Plant Leaf Disease Detection Using Efficient Image Processing and Machine Learning Algorithms

India is often described as a country of villages, where a majority of the population depends on agriculture for their livelihood. The landscape of Indian agriculture is approximately 159.7 million hectares. Agriculture plays a pivotal role in India's Gross Domestic Product (GDP), accounting for about 18% of the nation's economic output. Diseases and pests can have detrimental effects on crops, leading to reduced yields. These challenges can include the spread of plant diseases, infestations by insects or other pests, and the overall degradation of crop health. Early detection of diseases in crops is crucial for several reasons. Detecting diseases at an early stage allows for prompt intervention, such as applying appropriate pesticides or taking preventive measures. The main aim of this study is to develop a highly effective method for plant leaf disease detection using computer vision techniques. Here, leaf disease detection comprises histogram equalization, denoising, image color threshold masking, feature descriptors such as Haralick textures, Hu moments, and color histograms to extract the salient features of leaf images. These features are then used to classify the images by training Logistic Regression, Linear Discriminant Analysis, K-nearest neighbor, decision tree, Random Forest, and Support Vector Machine algorithms using K-fold validation. K-fold validation is used to separate the validation samples from the training samples, and the K indicates the number of times this is repeated for the generalization. The training and validation processes are performed in two approaches. The first approach uses default hyperparameters with segmented and non-segmented images. In the second approach, all hyperparameters of the models are optimized to train segmented datasets. The classification accuracy improved by 2.19% by utilizing segmentation and hyperparameter tuning further improved by 0.48%. The highest average classification accuracy of 97.92% is achieved using the Random Forest classifier to classify 40 classes of 10 different plant species. Accurate detection of plant disease leads to the sustained growth of plants throughout the growing span of the plants

Monitoring lipid accumulation in the green microalga Botryococcus braunii with frequency-modulated stimulated Raman scattering

© 2015 SPIE.The potential of microalgae as a source of renewable energy has received considerable interest because they can produce lipids (fatty acids and isoprenoids) that can be readily converted into biofuels. However, significant research in this area is required to increase yields to make this a viable renewable source of energy. An analytical tool that could provide quantitative in situ spectroscopic analysis of lipids synthesis in individual microalgae would significantly enhance our capability to understand the synthesis process at the cellular level and lead to the development of strategies for increasing yield. Stimulated Raman scattering (SRS) microscopy has great potential in this area however, the pump-probe signal from two-color two-photon absorption of pigments (chlorophyll and carotenoids) overwhelm the SRS signal and prevent its application. Clearly, the development of a background suppression technique is of significant value for this important research area. To overcome the limitation of SRS in pigmented specimens, we establish a frequency-modulated stimulated Raman scattering (FM-SRS) microscopy that eliminates the non-Raman background by rapidly toggling on-and-off the targeted Raman resonance. Moreover, we perform the background-free imaging and analysis of intracellular lipid droplets and extracellular hydrocarbons in a green microalga with FM-SRS microscopy. We believe that FM-SRS microscopy demonstrates the potential for many applications in pigmented cells and provides the opportunity for improved selective visualization of the chemical composition of algae and plants.We thank Delong Zhang at Purdue University for fruitful discussion. This research was supported by grants (BB/K013602/1) from the Biotechnology and Biological Sciences Research Council (BBSRC) and Syngenta

Development and Operation Analysis of Spectrum Monitoring Subsystem 2.4–2.5 GHz Range

The paper presents a substantiation of the effectiveness of IEEE 802.11 wireless network analysis subsystem implementation using miniature spectrum analyzers. Also it was given an overview of firmware work scheme, development process of trial versions, monitoring system development approaches, current development stage, infrastructure for research system, reliability and scan check, our system design and hardware implementation, future work, etc. Paper also provides technical solutions on automation, optimal algorithms searching, errors correcting, organizing software according to the Model-View-Controller scheme, harmonizing data exchange protocols, storing and presenting the obtained results

Global Perspectives on Task Shifting and Task Sharing in Neurosurgery.

BACKGROUND: Neurosurgical task shifting and task sharing (TS/S), delegating clinical care to non-neurosurgeons, is ongoing in many hospital systems in which neurosurgeons are scarce. Although TS/S can increase access to treatment, it remains highly controversial. This survey investigated perceptions of neurosurgical TS/S to elucidate whether it is a permissible temporary solution to the global workforce deficit. METHODS: The survey was distributed to a convenience sample of individuals providing neurosurgical care. A digital survey link was distributed through electronic mailing lists of continental neurosurgical societies and various collectives, conference announcements, and social media platforms (July 2018-January 2019). Data were analyzed by descriptive statistics and univariate regression of Likert Scale scores. RESULTS: Survey respondents represented 105 of 194 World Health Organization member countries (54.1%; 391 respondents, 162 from high-income countries and 229 from low- and middle-income countries [LMICs]). The most agreed on statement was that task sharing is preferred to task shifting. There was broad consensus that both task shifting and task sharing should require competency-based evaluation, standardized training endorsed by governing organizations, and maintenance of certification. When perspectives were stratified by income class, LMICs were significantly more likely to agree that task shifting is professionally disruptive to traditional training, task sharing should be a priority where human resources are scarce, and to call for additional TS/S regulation, such as certification and formal consultation with a neurosurgeon (in person or electronic/telemedicine). CONCLUSIONS: Both LMIC and high-income countries agreed that task sharing should be prioritized over task shifting and that additional recommendations and regulations could enhance care. These data invite future discussions on policy and training programs

Extractive spectrophotometric determination of molybdenum(V) in molybdenum steels

An extraction spectrophotometric method has been developed for the determination of traces of molybdenum present in molybdenum steels which is based on the extraction of the orange-red molybdenum-thiocyanate-acetonethiosemicarbazone complex into chloroform from hydrochloric acid medium. The complex has an absorption maximum at 472 nm with a molar absorptivity of 1.9 × 104 liters mol−1 cm−1. Beer's law is valid over the concentration range 0.1–9.5 ppm of molybdenum with an optimum concentration range of 0.4–9 ppm. The equilibrium shift method indicates 1:4:2 composition for molybdenumthiocyanate-acetonethiosemicarbazone complex. The effect of acidity, reagent concentrations, temperature, and interferences from various ions are reported

Structural features of biologically active coordination compounds of vanillinsemicarbazone

Complexes of vanillinsemicarbazone (VSC) with MnII, FeII, CoII, NiII, CuII, ZnII, CdII and HgII have been prepared and characterised by elemental analyses, molar conductance, magnetic susceptibility and spectral data. Probable structures for the complexes are suggested on the basis of their physico-chemical properties. The fungitoxicity of VSC and the isolated complexes have been tested on pathogenic fungi. © 1985 VCH Verlagsgesellschaft mbH

Stereochemistry and fungitoxicity of complexes of p-anisaldehydethiosemicarbazone with Mn(II), Fe(II), Co(II) and Ni(II)

The complexes of p-Anisaldehydethiosemicarbazone (PAT) with Mn(II), Fe(II), Co(II) and Ni(II) have been isolated and characterised on the basis of elemental analyses, molar conductance, magnetic moment and spectral studies. Fungicidal activity has been evaluated against Alternaria (Sp.), Paecilomyces (Sp.) and Pestalotia (Sp.)