Background: High throughput non-destructive phenotyping is emerging as a significant approach for phenotyping germplasm and breeding populations for the identification of superior donors, elite lines, and QTLs. Detection and counting of spikes, the grain bearing organs of wheat, is critical for phenomics of a large set of germplasm and breeding lines in controlled and field conditions. It is also required for precision agriculture where the application of nitrogen, water, and other inputs at this critical stage is necessary. Further, counting of spikes is an important measure to determine yield. Digital image analysis and machine learning techniques play an essential role in non-destructive plant phenotyping analysis. Results:In this study, an approach based on computer vision, particularly object detection, to recognize and count the number of spikes of the wheat plant from the digital images is proposed. For spike identification, a novel deeplearning network, SpikeSegNet, has been developed by combining two proposed feature networks: Local Patch extraction Network (LPNet) and Global Mask refinement Network (GMRNet). In LPNet, the contextual and spatial features are learned at the local patch level. The output of LPNet is a segmented mask image, which is further refined at the global level using GMRNet. Visual (RGB) images of 200 wheat plants were captured using LemnaTec imaging system installed at Nanaji Deshmukh Plant Phenomics Centre, ICAR-IARI, New Delhi. The precision, accuracy, and robustness (F 1 score) of the proposed approach for spike segmentation are found to be 99.93%, 99.91%, and 99.91%, respectively. For counting the number of spikes, "analyse particles"-function of imageJ was applied on the output image of the proposed SpikeSegNet model. For spike counting, the average precision, accuracy, and robustness are 99%, 95%, and 97%, respectively. SpikeSegNet approach is tested for robustness with illuminated image dataset, and no significant difference is observed in the segmentation performance. © The Author(s) 2020. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article' s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article' Conclusion: In this study, a new approach called as SpikeSegNet has been proposed based on combined digital image analysis and deep learning techniques. A dedicated deep learning approach has been developed to identify and count spikes in the wheat plants. The performance of the approach demonstrates that SpikeSegNet is an effective and robust approach for spike detection and counting. As detection and counting of wheat spikes are cl...

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Biochar and arbuscular mycorrhizal fungi (AMF) can promote plant growth, improve soil properties, and maintain microbial activity. The effects of biochar and AMF on plant growth, root morphological traits, physiological properties, and soil enzymatic activities were studied in spinach (Spinacia oleracea L.). A pot experiment was conducted to evaluate the effect of biochar and AMF on the growth of spinach. Four treatments, a T1 control (soil without biochar), T2 biochar alone, T3 AMF alone, and T4 biochar and AMF together, were arranged in a randomized complete block design with five replications. The biochar alone had a positive effect on the growth of spinach, root morphological traits, physiological properties, and soil enzymatic activities. It significantly increased the plant growth parameters, such as the shoot length, leaf number, leaf length, leaf width, shoot fresh weight, and shoot dry weight. The root morphological traits, plant physiological attributes, and soil enzymatic activities were significantly enhanced with the biochar alone compared with the control. However, the combination of biochar and AMF had a greater impact on the increase in plant growth, root morphological traits, physiological properties, and soil enzymatic activities compared with the other treatments. The results suggested that the combined biochar and AMF led to the highest levels of spinach plant growth, microbial biomass, and soil enzymatic activity.

Temperature stress is one of the major limitations to crop productivity worldwide. Identifying suitable screening indices and quantifiable traits would facilitate the crop improvement process for high temperature tolerance. The objective of the present study was to screen and to identify temperature tolerant Brassica genotypes on the basis of physiological parameters viz. relative water content (RWC), total chlorophyll content, membrane stability index (MSI), total carotenoid content and yield. Fifteen Brassica juncea genotypes subjected to temperature stress by growing the crops at three dates of sowing i.e. 15th October (D 1 ), 1st November (D 2 ) and 15th November (D 3 ); showed decrease in RWC, MSI and chlorophyll content at D 2 and D 3 sowings compared to the D 1 . Genotypes like Proagro, NDR 8801 and CS-52 showed lower decline in MSI, RWC, chlorophyll and carotenoid content in leaves and seed yield/plant, while Pusa Agrani, EJ-15 and Pusa Tarak showed comparatively greater decline in the above parameters. The results suggest that physiological parameters like MSI, RWC and chlorophyll and carotenoid contents can be used as simple indices for screening and identifying temperature stress tolerant genotypes.