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No full textNot AvailableNot AvailableVegetables are the staple food in our diets. Vegetable
prices are difficult to forecast because they are influenced by a variety of factors, including weather, demand
and supply chain, Government policies, etc. and exhibit
volatile fluctuations. Marketing of vegetables is complex,
especially because of their perishability, seasonality and
bulkiness. An accurate and timely forecast of vegetables is essential to help its stakeholders. Previous studies
observed that traditional statistical models are unable
to capture the complex behaviour of vegetable markets.
In this study, a comparative assessment has been carried
out among the traditional time-series model, machine
learning and deep learning techniques in order to find
the best-suited model. For empirical illustration, cauliflower markets have been chosen as it is one of India’s
most important and popular winter. In order to identify
the complexity in the price of cauliflower, the machine
learning technique, i.e. artificial neural network and
deep learning technique, i.e. long short-term memory
model have been implemented. In addition, the traditional stochastic time-series model, i.e. autoregressive integrated moving average model, was used to compare
the prediction accuracy of the above models. To this
end, the moving window forecast approach was also
implemented to evaluate the sensitivity of these models
with respect to forecast length. It can be concluded that
the deep learning model outperforms the traditional
time-series model and the machine learning technique
for both short- and long-term forecasting.Not Availabl
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No full textNot AvailableAgriculture is essential for human existence, and it plays an important
role in the world economy. There is increasing demand for food to feed the everincreasing world population. Agriculture is affected by climate changes along with
weed control. Weeds are unwanted plants that compete with plants for nutrition,
and sunlight and adversely affect crop quality and production. Manual weeding is
a tedious and labor-intensive task because both crop and weed look the same by
visual appearance. Artificial intelligence techniques like deep learning can address
this problem of crop and weed classification. In this research work, a deep learningbased classification system has been proposed to classify the weed and crop based
on RGB images. We investigated two popular deep learning-based transfer learning
models, namely DenseNet169 and MobileNetV2, and assessed their performances
for crop and weed recognition. These models perform excellently with an accuracy
of 97.14 and 94.92%, respectively. The significant accuracy results make the model
an important tool for farmers to identify weeds.Not Availabl
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No full textNot AvailableMicrobes play a vital role in influencing the quality and health of soil and plants. Several studies had led to
understanding of diversity and structure in the plant rhizosphere: linking with Soil Microbial diversity to Modern
Agriculture, amrita gupta et.al (2022); Influence of long-term fertilization on soil microbial biomass, dehydrogenase
activity, and bacterial and fungal community structure in a brown soil of northeast, China Peiyu Luo et.al (2013).
The microbial population has many applications and advantages which are determined only through Long term
fertilizer studies. Some of the methods used for assessing the biodiversity of these rhizosphere microbes are: 16s
rRNA sequencing, DDGE, TDGE, SEM, etc. Some advantages of rhizosphere microbes are improved crop yield,
fighting climate change with minimal soil and environmental degradation, the protection of human health for current
and future generations, macro and micro-nutrient cycling for optimum agricultural growth, higher crop yield, and
also prevents land degradation. In this article we will study about importance of biodiversity of rhizosphere microbes
and various methods to identify the microbial population.Not Availabl
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No full textNot AvailablePiper yellow mottle virus (PYMoV) is a pararetrovirus associated with stunt disease in black pepper. As the
primary spread of the virus occurs through vegetative propagation, effective diagnostics are required for the
production of virus-free plants. Currently, available assays are time-consuming, require expensive equipment, and are not suitable for on-site detection. In the present study, two rapid assays based on the recombinase polymerase amplification (RPA) coupled with lateral flow assay (LFA) using (i) 6-carboxyfluorescein (FAM) labeled nfo probe and biotin-labeled reverse primer and (ii) FAM labeled forward and biotin-labeled reverse primer was developed for the detection of PYMoV. The assays were performed using TwistAmp DNA amplification reagents and crude extract from the infected plant and mealybug as templates. Both assays were optimized for parameters like concentration of magnesium acetate, temperature, and time. The RPA product was then diluted and applied to the sample pad of a lateral flow device for visualizing the results. The formation of a colored line at the test line was considered positive for PYMoV. The entire process from sample preparation to visualization of results could be completed in about 30 min. The developed assays were specific and 10 times more sensitive than PCR. The assays were validated using field samples of black pepper and mealybug vectors.Not Availabl
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No full textNot AvailableDue to relentless production and disposal of nano zinc oxide (nZnO), it has become critical to comprehend the serious risks large-scale accumulation of nZnO pose to bacterial communities in soil. The primary objective was to evaluate the changes in bacterial community structure and associated functional pathways through predictive metagenomic profiling and subsequent validation through Quantitative Realtime PCR in soil spiked with nZnO (0, 50, 200, 500 and 1000 mg Zn kg-1) and similar levels of bulk ZnO (bZnO). The results revealed that soil microbial biomass-C, -N, -P, soil respiration and enzyme activities decreased markedly at higher ZnO levels. The alpha diversity decreased with increasing ZnO level, with more impact under nZnO, while beta diversity analyses indicated a distinct dose- dependent separation of bacterial communities. The dominant taxa including Proteobacteria, Bacterioidetes, Acidobacteria and Planctomycetes significantly increased in abundance, while Firmicutes, Actinobacteria and Chloroflexi decreased in abundance with elevated nZnO and bZnO levels. Redundancy analysis indicated that changes in bacterial community structure instilled a greater dose- rather than size- specific response on key microbial parameters. Predicted key functions did not show a dose- specific response, and at 1000 mg Zn kg-1, methane metabolism as well as starch and sucrose metabolism were attenuated, while functions involving two component systems and bacterial secretion systems were enhanced under bZnO indicating better stress avoidance mechanism than under nZnO. Realtime PCR and microbial endpoint assays confirmed the metagenome derived taxonomic and functional data, respectively. Taxa and functions that varied substantially under stress were established as bioindicators to predict nZnO toxicity in soils. Taxon-function decoupling indicated that the soil bacterial communities deployed adaptive mechanisms under high ZnO, with lesser buffering capacity and resilience of communities under nZnO.Not Availabl
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No full textNot AvailableDrying of freshly harvested green pepper (Piper nigrum) was performed in a reverse air flow mechanical dryer (RRLT‐NC – 101). Fresh and blanched pepper of varieties, Sreekara and Panniyur‐1 were dried at 50°C, 55°C, and 60°C. Blanching was done by dipping in boiling water for 1 min. The experimental data for moisture loss was converted to moisture ratios and fitted to five thin layer drying models to describe the drying process mathematically. The results were compared for their goodness of fit in terms of coefficient of determination (r²), root mean square error (RMSE) and mean square of deviation (χ²). Logarithmic model was found most suitable to describe the drying process of black pepper. The unblanched Sreekara took 42, 34, and 26 h and blanched took 36, 30, and 24 h to dry from moisture content of 180.89–9.4% d.b. at air temperatures of 50°C, 55°C, and 60°C, respectively. In case of Panniyur the unblanched took 44, 36, and 28 h and blanched took 37, 32, and 26 h to dry from moisture content of 197.62–9.2% d.b. for the same temperatures. The effective moisture diffusivity varied from 3.28 × 10⁻⁷ to 6.44 × 10⁻⁷ m²/s. The activation energy was higher for unblanched than for blanched black pepper varied between 39.45 and 44.05 kJ/mol. Practical applications Drying of harvested pepper is one of the most important unit operations during processing of green pepper. Sun drying is the conventional method followed for drying. The despiked pepper berries are dried under sun for 4–5 days to bring the moisture content below 11%. The process of blanching green pepper in boiling water for 1 min followed by mechanical drying could produce better quality dried product. A simple reverse air flow, natural convection mechanical dryer suitable for drying various agricultural produce at farm level was used for drying green pepper. The results of the study could be applied for gathering information related to the drying process in a mechanical dryer like the drying kinetics, effective moisture diffusivity and activation energy as well as the effect of blanching on these parametersNot Availabl
