Fully automated yield estimation of intact fruits prior to harvesting provides various benefits to farmers. Until now, several studies have been conducted to estimate fruit yield using image-processing technologies. However, most of these techniques require thresholds for features such as color, shape and size. In addition, their performance strongly depends on the thresholds used, although optimal thresholds tend to vary with images. Furthermore, most of these techniques have attempted to detect only mature and immature fruits, although the number of young fruits is more important for the prediction of long-term fluctuations in yield. In this study, we aimed to develop a method to accurately detect individual intact tomato fruits including mature, immature and young fruits on a plant using a conventional RGB digital camera in conjunction with machine learning approaches. The developed method did not require an adjustment of threshold values for fruit detection from each image because image segmentation was conducted based on classification models generated in accordance with the color, shape, texture and size of the images. The results of fruit detection in the test images showed that the developed method achieved a recall of 0.80, while the precision was 0.88. The recall values of mature, immature and young fruits were 1.00, 0.80 and 0.78, respectively.

A genetic linkage map covering a large region of the genome with informative markers is essential for plant genome analysis, including identification of quantitative trait loci (QTLs), map-based cloning, and construction of a physical map. We constructed a soybean genetic linkage map using 190 F2 plants derived from a single cross between the soybean varieties Misuzudaizu and Moshidou Gong 503, based on restriction-fragment-length polymorphisms (RFLPs) and simple-sequence-repeat polymorphisms (SSRPs). This linkage map has 503 markers, including 189 RFLP markers derived from expressed sequence tag (EST) clones, and consists of 20 major linkage groups that may correspond to the 20 pairs of soybean chromosomes, covering 2908.7 cM of the soybean genome in the Kosambi function. Using this linkage map, we identified 4 QTLs--FT1, FT2, FT3, and FT4--for flowering time, the QTLs for the 5 largest principal components determining leaflet shape, 6 QTLs for single leaflet area, and 18 regions of segregation distortion. All 503 analyzed markers identified were located on the map, and almost all phenotypic variations in flowering time were explained by the detected QTLs. These results indicate that this map covers a large region of the soybean genome.

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The yield of cereal crops such as sorghum (Sorghum bicolor L. Moench) depends on the distribution of crop-heads in varying branching arrangements. Therefore, counting the head number per unit area is critical for plant breeders to correlate with the genotypic variation in a specific breeding field. However, measuring such phenotypic traits manually is an extremely labor-intensive process and suffers from low efficiency and human errors. Moreover, the process is almost infeasible for large-scale breeding plantations or experiments. Machine learning-based approaches like deep convolutional neural network (CNN) based object detectors are promising tools for efficient object detection and counting. However, a significant limitation of such deep learning-based approaches is that they typically require a massive amount of hand-labeled images for training, which is still a tedious process. Here, we propose an active learning inspired weakly supervised deep learning framework for sorghum head detection and counting from UAV-based images. We demonstrate that it is possible to significantly reduce human labeling effort without compromising final model performance (R2 between human count and machine count is 0.88) by using a semitrained CNN model (i.e., trained with limited labeled data) to perform synthetic annotation. In addition, we also visualize key features that the network learns. This improves trustworthiness by enabling users to better understand and trust the decisions that the trained deep learning model makes.

Within a flower the major source of the symmetrical elements is genotypic and the asymmetrical elements are strongly affected by the environment. With respect to petal area, the contribution of genotypes is also large; it is, however, affected by the macro-environment more notably than is petal shape.