Root system architecture (RSA) plays a fundamental role in nutrient uptake, including zinc (Zn). Wheat grains are inheritably low in Zn. As Zn is an essential nutrient for plants, improving its uptake will not only improve their growth and yield but also the nutritional quality of staple grains. A rhizobox study followed by a pot study was conducted to evaluate Zn variability with respect to RSA and its impact on grain Zn concentration. The grain Zn content of one hundred wheat varieties was determined and grown in rhizoboxes with differential Zn (no Zn and 0.05 mg L-1 ZnSO4). Seedlings were harvested 12 days after sowing, and root images were taken and analyzed by SmartRoot software. Using principal component analysis, twelve varieties were screened out based on vigorous and weaker RSA with high and low grain Zn content. The screened varieties were grown in pots with (11 mg ZnSO4 kg-1 soil) and without Zn application to the soil. Zinc translocation, localization, and agronomic parameters were recorded after harvesting at maturity. In the rhizobox experiment, 4% and 8% varieties showed higher grain Zn content with vigorous and weaker RSA, respectively, while 45% and 43% varieties had lower grain Zn content with vigorous and weaker RSA. However, the pot experiment revealed that varieties with vigorous root system led to higher grain yield, though the grain Zn concentration were variable, while all varieties with weaker root system had lower yield as well as grain Zn concentration. Zincol-16 revealed the highest Zn concentration (28.07 mg kg-1) and grain weight (47.9 g). Comparatively higher level of Zn was localized in the aleurone layer than in the embryonic region and endosperm. It is concluded that genetic variability exists among wheat varieties for RSA and grain Zn content, with a significant correlation. Therefore, RSA attributes are promising targets for the Zn biofortification breeding program. However, Zn localization in endosperm needs to be further investigated to achieve the goal of reducing Zn malnutrition.
Triticum , Zinc , Zinc/analysis , Triticum/genetics , Plant Breeding , Minerals , Edible Grain/chemistry , Soil
Heavy metals contamination is posing severe threat to the soil health and environmental sustainability. Application of industrial and sewage waste as irrigation and growing urbanization and agricultural industry is the main reason for heavy metals pollution. Therefore, the present study was planned to assess the influence of different irrigation sources such as industrial effluents, sewage wastewater, tube well water, and canal water on the soil physio-chemical, soil biological, and enzymatic characteristics. Results showed that sewage waste and industrial effluents affect the soil pH, organic matter, total organic carbon, and cation exchange capacity. The highest total nickel (383.71 mg kg-1), lead (312.46 mg kg-1), cadmium (147.75 mg kg-1), and chromium (163.64 mg kg-1) were recorded with industrial effluents application. Whereas, industrial effluent greatly reduced the soil microbial biomass carbon (SMB-C), soil microbial biomass nitrogen (SMB-N), soil microbial biomass phosphorus (SMB-P), and soil microbial biomass sulphur (SMB-S) in the winter season at sowing time. Industrial effluent and sewage waste inhibited the soil enzymes activities. For instance, the minimum activity of amidase, urease, alkaline-phosphatase, ß-glucosidase, arylsulphatase and dehydrogenase activity was noted with HMs contamination. The higher levels of metals accumulation was observed in vegetables grown in soil contaminated with untreated waste water and industrial effluent in comparison to soil irrigated with canal and tube well water. The mean increase in soil microbial parameters and enzyme activities was also observed in response to the change in season from winter to spring due to increase in soil mean temperature. The SMB-C, SMB-N, SMB-P and SMB-S showed significant positive correlation with soil enzymes (amidase, urease, alkaline-phosphatase, ß-glucosidase, arylsulphatase and dehydrogenase). The heavy metals accumulation in soil is toxic to microorganisms and inhibits enzyme functions critical for nutrient cycling and organic matter decomposition and can disrupt the delicate balance of soil ecosystem and may lead to long-term damage of soil biological health.
This study proposes an adaptive image augmentation scheme using deep reinforcement learning (DRL) to improve the performance of a deep learning-based automated optical inspection system. The study addresses the challenge of inconsistency in the performance of single image augmentation methods. It introduces a DRL algorithm, DQN, to select the most suitable augmentation method for each image. The proposed approach extracts geometric and pixel indicators to form states, and uses DeepLab-v3+ model to verify the augmented images and generate rewards. Image augmentation methods are treated as actions, and the DQN algorithm selects the best methods based on the images and segmentation model. The study demonstrates that the proposed framework outperforms any single image augmentation method and achieves better segmentation performance than other semantic segmentation models. The framework has practical implications for developing more accurate and robust automated optical inspection systems, critical for ensuring product quality in various industries. Future research can explore the generalizability and scalability of the proposed framework to other domains and applications. The code for this application is uploaded at https://github.com/lynnkobe/Adaptive-Image-Augmentation.git.
Agricultural crops are subject to a variety of biotic and abiotic stresses that adversely affect growth and reduce the yield of crop plantss. Traditional crop stress management approaches are not capable of fulfilling the food demand of the human population which is projected to reach 10 billion by 2050. Nanobiotechnology is the application of nanotechnology in biological fields and has emerged as a sustainable approach to enhancing agricultural productivity by alleviating various plant stresses. This article reviews innovations in nanobiotechnology and its role in promoting plant growth and enhancing plant resistance/tolerance against biotic and abiotic stresses and the underlying mechanisms. Nanoparticles, synthesized through various approaches (physical, chemical and biological), induce plant resistance against these stresses by strengthening the physical barriers, improving plant photosynthesis and activating plant defense mechanisms. The nanoparticles can also upregulate the expression of stress-related genes by increasing anti-stress compounds and activating the expression of defense-related genes. The unique physico-chemical characteristics of nanoparticles enhance biochemical activity and effectiveness to cause diverse impacts on plants. Molecular mechanisms of nanobiotechnology-induced tolerance to abiotic and biotic stresses have also been highlighted. Further research is needed on efficient synthesis methods, optimization of nanoparticle dosages, application techniques and integration with other technologies, and a better understanding of their fate in agricultural systems.
Accurate segregation of retinal blood vessels network plays a crucial role in clinical assessments, treatments, and rehabilitation process. Owing to the presence of acquisition and instrumentation anomalies, precise tracking of vessels network is challenging. For this, a new fundus image segmentation framework is proposed by combining deep neural networks, and hidden Markov model. It has three main modules: the Atrous spatial pyramid pooling-based encoder, the decoder, and hidden Markov model vessel tracker. The encoder utilized modified ResNet18 deep neural networks model for low-and-high-levels features extraction. These features are concatenated in module-II by the decoder to perform convolution operations to obtain the initial segmentation. Previous modules detected the main vessel structure and overlooked some small capillaries. For improved segmentation, hidden Markov model vessel tracker is integrated with module-I and-II to detect overlooked small capillaries of the vessels network. In last module, final segmentation is obtained by combining multi-oriented sub-images using logical OR operation. This novel framework is validated experimentally using two standard DRIVE and STARE datasets. The developed model offers high average values of accuracy, area under the curve, and sensitivity of 99.8, 99.0, and 98.2%, respectively. Analysis of the results revealed that the developed approach offered enhanced performance in terms of sensitivity 18%, accuracy 3%, and specificity 1% over the state-of-the-art approaches. Owing to better learning and generalization capability, the developed approach tracked blood vessels network efficiently and automatically compared to other approaches. The proposed approach can be helpful for human eye assessment, disease diagnosis, and rehabilitation process.
Deep Learning , Humans , Algorithms , Neural Networks, Computer , Retinal Vessels/diagnostic imaging , Fundus Oculi , Image Processing, Computer-Assisted/methods
Dengue virus (DENV) infection is one of the major health issues and a substantial epidemic infectious human disease. More than two billion humans are living in dengue susceptible regions with annual infection mortality rate is about 5%-20%. At initial stages, it is difficult to differentiate dengue virus symptoms with other similar diseases. The main objective of this research is to diagnose dengue virus infection in human blood sera for better treatment and rehabilitation process. A novel and robust approach is proposed based on Raman spectroscopy and deep learning. In this regard, the ResNet101 deep learning model is modified by exploiting transfer learning (TL) concept on Raman spectroscopic data of human blood sera. Sample size was selected using standard statistical tests. The proposed model is evaluated on 2,000 Raman spectra images in which 1,200 are DENV-infected of human blood sera samples, and 800 are healthy ones. It offers 96.0% accuracy on testing data for DENV infection diagnosis. Moreover, the developed approach demonstrated minimum improvement of 6.0% and 7.0% in terms of AUC and Kappa index respectively over the other state-of-the-art techniques. The developed model offers superior performance to capture minute Raman spectral variations due to the better residual learning capability and generalization ability compared to others deep learning models. The developed model revealed that it might be applied for diagnosis of DENV infection to save precious human lives.
Sustainable food security is a major challenge in today's world, particularly in developing countries. Among many factors, environmental stressors, i.e., drought, salinity and heavy metals are major impediments in achieving sustainable food security. This calls for finding environment-friendly and cheap solutions to address these stressors. Plant growth-promoting rhizobacteria (PGPR) have long been established as an environment-friendly means to enhance agricultural productivity in normal and stressed soils and are being applied at field scale. Similarly, pyrolyzing agro-wastes into biochar with the aim to amend soils is being proposed as a cheap additive for enhancement of soil quality and crop productivity. Many pot and some field-scale experiments have confirmed the potential of biochar for sustainable increase in agricultural productivity. Recently, many studies have combined the PGPR and biochar for improving soil quality and agricultural productivity, under normal and stressed conditions, with the assumption that both of these additives complement each other. Most of these studies have reported a significant increase in agricultural productivity in co-applied treatments than sole application of PGPR or biochar. This review presents synthesis of these studies in addition to providing insights into the mechanistic basis of the interaction of the PGPR and biochar. Moreover, this review highlights the future perspectives of the research in order to realize the potential of co-application of the PGPR and biochar at field scale.
BACKGROUND: Zinc (Zn) deficiency and low soil fertility are the major factors responsible for low yield in chickpea. This study was conducted to evaluate the effect of Zn application and plant growth-promoting bacteria (PGPB) (endophyte Enterobacter sp. MN17) on soil health and aboveground biomass of desi and kabuli chickpea under natural field conditions. Zn was applied as seed priming (0.001 mol L-1 ) and soil application (10 kg Zn ha-1 ) with and without PGPB. To determine the impacts of Zn and PGPB on soil biological health, soil microbial biomass carbon (MBC) and soil extracellular enzyme activities were analyzed at two growth stages: vegetative (90 days after sowing) and maturity (163 days after sowing). RESULTS: The highest aboveground biomass (5.1 t ha-1 ) was recorded with Zn seed priming + PGPB in kabuli chickpea and in desi chickpea (4.8 t ha-1 ) with Zn seed priming only. The application of Zn significantly increased soil MBC, which was higher in kabuli (795 and 731 µg C g-1 ) compared to desi chickpea (655 and 533 µg C g-1 ) at both vegetative and reproductive growth stages, respectively. The highest extracellular soil enzyme activities, - ß-glucosidase (4758 nmol g-1 h-1 ), acid phosphatase (5508 nmol g-1 h-1 ), chitinase (5997 nmol g-1 h-1 ) and leucine aminopeptidase (993 nmol g-1 h-1 ) - were recorded with Zn seed priming. Of the chickpea types, kabuli chickpea had higher soil extracellular enzyme activities in the rhizosphere than desi chickpea. CONCLUSION: Zn seed priming along with PGPB application may improve soil health and chickpea biomass in marginal soils. © 2021 Society of Chemical Industry.
Cicer , Bacteria , Biomass , Soil , Zinc
Nanotechnology has shown promising potential to promote sustainable agriculture. This article reviews the recent developments on applications of nanotechnology in agriculture including crop production and protection with emphasis on nanofertilizers, nanopesticides, nanobiosensors and nano-enabled remediation strategies for contaminated soils. Nanomaterials play an important role regarding the fate, mobility and toxicity of soil pollutants and are essential part of different biotic and abiotic remediation strategies. Efficiency and fate of nanomaterials is strongly dictated by their properties and interactions with soil constituents which is also critically discussed in this review. Investigations into the remediation applications and fate of nanoparticles in soil remain scarce and are mostly limited to laboratory studies. Once entered in the soil system, nanomaterials may affect the soil quality and plant growth which is discussed in context of their effects on nutrient release in target soils, soil biota, soil organic matter and plant morphological and physiological responses. The mechanisms involved in uptake and translocation of nanomaterials within plants and associated defense mechanisms have also been discussed. Future research directions have been identified to promote the research into sustainable development of nano-enabled agriculture.
Direct discharge of untreated industrial waste water in water bodies and then irrigation from these sources has increased trace metals contamination in paddy fields of southern China. Among trace metals, cadmium (Cd) and lead (Pb) are classified as most harmful contaminants in farmland to many organisms including plants, animals and humans. Rice is a staple food which is consumed by half population of the world; due to longer growth period it can easily absorb and accumulate the trace metals from soil. The objective of study was to check the efficacy of Se and Si NPs (nanoparticles) alone or in combination on metals accumulation and Se-fortified rice (Oryzasativa L.) production as their efficiency remained untested. Alone as well as combined application of Se- and Si-NPs (5, 10 and 20 mg L-1) was achieved along with CK. All the treatments significantly reduced the Cd and Pb contents in brown rice, except CK, Se3, Si1 and Se1Si3. Combined application of Se and Si (Se3Si2) was more effective in reducing the Cd and Pb contents by 62 and 52%, respectively. In addition, foliar application of both NPs improved the rice growth and quality by increasing the grain yield, rice biomass, and Se contents in brown rice. Highest concentration of Se (1.35 mg kg-1) in brown rice was observed with combined application of Se- and Si-Nps (Se3Si2). Selenium speciation revealed the presence of organic species (74%) in brown rice. The combinations of different doses of Se- and Si-Nps are the main determining factor for total concentration of metals in grains. These results demonstrate that foliage supplementation of Se and Si-Nps alleviate the Cd and Pb toxicity by reducing the metals' concentration in brown rice. Additionally foliage supplementation improved the nutritional quality by reducing the phytic acid contents in rice grains.
Nanoparticles , Oryza , Cadmium , China , Lead , Selenium , Silicon , Soil , Soil Pollutants
Food contamination with cadmium (Cd) is a serious health threat to humans worldwide and Cd accumulation by rice is a major source of Cd entrance to the food chain. Silicon (Si) application decreases the Cd content in rice but the timing of Si application may need further investigation. The present study investigated the effect of split application of Si in the soil (600 kg/ha of Si) at different growth stages of rice on the growth and Cd accumulation by rice under Cd stress. Rice plants were grown in the presence and absence of Cd and Si was applied in the soil at different growth stages of rice under Cd stress. The results indicated that Cd stress alone reduced the growth and photosynthesis and increased the Cd content in different tissues and grains of rice. Silicon application improved the plant growth and reduced the Cd accumulation, translocation factor, and bioaccumulation factor in rice especially in grains, whereas the response of Si varied with the application of Si at different growth stages. The application of Si in three splits (transplanting (S1), tillering (S2), panicle initiation (S3)) was the best in improving growth and reducing Cd concentrations in plants compared to other combinations of Si application. Silicon application in three splits (S1+S2+S3) reduced the grain Cd concentrations below the threshold level (0.2 mg/kg) and reduced the Cd health risk index under the experimental conditions. Overall, split application of Si at three growth stages may function as remediator and diminishes Cd uptake into rice grains.
Cadmium/chemistry , Oryza/chemistry , Silicon/chemistry , Soil Pollutants/chemistry , Soil/chemistry , Photosynthesis
