Convolutional Neural Networks for Image-based Corn Kernel Detection and Counting

Precise in-season corn grain yield estimates enable farmers to make real-time accurate harvest and grain marketing decisions minimizing possible losses of profitability. A well developed corn ear can have up to 800 kernels, but manually counting the kernels on an ear of corn is labor-intensive, time consuming and prone to human error. From an algorithmic perspective, the detection of the kernels from a single corn ear image is challenging due to the large number of kernels at different angles and very small distance among the kernels. In this paper, we propose a kernel detection and counting method based on a sliding window approach. The proposed method detect and counts all corn kernels in a single corn ear image taken in uncontrolled lighting conditions. The sliding window approach uses a convolutional neural network (CNN) for kernel detection. Then, a non-maximum suppression (NMS) is applied to remove overlapping detections. Finally, windows that are classified as kernel are passed to another CNN regression model for finding the (x,y) coordinates of the center of kernel image patches. Our experiments indicate that the proposed method can successfully detect the corn kernels with a low detection error and is also able to detect kernels on a batch of corn ears positioned at different angles.Comment: 14 pages, 9 figure

Simulation of Fragmentation Characteristics of Projectile Jacket Made of Tungsten Alloy after Penetrating Metal Target Plate using SPH Method

A smooth particle hydrodynamics (SPH) model was used to simulate the fragmentation process of the jacket during penetrator with lateral efficiency (PELE) penetrating the metal target plate to study the fragmentation characteristics of PELE jacket made of tungsten alloy. The validity of the SPH model was verified by experimental results. Then the SPH model was used to simulate the jacket fragmentation under different impact velocity and thickness of target plate. The influence of impact velocity and thickness of target plate on the jacket fragmentation was obtained by analysing the mass distribution and quantity distribution of the fragments formed by the jacket. The results show that the dynamic fragmentation of tungsten alloy can be simulated effectively using the SPH model, Johnson-Cook strength model, maximum tensile stress failure criterion and stochastic failure model. When the thickness of target plate is fixed, the greater the impact velocity, the greater the pressure produced by the projectile impacting the target plate; with the increase of impact velocity, the mass of residual projectile decreases, the number of fragments formed by fragmentation of jacket increases linearly, and the average mass of fragments decreases exponentially. When the impact velocity is constant, the greater the thickness of the target plate, the longer the pressure duration by the projectile impacting the target plate; with the increase of the thickness of target plate, the mass of residual projectile decreases, the number of fragments formed by fragmentation of jacket increases linearly, and the average mass of fragments decreases exponentially. The numerical calculation model and research method adopted in this paper can be used to study the impact fragmentation of solid materials effectively

Fragmentation Behaviour of Radial Layered PELE Impacting Thin Metal Target Plates

The fragmentation mechanism of the penetrator with lateral effect (PELE) after perforating a thin target plate has been summarised and analysed firstly. Then the fragmentation of radial layered PELE was analysed qualitatively and verified by experiment. In the experiment, the target plates were made of 45# steel and 2A12 aluminium respectively. Qualitative analysis and experimental results show that: for normal PELE without layered, after perforating the thin metal target plate, from the bottom to the head of the projectile, the number of fragments formed by the jacket gradually increases, and the mass of the fragment decreases correspondingly. Compared with the normal PELE without layered, the radial layered PELE is less likely to break into fragments, when impacting the thin metal target plate with the same material and thickness under the same impact velocity. However, from the mechanism of the PELE, when the resistance of the target plate is large enough, and the duration of pressure is long enough, the radial layered PELE also can break into fragments with transverse velocity component. The resistance of the target plate plays an important role in the fragmentation of radial layered PELE. The radial layered PELE produced massive fragments with transverse velocity component when impacting the 45# steel plate with5 mm thickness under the impact velocity of 657.2 m/s

DeepCorn: A Semi-Supervised Deep Learning Method for High-Throughput Image-Based Corn Kernel Counting and Yield Estimation

The success of modern farming and plant breeding relies on accurate and efficient collection of data. For a commercial organization that manages large amounts of crops, collecting accurate and consistent data is a bottleneck. Due to limited time and labor, accurately phenotyping crops to record color, head count, height, weight, etc. is severely limited. However, this information, combined with other genetic and environmental factors, is vital for developing new superior crop species that help feed the world's growing population. Recent advances in machine learning, in particular deep learning, have shown promise in mitigating this bottleneck. In this paper, we propose a novel deep learning method for counting on-ear corn kernels in-field to aid in the gathering of real-time data and, ultimately, to improve decision making to maximize yield. We name this approach DeepCorn, and show that this framework is robust under various conditions. DeepCorn estimates the density of corn kernels in an image of corn ears and predicts the number of kernels based on the estimated density map. DeepCorn uses a truncated VGG-16 as a backbone for feature extraction and merges feature maps from multiple scales of the network to make it robust against image scale variations. We also adopt a semi-supervised learning approach to further improve the performance of our proposed method. Our proposed method achieves the MAE and RMSE of 41.36 and 60.27 in the corn kernel counting task, respectively. Our experimental results demonstrate the superiority and effectiveness of our proposed method compared to other state-of-the-art methods.Comment: 27 pages, 7 figure

Identification of related long non-coding RNAs and mRNAs in subclinical hypothyroidism complicated with type 2 diabetes by transcriptome analysis — a preliminary study

Introduction: The pathology mechanism of subclinical hypothyroidism and subclinical hypothyroidism complicated with type 2 diabetes remained uncertain. We aimed to find potential related long non-coding RNAs (lncRNAs) and mRNAs in the above diseases. Material and methods: Transcriptome sequencing was performed in three patients with subclinical hypothyroidism (S), three patients with subclinical hypothyroidism complicated with type 2 diabetes (SD), and three healthy controls (N). Differentially expressed mRNAs (DEmRNAs) and differentially expressed lncRNAs (DElncRNAs) were screened in S vs. N, SD vs. N, and SD vs. S group, and the nearby and co-expressed DEmRNAs of DElncRNAs were screened in S vs. N and SD vs. N. Moreover, functional analysis of DEmRNAs was then performed by Metascape. Results: In total, 465, 1058, and 943 DEmRNAs were obtained in S vs. N, SD vs. N, SD vs. S, respectively, and 191 overlapping genes were obtained in S vs. N and SD vs. N group. Among which, LAIR2, PNMA6A, and SFRP2 were deduced to be involved in subclinical hypothyroidism, and GPR162, APOL4, and ANK1 were deduced to be associated with subclinical hypothyroidism complicated with type 2 diabetes. A total of 50, 100, and 88 DElncRNAs were obtained in S vs. N, SD vs. N and SD vs. S, respectively. Combining with the interaction network of DElncRNA-DEmRNA, PAX8-AS1, co-expressed with KIR3DL1, was identified to function in subclinical hypothyroidism, and JHDM1D-AS1, co-expressed with ANK1, was deduced to play a role in subclinical hypothyroidism complicated with type 2 diabetes. Conclusions: Dysfunctional lncRNAs and mRNAs may be involved in the development of subclinical hypothyroidism and subclinical hypothyroidism complicated with type 2 diabetes.

Dynamic Programming for Resource Allocation in Multi-Allelic Trait Introgression

Trait introgression is a complex process that plant breeders use to introduce desirable alleles from one variety or species to another. Two of the major types of decisions that must be made during this sophisticated and uncertain workflow are: parental selection and resource allocation. We formulated the trait introgression problem as an engineering process and proposed a Markov Decision Processes (MDP) model to optimize the resource allocation procedure. The efficiency of the MDP model was compared with static resource allocation strategies and their trade-offs among budget, deadline, and probability of success are demonstrated. Simulation results suggest that dynamic resource allocation strategies from the MDP model significantly improve the efficiency of the trait introgression by allocating the right amount of resources according to the genetic outcome of previous generations

Radiation-induced brain structural and functional abnormalities in presymptomatic phase and outcome prediction: Radiation-Induced Brain Abnormalities

Radiation therapy, a major method of treatment for brain cancer, may cause severe brain injuries after many years. We used a rare and unique cohort of nasopharyngeal carcinoma patients with normal-appearing brains to study possible early irradiation injury in its presymptomatic phase before severe, irreversible necrosis happens. The aim is to detect any structural or functional imaging biomarker that is sensitive to early irradiation injury, and to understand the recovery and progression of irradiation injury that can shed light on outcome prediction for early clinical intervention. We found an acute increase in local brain activity that is followed by extensive reductions in such activity in the temporal lobe and significant loss of functional connectivity in a distributed, large-scale, high-level cognitive function-related brain network. Intriguingly, these radiosensitive functional alterations were found to be fully or partially recoverable. In contrast, progressive late disruptions to the integrity of the related far-end white matter structure began to be significant after one year. Importantly, early increased local brain functional activity was predictive of severe later temporal lobe necrosis. Based on these findings, we proposed a dynamic, multifactorial model for radiation injury and another preventive model for timely clinical intervention. Hum Brain Mapp 39:407-427, 2018. © 2017 Wiley Periodicals, Inc

Comparative analysis of liver transcriptome reveals adaptive responses to hypoxia environmental condition in Tibetan chicken

Objective Tibetan chickens, which have unique adaptations to extreme high-altitude environments, exhibit phenotypic and physiological characteristics that are distinct from those of lowland chickens. However, the mechanisms underlying hypoxic adaptation in the liver of chickens remain unknown. Methods RNA-sequencing (RNA-Seq) technology was used to assess the differentially expressed genes (DEGs) involved in hypoxia adaptation in highland chickens (native Tibetan chicken [HT]) and lowland chickens (Langshan chicken [LS], Beijing You chicken [BJ], Qingyuan Partridge chicken [QY], and Chahua chicken [CH]). Results A total of 352 co-DEGs were specifically screened between HT and four native lowland chicken breeds. Gene ontology and Kyoto encyclopedia of genes and genomes enrichment analyses indicated that these co-DEGs were widely involved in lipid metabolism processes, such as the peroxisome proliferator-activated receptors (PPAR) signaling pathway, fatty acid degradation, fatty acid metabolism and fatty acid biosynthesis. To further determine the relationship from the 352 co-DEGs, protein-protein interaction network was carried out and identified eight genes (ACSL1, CPT1A, ACOX1, PPARC1A, SCD, ACSBG2, ACACA, and FASN) as the potential regulating genes that are responsible for the altitude difference between the HT and other four lowland chicken breeds. Conclusion This study provides novel insights into the molecular mechanisms regulating hypoxia adaptation via lipid metabolism in Tibetan chickens and other highland animals