DeepWeeds: a multiclass weed species image dataset for deep learning

Robotic weed control has seen increased research of late with its potential for boosting productivity in agriculture. Majority of works focus on developing robotics for croplands, ignoring the weed management problems facing rangeland stock farmers. perhaps the greatest obstacle to widespread uptake of robotic weed control is the robust classification of weed species in their natural environment. the unparalleled successes of deep learning make it an ideal candidate for recognising various weed species in the complex rangeland environment. This work contributes the first large, public, multiclass image dataset of weed species from the Australian rangelands; allowing for the development of robust classification methods to make robotic weed control viable. The DeepWeeds dataset consists of 17,509 labelled images of eight nationally significant weed species native to eight locations across northern Australia. This paper presents a baseline for classification performance on the dataset using the benchmark deep learning models, Inception-v3 and ResNet-50. These models achieved an average classification accuracy of 95.1% and 95.7%, respectively. We also demonstrate real time performance of the ResNet-50 architecture, with an average inference time of 53.4 ms per image. These strong results bode well for future field implementation of robotic weed control methods in the Australian rangelands

DeepWeeds: a multiclass weed species image dataset for deep learning

Robotic weed control has seen increased research of late with its potential for boosting productivity in agriculture. Majority of works focus on developing robotics for croplands, ignoring the weed management problems facing rangeland stock farmers. perhaps the greatest obstacle to widespread uptake of robotic weed control is the robust classification of weed species in their natural environment. the unparalleled successes of deep learning make it an ideal candidate for recognising various weed species in the complex rangeland environment. This work contributes the first large, public, multiclass image dataset of weed species from the Australian rangelands; allowing for the development of robust classification methods to make robotic weed control viable. The DeepWeeds dataset consists of 17,509 labelled images of eight nationally significant weed species native to eight locations across northern Australia. This paper presents a baseline for classification performance on the dataset using the benchmark deep learning models, Inception-v3 and ResNet-50. These models achieved an average classification accuracy of 95.1% and 95.7%, respectively. We also demonstrate real time performance of the ResNet-50 architecture, with an average inference time of 53.4 ms per image. These strong results bode well for future field implementation of robotic weed control methods in the Australian rangelands

Single-Nucleus Transcriptome Profiling of Dorsolateral Prefrontal Cortex:Mechanistic Roles for Neuronal Gene Expression, Including the 17q21.31 Locus, in PTSD Stress Response

OBJECTIVE: Multidisciplinary studies of posttraumatic stress disorder (PTSD) and major depressive disorder (MDD) implicate the dorsolateral prefrontal cortex (DLPFC) in disease risk and pathophysiology. Postmortem brain studies have relied on bulk-tissue RNA sequencing (RNA-seq), but single-cell RNA-seq is needed to dissect cell-type-specific mechanisms. The authors conducted the first single-nucleus RNA-seq postmortem brain study in PTSD to elucidate disease transcriptomic pathology with cell-type-specific resolution. METHOD: Profiling of 32 DLPFC samples from 11 individuals with PTSD, 10 with MDD, and 11 control subjects was conducted (~415K nuclei; >13K cells per sample). A replication sample included 15 DLPFC samples (~160K nuclei; >11K cells per sample). RESULTS: Differential gene expression analyses identified significant single-nucleus RNA-seq differentially expressed genes (snDEGs) in excitatory (EX) and inhibitory (IN) neurons and astrocytes, but not in other cell types or bulk tissue. MDD samples had more false discovery rate-corrected significant snDEGs, and PTSD samples had a greater replication rate. In EX and IN neurons, biological pathways that were differentially enriched in PTSD compared with MDD included glucocorticoid signaling. Furthermore, glucocorticoid signaling in induced pluripotent stem cell (iPSC)-derived cortical neurons demonstrated greater relevance in PTSD and opposite direction of regulation compared with MDD, especially in EX neurons. Many snDEGs were from the 17q21.31 locus and are particularly interesting given causal roles in disease pathogenesis and DLPFC-based neuroimaging (PTSD: , , and ; MDD: and ), while others were regulated by glucocorticoids in iPSC-derived neurons (PTSD: , ; MDD: ). CONCLUSIONS: The study findings point to cell-type-specific mechanisms of brain stress response in PTSD and MDD, highlighting the importance of examining cell-type-specific gene expression and indicating promising novel biomarkers and therapeutic targets