de herbicidas aplicados

自然環境下で撮影した作物時系列画像を用いた高速フェノタイピングに関する研究

学位の種別:課程博士University of Tokyo(東京大学

Improvement of soybean breeding via high throughput phenotyping and disease resistance

Development of an Unmanned Aerial Vehicle High Throughput Phenotyping Platform to Improve Soybean Breeding Efficiency Advances in phenotyping technology are critical to ensure the genetic improvement of crops meet future global demands for food and fuel. Field-based phenotyping platforms are being evaluated for their ability to deliver the necessary throughput for large scale experiments and to provide an accurate depiction of trait performance in real-world environments. We developed a dual-camera high throughput phenotyping (HTP) platform on an unmanned aerial vehicle (UAV) and collected time course multispectral images for large scale soybean [Glycine max (L.) Merr.] breeding trials. We used a supervised machine learning model (Random Forest) to measure crop geometric features and obtained high correlations with final yield in breeding populations (r = 0.82). The traditional yield estimation model was significantly improved by incorporating plot row length as covariate (p<0.01). We developed a binary prediction model from time-course multispectral HTP image data and achieved over 93% accuracy in classifying soybean maturity. This prediction model was validated in an independent breeding trial with a different plot type. These results show that multispectral data collected from the UAV-based HTP platform could improve yield estimation accuracy and maturity recording efficiency in a modern soybean breeding program. Impact of Rhg1 Copy Number and Interaction with Rhg4 on Resistance to Heterodera glycines in Soybean Rhg1 and Rhg4 are important loci conferring resistance to soybean cyst nematode (SCN; Heterodera glycines). Alleles at Rhg1 have been shown to vary for copy number and type and the importance of this variation in conferring resistance is not well defined. The repeat number ranges from one to 10 and there are three variant repeat sequence types [PI 88788-'Fayette' type (F), 'Peking' type (P) and Williams 82 type (W)] across diverse soybean germplasm. We developed populations segregating for Rhg1 copy number and type and Rhg4 allele type to investigate the effect of these factors and their interaction on SCN resistance. F2 plants from each cross were evaluated for the segregation of Rhg1 and Rhg4 alleles and for SCN reproduction after infesting plants with HG type 2.5.7 and HG type 7 populations. Within repeat types, an increase in repeat number was associated with greater resistance. The P type Rhg1 showed an advantage over F+W type for SCN population HG type 2.5.7 but this was not observed for SCN HG type 7. While plants with P type Rhg1 required Rhg4 to achieve full resistance, Rhg4 did not increase resistance in the background of F+W type Rhg1 repeat. This study demonstrates the importance of both Rhg1 copy number and type in determining resistance and can assist soybean breeders in determining what alleles would best fit their breeding goals. Fine Mapping of the SCN Resistance QTL cqSCN-006 and cqSCN-007 from Glycine soja PI 468916 The majority of soybean cyst nematode (Heterodera glycines Ichinohe, SCN) resistant cultivars available to growers in the northern USA have resistance originating from PI 88788, which is being overcome by shifting SCN populations. The novel resistance quantitative trait loci (QTL) cqSCN-006 and cqSCN-007 were mapped from Glycine soja Sieb. and Zucc. plant introduction (PI) 468916. The objective of this study was to further narrow down these QTL intervals to improve the effectiveness of marker-assisted selection (MAS) for resistance and to provide resources for cloning these genes. The fine mapping was initiated by screening recombinant plants using simple sequence repeat (SSR) and single nucleotide polymorphism (SNP) markers that flank these QTL. Selected recombinant plants were tested with additional genetic markers saturating the QTL intervals and progeny from the recombinant plants were then tested for resistance in a SCN bioassay. These efforts resulted in the fine mapping of cqSCN-006 into a 212.1 kb interval and cqSCN-007 to a 103.2 kb interval on the Williams 82 reference genome (Glyma.Wm82.a2), which reduced the interval size compared to previous fine mapping by 62% and 30%. One gene located in the cqSCN-006 region was predicted to encode a γ-soluble N-ethylmaleimide–sensitive factor attachment protein (γ-SNAP), which is involved in the same process as α-SNAP, one of the required components in Rhg1 SCN resistance. The identified SSR and SNP markers close to these novel SCN resistance QTL and the candidate gene information presented in this study will be significant resources for MAS and gene cloning research. Fine mapping of the Asian soybean rust resistance gene Rpp2 from soybean PI 230970 Asian soybean rust (ASR), caused by the fungus Phakopsora pachyrhizi Syd. & P. Syd, is a serious disease in major soybean [Glycine max (L.) Merr.] production countries worldwide and causes yield losses up to 75%. Defining the exact chromosomal position of ASR resistance genes is critical for improving the effectiveness of marker-assisted selection (MAS) for resistance and for cloning these genes. The objective of this study was to fine map the ASR resistance gene Rpp2 from the plant introduction (PI) 230970. Rpp2 was previously mapped within a 12.9-cM interval on soybean chromosome 16. The fine mapping was initiated by identifying recombination events in F2 and F3 plants using simple sequence repeat (SSR) and single nucleotide polymorphism (SNP) markers that flank the gene. Seventeen recombinant plants were identified and then tested with additional genetic markers saturating the gene region to localize the positions of each recombination. The progeny of these selected plants were tested for resistance to ASR and with SSR markers resulting in the mapping of Rpp2 to a 188.1 kb interval on the Williams 82 reference genome (Glyma.Wm82.a2). Twelve genes including ten toll/interleukin-1 receptor (TIR) nucleotide-binding site (NBS) leucine-rich repeat (LRR) genes were predicted to exist in this interval on the Glyma.Wm82.a2.v1 gene model map. Eight of these ten genes were homologous to the Arabidopsis TIR-NBS-LRR gene AT5G17680.1. The identified SSR and SNP markers close to Rpp2 and the candidate gene information presented in this study will be significant resources for MAS and gene cloning research