Accurately detecting and counting fruits during plant growth using imaging and computer vision is of importance not only from the point of view of reducing labor intensive manual measurements of phenotypic information, but also because it is a critical step toward automating processes such as harvesting. Deep learning based methods have emerged as the state-of-the-art techniques in many problems in image segmentation and classification, and have a lot of promise in challenging domains such as agriculture, where they can deal with the large variability in data better than classical computer vision methods. This paper reports results on the detection of tomatoes in images taken in a greenhouse, using the MaskRCNN algorithm, which detects objects and also the pixels corresponding to each object. Our experimental results on the detection of tomatoes from images taken in greenhouses using a RealSense camera are comparable to or better than the metrics reported by earlier work, even though those were obtained in laboratory conditions or using higher resolution images. Our results also show that MaskRCNN can implicitly learn object depth, which is necessary for background elimination.

/pdf/tomato-fruit-detection-and-counting-in-greenhouses-using-4l6uvn0s5a.pdf

Tomato Fruit Detection and Counting in Greenhouses Using Deep Learning.

Multiple comparisons among genomes can clarify their evolution, speciation, and functional innovations. To date, the genome sequences of eight grasses representing the most economically important Poaceae (grass) clades have been published, and their genomic-level comparison is an essential foundation for evolutionary, functional, and translational research. Using a formal and conservative approach, we aligned these genomes. Direct comparison of paralogous gene pairs all duplicated simultaneously reveal striking variation in evolutionary rates among whole genomes, with nucleotide substitution slowest in rice and up to 48% faster in other grasses, adding a new dimension to the value of rice as a grass model. We reconstructed ancestral genome contents for major evolutionary nodes, potentially contributing to understanding the divergence and speciation of grasses. Recent fossil evidence suggests revisions of the estimated dates of key evolutionary events, implying that the pan-grass polyploidization occurred ∼96 million years ago and could not be related to the Cretaceous-Tertiary mass extinction as previously inferred. Adjusted dating to reflect both updated fossil evidence and lineage-specific evolutionary rates suggested that maize subgenome divergence and maize-sorghum divergence were virtually simultaneous, a coincidence that would be explained if polyploidization directly contributed to speciation. This work lays a solid foundation for Poaceae translational genomics.

Genome Alignment Spanning Major Poaceae Lineages Reveals Heterogeneous Evolutionary Rates and Alters Inferred Dates for Key Evolutionary Events

Fine mapping QTLs and identifying candidate genes for cotton fibre-quality and yield traits would be beneficial to cotton breeding. Here, we constructed a high-density genetic map by specific-locus amplified fragment sequencing (SLAF-seq) to identify QTLs associated with fibre-quality and yield traits using 239 recombinant inbred lines (RILs), which was developed from LMY22 (a high-yield Gossypium hirsutumL. cultivar) × LY343 (a superior fibre-quality germplasm with G. barbadenseL. introgressions). The genetic map spanned 3426.57 cM, including 3556 SLAF-based SNPs and 199 SSR marker loci. A total of 104 QTLs, including 67 QTLs for fibre quality and 37 QTLs for yield traits, were identified with phenotypic data collected from 7 environments. Among these, 66 QTLs were co-located in 19 QTL clusters on 12 chromosomes, and 24 QTLs were detected in three or more environments and determined to be stable. We also investigated the genomic components of LY343 and their contributions to fibre-related traits by deep sequencing the whole genome of LY343, and we found that genomic components from G. hirsutum races (which entered LY343 via its G. barbadense parent) contributed more favourable alleles than those from G. barbadense. We further identified six putative candidate genes for stable QTLs, including Gh_A03G1147 (GhPEL6), Gh_D07G1598 (GhCSLC6) and Gh_D13G1921 (GhTBL5) for fibre-length QTLs and Gh_D03G0919 (GhCOBL4), Gh_D09G1659 (GhMYB4) and Gh_D09G1690 (GhMYB85) for lint-percentage QTLs. Our results provide comprehensive insight into the genetic basis of the formation of fibre-related traits and would be helpful for cloning fibre-development-related genes as well as for marker-assisted genetic improvement in cotton.

https://onlinelibrary.wiley.com/doi/pdf/10.1111/pbi.13237

Identification of candidate genes for key fibre‐related QTLs and derivation of favourable alleles in Gossypium hirsutum recombinant inbred lines with G. barbadense introgressions

Ear density is one of the most important agronomical yield components in wheat. Ear counting is time-consuming and tedious as it is most often conducted manually in field conditions. Moreover, different sampling techniques are often used resulting in a lack of standard protocol, which may eventually affect inter-comparability of results. Thermal sensors capture crop canopy features with more contrast than RGB sensors for image segmentation and classification tasks. An automatic thermal ear counting system is proposed to count the number of ears using zenithal/nadir thermal images acquired from a moderately high resolution handheld thermal camera. Three experimental sites under different growing conditions in Spain were used on a set of 24 varieties of durum wheat for this study. The automatic pipeline system developed uses contrast enhancement and filter techniques to segment image regions detected as ears. The approach is based on the temperature differential between the ears and the rest of the canopy, given that ears usually have higher temperatures due to their lower transpiration rates. Thermal images were acquired, together with RGB images and in situ (i.e., directly in the plot) visual ear counting from the same plot segment for validation purposes. The relationship between the thermal counting values and the in situ visual counting was fairly weak (R2 = 0.40), which highlights the difficulties in estimating ear density from one single image-perspective. However, the results show that the automatic thermal ear counting system performed quite well in counting the ears that do appear in the thermal images, exhibiting high correlations with the manual image-based counts from both thermal and RGB images in the sub-plot validation ring (R2 = 0.75–0.84). Automatic ear counting also exhibited high correlation with the manual counting from thermal images when considering the complete image (R2 = 0.80). The results also show a high correlation between the thermal and the RGB manual counting using the validation ring (R2 = 0.83). Methodological requirements and potential limitations of the technique are discussed.

/pdf/automatic-wheat-ear-counting-using-thermal-imagery-3o9a90ukil.pdf

Automatic wheat ear counting using thermal imagery.

In current practice, broccoli heads are selectively harvested by hand. The goal of our work is to develop a robot that can selectively harvest broccoli heads, thereby reducing labor costs. An essential element of such a robot is an image-processing algorithm that can detect broccoli heads. In this study, we developed a deep learning algorithm for this purpose, using the Mask Region-based Convolutional Neural Network. To be applied on a robot, the algorithm must detect broccoli heads from any cultivar, meaning that it can generalize on the broccoli images. We hypothesized that our algorithm can be generalized through network simplification and data augmentation. We found that network simplification decreased the generalization performance, whereas data augmentation increased the generalization performance. In data augmentation, the geometric transformations (rotation, cropping, and scaling) led to a better image generalization than the photometric transformations (light, color, and texture). Furthermore, the algorithm was generalized on a broccoli cultivar when 5% of the training images were images of that cultivar. Our algorithm detected 229 of the 232 harvestable broccoli heads from three cultivars. We also tested our algorithm on an online broccoli data set, which our algorithm was not previously trained on. On this data set, our algorithm detected 175 of the 176 harvestable broccoli heads, proving that the algorithm was successfully generalized. Finally, we performed a cost-benefit analysis for a robot equipped with our algorithm. We concluded that the robot was more profitable than the human harvest and that our algorithm provided a sufficient basis for robot commercialization.

/pdf/the-effect-of-data-augmentation-and-network-simplification-4n4huzo5hn.pdf

The effect of data augmentation and network simplification on the image-based detection of broccoli heads with Mask R-CNN

We describe a genetic map with a total of 381 bins of 616 genotyping by sequencing (GBS)-based SNP markers in a F6-F8 recombinant inbred line (RIL) population of 393 individuals derived from crossing S. bicolor BTx623 to S. bicolor IS3620C, a guinea line substantially diverged from BTx623. Five segregation distorted regions were found with four showing enrichment for S. bicolor alleles, suggesting possible selection during formation of this RIL population. A quantitative trait locus (QTL) study with this number of individuals, tripled relative to prior studies of this cross, provided resources, validated previous findings, and demonstrated improved power to detect plant height and flowering time related QTL relative to other published studies. An unexpected low correlation between flowering time and plant height permitted us to separate QTL for each trait and provide evidence against pleiotropy. Ten non- random syntenic regions conferring QTL for the same trait suggest that those QTL may represent alleles at genes functioning in the same manner since the 96 million year ago genome duplication that created these syntenic relationships, while syntenic regions conferring QTL for different trait may suggest sub-functionalization after duplication. Collectively, this study provides resources for marker-assisted breeding, as well as a framework for fine mapping and subsequent cloning of major genes for important traits such as plant height and flowering time in sorghum.

Genotyping by Sequencing of 393 Sorghum bicolor BTx623 × IS3620C Recombinant Inbred Lines Improves Sensitivity and Resolution of QTL Detection.

The molecular genetic basis of cotton fiber strength and fineness in crosses between Gossypium mustelinum and Gossypium hirsutum (Upland cotton) was dissected using 21 BC3F2 and 12 corresponding BC3F2:3 and BC3F2:4 families. The BC3F2 families were genotyped with simple sequence repeat markers from a G. hirsutum by G. mustelinum linkage map, and the three generations of BC3-derived families were phenotyped for fiber strength (STR) and fineness (Micronaire, MIC). A total of 42 quantitative trait loci (QTLs) were identified through one-way analysis of variance, including 15 QTLs for STR and 27 for MIC, with the percentage of variance explained by individual loci averaging 13.86 and 14.06%, respectively. Eighteen of the 42 QTLs were detected at least twice near the same markers in different generations/families or near linked markers in the same family, and 28 of the 42 QTLs were identified in both mixed model-based composite interval mapping and one-way variance analyses. Alleles from G. mustelinum increased STR for eight of 15 and reduced MIC for 15 of 27 QTLs. Significant among-family genotypic effects (P < 0.001) were detected in 13 and 10 loci for STR and MIC respectively, and five loci showed significant (P < 0.001) genotype × family interaction for MIC. These results support the hypothesis that fiber quality improvement for Upland cotton could be realized by introgressing G. mustelinum alleles although complexities due to the different effects of genetic background on introgressed chromatin might be faced. Building on prior work with G. barbadense, G. tomentosum, and G. darwinii, QTL mapping involving introgression of G. mustelinum alleles offers new allelic variation to Upland cotton germplasm.

/pdf/advanced-backcross-qtl-analysis-of-fiber-strength-and-5b9zqewgzs.pdf

Advanced Backcross QTL Analysis of Fiber Strength and Fineness in a Cross between Gossypium hirsutum and G. mustelinum

Deep learning for thermal image segmentation to measure canopy temperature of Brassica oleracea in the field

We describe a genetic map with a total of 381 bins of 616 genotyping by sequencing (GBS)-based SNP markers in a F6-F8 recombinant inbred line (RIL) population of 393 individuals derived from crossing S. bicolor BTx623 to S. bicolor IS3620C, a guinea line substantially diverged from BTx623. Five segregation distorted regions were found with four showing enrichment for S. bicolor alleles, suggesting possible selection during formation of this RIL population. A quantitative trait locus (QTL) study with this number of individuals, tripled relative to prior studies of this cross, provided resources, validated previous findings, and demonstrated improved power to detect plant height and flowering time related QTLs relative to other published studies. An unexpected low correlation between flowering time and plant height permitted us to separate QTLs for each trait and provide evidence against pleiotropy. Ten non- random syntenic regions conferring QTLs for the same trait suggest that those QTLs may represent alleles at genes functioning in the same manner since the 96 million year ago genome duplication that created these syntenic relationships, while syntenic regions conferring QTLs for different trait may suggest sub-functionalization after duplication. Collectively, this study provides resources for marker-assisted breeding, as well as a framework for fine mapping and subsequent cloning of major genes for important traits such as plant height and flowering time in sorghum.

https://www.biorxiv.org/content/biorxiv/early/2018/04/25/308478.full.pdf

Lan-shuan Shuang

Papers

Genotyping by Sequencing of 393 Sorghum bicolor BTx623 × IS3620C Recombinant Inbred Lines Improves Sensitivity and Resolution of QTL Detection.

Advanced Backcross QTL Analysis of Fiber Strength and Fineness in a Cross between Gossypium hirsutum and G. mustelinum

Deep learning for thermal image segmentation to measure canopy temperature of Brassica oleracea in the field

Genotyping by sequencing of 393 Sorghum bicolor BTx623 × IS3620C recombinant inbred lines improves sensitivity and resolution of QTL detection