Catalogue Search | MBRL
Are you sure you want to remove the book from the shelf?
{{itemTitle}}
4,033
result(s) for
"Saleem, Muhammad"
Sort by:
Functions and strategies for enhancing zinc availability in plants for sustainable agriculture
Zinc (Zn), which is regarded as a crucial micronutrient for plants, and is considered to be a vital micronutrient for plants. Zn has a significant role in the biochemistry and metabolism of plants owing to its significance and toxicity for biological systems at specific Zn concentrations, i.e., insufficient or harmful above the optimal range. It contributes to several cellular and physiological activities of plants and promotes plant growth, development, and yield. Zn is an important structural, enzymatic, and regulatory component of many proteins and enzymes. Consequently, it is essential to understand the interplay and chemistry of Zn in soil, its absorption, transport, and the response of plants to Zn deficiency, as well as to develop sustainable strategies for Zn deficiency in plants. Zn deficiency appears to be a widespread and prevalent issue in crops across the world, resulting in severe production losses that compromise nutritional quality. Considering this, enhancing Zn usage efficiency is the most effective strategy, which entails improving the architecture of the root system, absorption of Zn complexes by organic acids, and Zn uptake and translocation mechanisms in plants. Here, we provide an overview of various biotechnological techniques to improve Zn utilization efficiency and ensure the quality of crop. In light of the current status, an effort has been made to further dissect the absorption, transport, assimilation, function, deficiency, and toxicity symptoms caused by Zn in plants. As a result, we have described the potential information on diverse solutions, such as root structure alteration, the use of biostimulators, and nanomaterials, that may be used efficiently for Zn uptake, thereby assuring sustainable agriculture.
Journal Article
Toxicity of Nano-Titanium Dioxide (TiO2-NP) Through Various Routes of Exposure: a Review
Nano-titanium dioxide (TiO
2
) is one of the most commonly used materials being synthesized for use as one of the top five nanoparticles. Due to the extensive application of TiO
2
nanoparticles and their inclusion in many commercial products, the increased exposure of human beings to nanoparticles is possible. This exposure could be routed via dermal penetration, inhalation and oral ingestion or intravenous injection. Therefore, regular evaluation of their potential toxicity and distribution in the bodies of exposed individuals is essential. Keeping in view the potential health hazards of TiO
2
nanoparticles for humans, we reviewed the research articles about studies performed on rats or other mammals as animal models. Most of these studies utilized the dermal or skin and the pulmonary exposures as the primary routes of toxicity. It was interesting that only very few studies revealed that the TiO
2
nanoparticles could penetrate through the skin and translocate to other tissues, while many other studies demonstrated that no penetration or translocation could happen through the skin. Conversely, the TiO
2
nanoparticles that entered through the pulmonary route were translocated to the brain or the systemic circulation from where these reached other organs like the kidney, liver, etc. In most studies, TiO
2
nanoparticles appeared to have caused oxidative stress, histopathological alterations, carcinogenesis, genotoxicity and immune disruption. Therefore, the use of such materials in humans must be either avoided or strictly managed to minimise risks for human health in various situations.
Journal Article
Automation in Agriculture by Machine and Deep Learning Techniques: A Review of Recent Developments
Recently, agriculture has gained much attention regarding automation by artificial intelligence techniques and robotic systems. Particularly, with the advancements in machine learning (ML) concepts, significant improvements have been observed in agricultural tasks. The ability of automatic feature extraction creates an adaptive nature in deep learning (DL), specifically convolutional neural networks to achieve human-level accuracy in various agricultural applications, prominent among which are plant disease detection and classification, weed/crop discrimination, fruit counting, land cover classification, and crop/plant recognition. This review presents the performance of recent uses in agricultural robots by the implementation of ML and DL algorithms/architectures during the last decade. Performance plots are drawn to study the effectiveness of deep learning over traditional machine learning models for certain agricultural operations. The analysis of prominent studies highlighted that the DL-based models, like RCNN (Region-based Convolutional Neural Network), achieve a higher plant disease/pest detection rate (82.51%) than the well-known ML algorithms, including Multi-Layer Perceptron (64.9%) and K-nearest Neighbour (63.76%). The famous DL architecture named ResNet-18 attained more accurate Area Under the Curve (94.84%), and outperformed ML-based techniques, including Random Forest (RF) (70.16%) and Support Vector Machine (SVM) (60.6%), for crop/weed discrimination. Another DL model called FCN (Fully Convolutional Networks) recorded higher accuracy (83.9%) than SVM (67.6%) and RF (65.6%) algorithms for the classification of agricultural land covers. Finally, some important research gaps from the previous studies and innovative future directions are also noted to help propel automation in agriculture up to the next level.
Journal Article
Alkaline and acidic soil constraints on iron accumulation by Rice cultivars in relation to several physio-biochemical parameters
Agricultural production is severely limited by an iron deficiency. Alkaline soils increase iron deficiency in rice crops, consequently leading to nutrient deficiencies in humans. Adding iron to rice enhances both its elemental composition and the nutritional value it offers humans through the food chain. The purpose of the current pot experiment was to investigate the impact of Fe treatment in alkaline (pH 7.5) and acidic (pH 5.5) soils to introduce iron-rich rice. Iron was applied to the plants in the soil in the form of an aqueous solution of FeSO
4
with five different concentrations (100, 200, 300, 400, and 500 mM). The results obtained from the current study demonstrated a significant increase in Fe content in
Oryza sativa
with the application of iron in both alkaline and acidic pH soils. Specifically, Basmati-515, one of the rice cultivars tested, exhibited a notable 13% increase in iron total accumulation per plant and an 11% increase in root-to-shoot ratio in acidic soil. In contrast to Basmati-198, which demonstrated maximum response in alkaline soil, Basmati-515 exhibited notable increases in all parameters, including a 31% increase in dry weight, 16% increase in total chlorophyll content, an 11% increase in CAT (catalase) activity, 7% increase in APX (ascorbate peroxidase) activity, 26% increase in POD (peroxidase) activity, and a remarkable 92% increase in SOD (superoxide dismutase) in acidic soil. In alkaline soil, Basmati-198 exhibited respective decreases of 40% and 39% in MDA and H
2
O
2
content, whereas Basmati-515 demonstrated a more significant decrease of 50% and 67% in MDA and H
2
O
2
in acidic soil. These results emphasize the potential for targeted soil management strategies to improve iron nutrition and address iron deficiency in agricultural systems. By considering soil conditions, it is possible to enhance iron content and promote its availability in alkaline and acidic soils, ultimately contributing to improved crop nutrition and human health.
Journal Article
Melatonin-Induced Salinity Tolerance by Ameliorating Osmotic and Oxidative Stress in the Seedlings of Two Tomato (Solanum lycopersicum L.) Cultivars
Melatonin is a crucial biological hormone associated with many physiological and biochemical processes in plants and also enhances resistance against various abiotic stresses. However, the mechanisms underlying the melatonin-assisted mitigation of salt stress in tomato (Solanum lycopersicum L.) plant are still poorly understood. A hydroponic experiment was conducted to investigate the protective role of melatonin in two tomato cultivars (Roma and FM9) under a highly saline growth medium (160 mM NaCl). The one level of melatonin (1.0 µmol L−1) was applied exogenously, sole, or in combination with the salinity stress. NaCl-induced phytotoxicity significantly (P < 0.05) reduced shoot and root dry matter accumulation, chlorophyll contents, relative water contents (RWC), membrane stability index (MSI), and antioxidant enzymatic activities in both cultivars as compared to the control treatment. Moreover, salt treatment alone increased soluble sugar contents (sucrose and fructose), sodium (Na+) uptake, as well as oxidative damage in the leaves of tomato seedlings. However, exogenous supply of melatonin alleviated salt toxicity in tomato seedlings which were more obvious in Roma cultivar as compared to FM 9 cultivar, as demonstrated by a higher increment in the values of growth indicators, RWC, MSI, gaseous exchange attributes, activities of superoxide dismutase (SOD), peroxidase (POD), catalase (CAT), and ascorbate peroxidase (APX). In addition, melatonin also alleviated salt-induced oxidative stress by suppressing malondialdehyde (MDA) and hydrogen peroxide (H2O2) contents as well as significantly reduced Na+ uptake at the root surface of tomato plants. It can be concluded that melatonin-induced salt tolerance in tomato is due to enhancement of plant water relations, and improved photosynthetic and antioxidant capacity along with ion homeostasis.
Journal Article
A high sensitivity, low cost and fully decoupled multi-axis capacitive tactile force sensor for robotic surgical systems
This paper presents the design of a multi-axis capacitive tactile force sensor with a fully decoupled output response for input normal and shear forces. A patterned elastomer is used as a dielectric layer between capacitive electrodes of the sensor that allows to achieve relatively higher sensitivity. The sensor is fabricated utilizing a low-cost rapid prototyping technique and is characterized for normal and shear forces in the range of 0 ~ 10 N and 0 ~ 3.1 N respectively. The achieved force sensitivity for the normal axis is 2.03%/N and for shear axes is 1.67%/N. The difference between the estimated force from the sensor and actual force applied is negligible, which demonstrates the accuracy of the sensor. The reliability of the sensor is analysed by performing hysteresis and repeatability tests. The hysteresis error is found to be 4.94% and 4.69% for normal and shear forces respectively. The repeatability error of the sensor is less than 5%, which shows the stability of the sensor. The high sensitivity, linear output response, high force measurement range, reliability and low cost make the proposed tactile sensor suitable for the force feedback in the robotic surgical systems.
Journal Article
Silk fibroin/hydroxyapatite scaffold: a highly compatible material for bone regeneration
In recent years remarkable efforts have been made to produce artificial bone through tissue engineering techniques. Silk fibroin (SF) and hydroxyapatite (HA) have been used in bone tissue regeneration as biomaterials due to mechanical properties of SF and biocompatibility of HA. There has been growing interest in developing SF/HA composites to reduce bone defects. In this regard, several attempts have been made to study the biocompatibility and osteoconductive properties of this material. This article overviews the recent advance from last few decades in terms of the preparative methods and application of SF/HA in bone regeneration. Its first part is related to SF that presents the most common sources, preparation methods and comparison of SF with other biomaterials. The second part illustrates the importance of HA by providing information about its production and properties. The third part presents comparative studies of SF/HA composites with different concentrations of HA along with methods of preparation of composites and their applications.
Journal Article
Image-Based Plant Disease Identification by Deep Learning Meta-Architectures
The identification of plant disease is an imperative part of crop monitoring systems. Computer vision and deep learning (DL) techniques have been proven to be state-of-the-art to address various agricultural problems. This research performed the complex tasks of localization and classification of the disease in plant leaves. In this regard, three DL meta-architectures including the Single Shot MultiBox Detector (SSD), Faster Region-based Convolutional Neural Network (RCNN), and Region-based Fully Convolutional Networks (RFCN) were applied by using the TensorFlow object detection framework. All the DL models were trained/tested on a controlled environment dataset to recognize the disease in plant species. Moreover, an improvement in the mean average precision of the best-obtained deep learning architecture was attempted through different state-of-the-art deep learning optimizers. The SSD model trained with an Adam optimizer exhibited the highest mean average precision (mAP) of 73.07%. The successful identification of 26 different types of defected and 12 types of healthy leaves in a single framework proved the novelty of the work. In the future, the proposed detection methodology can also be adopted for other agricultural applications. Moreover, the generated weights can be reused for future real-time detection of plant disease in a controlled/uncontrolled environment.
Journal Article
Drought Induced Changes in Growth, Osmolyte Accumulation and Antioxidant Metabolism of Three Maize Hybrids
Consequences of drought stress in crop production systems are perhaps more deleterious than other abiotic stresses under changing climatic scenarios. Regulations of physio-biochemical responses of plants under drought stress can be used as markers for drought stress tolerance in selection and breeding. The present study was conducted to appraise the performance of three different maize hybrids (Dong Dan 80, Wan Dan 13, and Run Nong 35) under well-watered, low, moderate and SD conditions maintained at 100, 80, 60, and 40% of field capacity, respectively. Compared with well-watered conditions, drought stress caused oxidative stress by excessive production of reactive oxygen species (ROS) which led to reduced growth and yield formation in all maize hybrids; nevertheless, negative effects of drought stress were more prominent in Run Nong 35. Drought-induced osmolyte accumulation and strong enzymatic and non-enzymatic defense systems prevented the severe damage in Dong Dan 80. Overall performance of all maize hybrids under drought stress was recorded as: Dong Dan 80 > Wan Dan 13 > Run Nong 35 with 6.39, 7.35, and 16.55% yield reductions. Consequently, these biochemical traits and differential physiological responses might be helpful to develop drought tolerance genotypes that can withstand water-deficit conditions with minimum yield losses.
Journal Article