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Plant diseases pose a major challenge in the agricultural sector, which affects plant development and crop productivity. Sugarcane farming is a highly organized part of farming. Owing to the desirable condition for sugarcane cultivation, India stands among the second largest producers of sugarcane over the globe. At the same time, sugarcane gets easily affected by multifarious diseases which significantly influence crop productivity. The recently developed computer vision (CV) and deep learning (DL) models with an effective design can be employed for the detection and classification of diseases in sugarcane plant. The disease detection in sugarcane plant is not accurate in the existing techniques. This paper presents a quantum behaved particle swarm optimization based deep transfer learning (QBPSO-DTL) model for sugarcane leaf disease detection and classification which produces high accuracy. The proposed QBPSO-DTL method is designed and trained for the prediction of diseased leaf images. The proposed QBPSO-DTL technique encompasses the design of optimal region growing segmentation to determine the affected regions in the leaf image. In addition, the SqueezeNet model is employed as a feature extractor and the deep stacked autoencoder (DSAE) model is applied as a classification model. Finally, the hyperparameter tuning of the DSAE model is carried out by using the QBPSO algorithm. For demonstrating the enhanced outcomes of the QBPSO-DTL approach, a wide range of experiments were implemented and the results ensured the improvements of the QBPSO-DTL model.

At present, early lung cancer screening is mainly based on radiologists’ experience in diagnosing benign and malignant pulmonary nodules by lung CT images. On the other hand, intraoperative rapid freezing pathology needs to analyse the invasive adenocarcinoma nodules with the worst recovery in adenocarcinoma. Moreover, rapid freezing pathology has a low diagnostic accuracy for small-diameter nodules. Because of the above problems, an algorithm for diagnosing invasive adenocarcinoma nodules in ground-glass pulmonary nodules is based on CT images. According to the nodule space information and plane features, sample data of different dimensions are designed, namely, 3D space and 2D plane feature samples. The network structure is designed based on the attention mechanism and residual learning unit; 2D and 3D neural networks are along built. By fusing the feature vectors extracted from networks of different dimensions, the diagnosis results of invasive adenocarcinoma nodules are finally obtained. The algorithm was studied on 1760 ground-glass nodules with 5-20 mm diameter collected from a city chest hospital with surgical and pathological results. There were 340 nodules with invasive adenocarcinoma and 340 with noninvasive adenocarcinoma. A total of 1420 invasive nodule samples were cross-validated on this example dataset. The classification accuracy of the algorithm was 82.7%, the sensitivity was 82.9%, and the specificity was 82.6%.

OBJECTIVES: Rubia cordifolia L. (RC) is a well-known and highly valuable medicinal plant in the Ayurvedic system. The present study involves evaluating antioxidant and cardioprotective property of RC root extract. MATERIALS AND METHODS: The characterization of RC root extract was carried out using standard phytochemical and biochemical analysis. The functional groups were analyzed by Fourier transform infrared (FTIR) spectroscopy and phytotherapeutic compounds were identified using high-resolution mass spectrometry (HR-MS). Cardioprotective activity of RC root extract was investigated against cyclophosphamide (CP; 100 mg/kg, i.p)-induced cardiotoxicity in male albino Wistar rats. RC (100, 200, and 400 mg/kg, p.o) or silymarin (100 mg/kg, p.o) was administered immediately after CP on the 1st day and the next consecutive 10 days. Biochemical and histopathological analysis was performed to observe the cardioprotective effects of RC root extract. RESULTS: Phytochemical analysis revealed the presence of secondary metabolites that include alkaloids, flavonoids, saponins, and anthraquinones in RC root extract. FTIR analysis revealed the presence of several functional groups. Based on HR-MS analysis, eight major phytotherapeutic compounds were identified in methanol root extract of RC. Biochemical analysis in CP-induced rat model administered with RC extract revealed significantly enhanced levels of antioxidant markers such as superoxide dismutase, catalase, and glutathione S-transferase. Histopathological study showed that the rat model treated with the root extract had reduced the cardiac injury. CONCLUSION: Our results have shown that the RC extract contains various antioxidant compounds with cardioprotective effect. Treatment with RC root extract could significantly protect CP-induced rats from cardiac tissue injury by restoring the antioxidant markers.

An organization seeks to ensure the participation of employees in decisions making. It helps in the utilization of skills. It also provides better incentives related to the job.Creativity and continuous improvement with minimal loss of time and resources allowed HPHR Practices to be implemented in the organization. (Ali et al., 2018) Researchers found that all together when human resource practices were used strategically they resulted in a positive relation towards the performance of an organization. According to Schuler & Jackson (1987), HRM practices are described as organizational actions. They intend to oversee the human resources that are available and also enumerate whether the available resource is utilized or not in achieving the corporate objectives.  This article concentrates on assess the influence of present organization experience of the respondents on high performance human resource practices. The researcher used  Anova analysis to find the result of the research study. The findings suggest that employees with fewer years in their present organization tend to rate high-performance HR practices more favorably than those with longer tenure. Employees with more than 10 years of experience consistently reported the lowest mean scores across all six HR practices, indicating potential dissatisfaction or declining perceived relevance of these practices over time.  

Achieving global goals for sustainable nutrition, health, and wellbeing will depend on delivering enhanced diets to humankind. This will require instantaneous access to information on food-source quality at key points of agri-food systems. Although laboratory analysis and benchtop NIR spectrometers are regularly used to quantify grain quality, these do not suit all end users, for example, stakeholders in decentralized agri-food chains that are typical in emerging economies. Therefore, we explored benchtop and portable NIR instruments, and the methods that might aid these particular end uses. For this purpose, we generated NIR spectra for 328 grain samples from multiple cereals (finger millet, foxtail millet, maize, pearl millet, and sorghum) with a standard benchtop NIR spectrometer (DS2500, FOSS) and a novel portable NIR-based instrument (HL-EVT5, Hone). We explored classical deterministic methods (via winISI, FOSS), novel machine learning (ML)-driven methods (via Hone Create, Hone), and a convolutional neural network (CNN)-based method for building the calibrations to predict grain protein out of the NIR spectra. All of the tested methods enabled us to build relevant calibrations out of both types of spectra (i.e., R2 ≥ 0.90, RMSE ≤ 0.91, RPD ≥ 3.08). Generally, the calibration methods integrating the ML techniques tended to enhance the prediction capacity of the model. We also documented that the prediction of grain protein content based on the NIR spectra generated using the novel portable instrument (HL-EVT5, Hone) was highly relevant for quantitative protein predictions (R2 = 0.91, RMSE = 0.97, RPD = 3.48). Thus, the presented findings lay the foundations for the expanded use of NIR spectroscopy in agricultural research, development, and trade.

This research paper examines the adverse effect of theoretical explanation of the grammatical rules among the learners. Exploration of the methods and materials taught inductively or deductively is the panacea to achieve the required goal. The study throws light on the pedagogical implication of adopting appropriate methods and materials for building the learners’ grammar and language. It primarily intends to explore a new teaching method using language corpora that can be employed in the English grammar classes in colleges at the undergraduate level. It strives to evaluate the effectiveness of teaching sentence patterns through corpus based activities comparing with the traditional based teaching. Thus the methodology aims to encourage students to become independent corpus users.

Background Agricultural systems are under extreme pressure to meet the global food demand, hence necessitating faster crop improvement. Rapid evaluation of the crops using novel imaging technologies coupled with robust image analysis could accelerate crops research and improvement. This proof-of-concept study investigated the feasibility of using X-ray imaging for non-destructive evaluation of rice grain traits. By analyzing 2D X-ray images of paddy grains, we aimed to approximate their key physical Traits (T) important for rice production and breeding: (1) T1 chaffiness, (2) T2 chalky rice kernel percentage (CRK%), and (3) T3 head rice recovery percentage (HRR%). In the future, the integration of X-ray imaging and data analysis into the rice research and breeding process could accelerate the improvement of global agricultural productivity. Results The study indicated, computer-vision based methods (X-ray image segmentation, features-based multi-linear models and thresholding) can predict the physical rice traits (chaffiness, CRK%, HRR%). We showed the feasibility to predict all three traits with reasonable accuracy (chaffiness: R2 = 0.9987, RMSE = 1.302; CRK%: R2 = 0.9397, RMSE = 8.91; HRR%: R2 = 0.7613, RMSE = 6.83) using X-ray radiography and image-based analytics via PCA based prediction models on individual grains. Conclusions Our study demonstrated the feasibility to predict multiple key physical grain traits important in rice research and breeding (such as chaffiness, CRK%, and HRR%) from single 2D X-ray images of whole paddy grains. Such a non-destructive rice grain trait inference is expected to improve the robustness of paddy rice evaluation, as well as to reduce time and possibly costs for rice grain trait analysis. Furthermore, the described approach can also be transferred and adapted to other grain crops.

Background: In India, raw peanuts are obtained by aggregators from smallholder farms in the form of whole pods and the price is based on a manual estimation of basic peanut pod and kernel characteristics. These methods of raw produce evaluation are slow and can result in procurement irregularities. The procurement delays combined with the lack of storage facilities lead to fungal contaminations and pose a serious threat to food safety in many regions. To address this gap, we investigated whether X-ray technology could be used for the rapid assessment of the key peanut qualities that are important for price estimation. Results: We generated 1752 individual peanut pod 2D X-ray projections using a computed tomography (CT) system (CTportable160.90). Out of these projections we predicted the kernel weight and shell weight, which are important indicators of the produce price. Two methods for the feature prediction were tested: (i) X-ray image transformation (XRT) and (ii) a trained convolutional neural network (CNN). The prediction power of these methods was tested against the gravimetric measurements of kernel weight and shell weight in diverse peanut pod varieties1. Both methods predicted the kernel mass with R2 > 0.93 (XRT: R2 = 0.93 and mean error estimate (MAE) = 0.17, CNN: R2 = 0.95 and MAE = 0.14). While the shell weight was predicted more accurately by CNN ( R2 = 0.91, MAE = 0.09) compared to XRT ( R2 = 0.78; MAE = 0.08). Conclusion: Our study demonstrated that the X-ray based system is a relevant technology option for the estimation of key peanut produce indicators (Figure 1). The obtained results justify further research to adapt the existing X-ray system for the rapid, accurate and objective peanut procurement process. Fast and accurate estimates of produce value are a necessary pre-requisite to avoid post-harvest losses due to fungal contamination and, at the same time, allow the fair payment to farmers. Additionally, the same technology could also assist crop improvement programs in selecting and developing peanut cultivars with enhanced economic value in a high-throughput manner by skipping the shelling of the pods completely. This study demonstrated the technical feasibility of the approach and is a first step to realize a technology-driven peanut production system transformation of the future.

Chickpea (Cicer arietinum L.) is mostly exposed to terminal drought stress which adversely influences its yield. Development of cultivars for suitable drought environments can offer sustainable solutions. We genetically engineered a desi-type chickpea variety to ectopically overexpress AtDREB1A, a transcription factor known to be involved in abiotic stress response, driven by the stress-inducible Atrd29A promoter. From several transgenic events of chickpea developed by Agrobacterium-mediated genetic transformation, four single copy events (RD2, RD7, RD9 and RD10) were characterized for DREB1A gene overexpression and evaluated under water stress in a biosafety greenhouse at T6 generation. Under progressive water stress, all transgenic events showed increased DREB1A gene expression before 50 % of soil moisture was lost (50 % FTSW or fraction of transpirable soil water), with a faster DREB1A transcript accumulation in RD2 at 85 % FTSW. Compared to the untransformed control, RD2 reduced its transpiration in drier soil and higher vapor pressure deficit (VPD) range (2.0–3.4 kPa). The assessment of terminal water stress response using lysimetric system that closely mimics the soil conditions in the field, showed that transgenic events RD7 and RD10 had increased biomass partitioning into shoot, denser rooting in deeper layers of soil profile and higher transpiration efficiency than the untransformed control. Also, RD9 with deeper roots and RD10 with higher root diameter showed that the transgenic events had altered rooting pattern compared to the untransformed control. These results indicate the implicit influence of rd29A::DREB1A on mechanisms underlying water uptake, stomatal response, transpiration efficiency and rooting architecture in water-stressed plants.