Yield Maximization Research Articles

Enhancing the light yield of He:CF4\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$_4$$\\end{document} based gaseous detector

The CYGNO experiment aims to build a large (O(10)\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\mathcal {O}(10)$$\\end{document} m3\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$^3$$\\end{document}) directional detector for rare event searches, such as nuclear recoils (NRs) induced by dark matter (DM), such as weakly interactive massive particles (WIMPs). The detector concept comprises a time projection chamber (TPC), filled with a He:CF4\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$_4$$\\end{document} 60/40 scintillating gas mixture at room temperature and atmospheric pressure, equipped with an amplification stage made of a stack of three gas electron multipliers (GEMs) which are coupled to an optical readout. The latter consists in scientific CMOS (sCMOS) cameras and photomultipliers tubes (PMTs). The maximisation of the light yield of the amplification stage plays a major role in the determination of the energy threshold of the experiment. In this paper, we simulate the effect of the addition of a strong electric field below the last GEM plane on the GEM field structure and we experimentally test it by means of a 10 ×\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\ imes $$\\end{document} 10 cm2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$^2$$\\end{document} readout area prototype. The experimental measurements analyse stacks of different GEMs and helium concentrations in the gas mixture combined with this extra electric field, studying their performances in terms of light yield, energy resolution and intrinsic diffusion. It is found that the use of this additional electric field permits large light yield increases without degrading intrinsic characteristics of the amplification stage with respect to the regular use of GEMs.

Data-driven modeling of bio-oil yield in agricultural biomass pyrolysis with machine learning

Abundant amounts of solid waste are produced due to the processing of various agricultural goods, such as garbage incineration. This agricultural waste management strategy causes a significant increase in air pollution and environmental deterioration. Several methods are widely applied to treat agricultural waste; the pyrolysis process, which includes burning biomass feedstocks without oxygen, is one of the most recommended. Pyrolysis of agricultural waste can produce biochar, bio-oil, and syngas. Pyrolysis is a complicated procedure that depends on feedstock characteristics such as feed composition (i.e., carbon, hydrogen, and oxygen elemental composition), fixed carbon, and volatile matter. Investigation of the individual property of the feedstock on the overall product yield by experimental or computational study is laborious and time-consuming. Therefore, the machine learning technique is applied in this paper to develop a robust and accurate model to predict the agricultural waste bio-oil yield. A total of 387 data points are gathered from various literature sources. This paper utilizes the proximate analysis, ultimat analysis of the agricultural feedstock, and pyrolysis reaction conditions as input features. Eight different machine-learning algorithms, i.e. adaptive boosting, artificial neural network, categorical boosting, decision tree with bagging, k nearest neighbor, light gradient boosting, random forest, and extreme gradient boosting are deployed for this objective. In addition, a SHAP analysis with a bar plot and a beeswarm plot is built to identify the input variables that had the greatest impact on bio-oil production. The categorical boost model gave the lowest root mean squared error of 2.65 out of all the models and had the greatest R2 value of 93.2% on the testing dataset. The SHAP analysis of the developed model for identifying the feature importance and the beeswarm plot is also performed, which shows that for the pyrolysis of agricultural waste, the hydrogen content is one of the most crucial parameters for the maximization of bio-oil yield.

Bang-bang optimal control in coherent spin dynamics of radical pairs in quantum biology

Optimal control of the external electromagnetic field input for the maximization of the quantum triplet-singlet yield of the radical pairs in biochemical reactions modeled by Schrödinger system with spin Hamiltonians given by the sum of Zeeman interaction and hyperfine coupling interaction terms are analyzed. Fréchet differentiability and Pontryagin Maximum Principle in Hilbert space is proved and the bang-bang structure of the optimal control is established. A closed optimality system of nonlinear differential equations for the identification of the bang-bang optimal control is revealed. Numerical methods for the identification of the bang-bang optimal control based on the Pontryagin maximum principle are developed. Numerical simulations are pursued, and the convergence and stability of the numerical methods are demonstrated. The results contribute towards understanding the structure-function relationship of the putative magnetoreceptor to manipulate and enhance quantum coherences at room temperature and leveraging biofidelic function to inspire novel quantum devices.

Optimization of methanol yield and carbon dioxide utilization in methanol synthesis process

The method of CO2 hydrogenation to methanol can effectively reduce carbon emission and has wide application prospects in chemical industry and environment. Therefore, methanol synthesis is an important way to save energy, reduce emission and transform energy utilization. In this paper, the mathematical model of methanol synthesis reactor is established. Under the condition of controllable cooling temperature, the optimization is carried out with maximum methanol yield and CO2 utilization rate. The results show that the methanol yield can be increased by 6.57% and CO2 utilization rate can be increased to 39.1% by adjusting the cooling temperature control strategy. The comparative study found that the optimal cooling temperatures all show up and down variations, with local minima and maxima. In addition, an industrial application method of methanol yield maximization cooling strategy is proposed. When the stage cooling at different temperatures is adopted, the methanol yield could be increased by 5.38%.

Demonstration on yield maximization in groundnut lead positive impact among farmers of Tamil Nadu, India

Demonstration on yield maximization in groundnut lead positive impact among farmers of Tamil Nadu, India

Bio-oil yield maximization and characteristics of neem based biomass at optimum conditions along with feasibility of biochar through pyrolysis

Bio-oil yield maximization and characteristics of neem based biomass at optimum conditions along with feasibility of biochar through pyrolysis

Multi-level decisions for addressing trade-off in the cross-regional water-environment-agriculture interactive system under constraint of water ecological carrying capacity

The complex-enhanced hierarchical relationship among multiple stakeholders in the water-environment-agriculture interactive system has been overlooked. This study develops a leader-follower-enhanced framework (named as FCMLP) that integrates variable-weight combination prediction model, multi-level programming, and fuzzy credibility constrained programming, which can effectively address the above problems under uncertainties. Five water ecological carrying capacity (WECC) statuses are treated as a critical constraint into the modeling framework to improve the accuracy of decision-making. An interactive fuzzy satisfaction algorithm is advanced for solving this multi-level problem, in which COD discharge minimization, economic benefits maximization, and grain yield maximization are taken as the upper-, middle-, and lower-level goals, respectively. The framework is applied to plan the cross-regional water-environment-agriculture interactive system in the Beijing-Tianjin-Hebei and Yangtze River Economic Belt. Solutions reveal that increased WECC status and credibility level would decrease 1.40%–1.74%, 0.71%–9.61%, and 1.63%–2.26% of water resources allocation, COD emissions, and economic benefits, respectively. Crop area and grain yield would dramatically decline by 4.13%–4.46% and 4.03%–4.67% when a credibility level increases from 0.8 to 1, respectively. The overall satisfactory degree would range from 0.58 to 0.70, which illustrates interactive decision-making process of multiple stakeholders. Significant differences can be observed in the optimized schemes of water resources allocation and environmental-economic-agricultural performances among various models. The amounts of allocated water resources, pollutant discharge, and economic output from the FCMLP model would be respectively 11.30%–13.45%, 14.90%–15.21%, and 73.12%–73.48% higher than those from the environment- and agriculture-oriented schemes, yet 13.81%, 32.05%, and 15.29% lower than those from the economy-oriented scheme. Some water adaptability countermeasures are given for ensuring the scientific operation of the South-to-North Water Transfer Project and alleviating conflicts between water source and receiving areas. Further exploration of the optimization scheme of water-environment-energy-agriculture system driven by climate change is still required for guaranteeing the dynamic balance of regional resources.

Refining flowering date enhances sesame yield independently of day-length

BackgroundThe transition from vegetative to reproductive growth is a key factor in yield maximization. Sesame (Sesamum indicum), an indeterminate short-day oilseed crop, is rapidly being introduced into new cultivation areas. Thus, decoding its flowering mechanism is necessary to facilitate adaptation to environmental conditions. In the current study, we uncover the effect of day-length on flowering and yield components using F2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$_2$$\\end{document} populations segregating for previously identified quantitative trait loci (Si_DTF QTL) confirming these traits.ResultsGenerally, day-length affected all phenotypic traits, with short-day preceding days to flowering and reducing yield components. Interestingly, the average days to flowering required for yield maximization was 50 to 55 days, regardless of day-length. In addition, we found that Si_DTF QTL is more associated with seed-yield and yield components than with days to flowering. A bulk-segregation analysis was applied to identify additional QTL differing in allele frequencies between early and late flowering under both day-length conditions. Candidate genes mining within the identified major QTL intervals revealed two flowering-related genes with different expression levels between the parental lines, indicating their contribution to sesame flowering regulation.ConclusionsOur findings demonstrate the essential role of flowering date on yield components and will serve as a basis for future sesame breeding.

Amaranthus crop for food security and sustainable food systems.

This review ascertains amaranth grains as a potent crop for food security and sustainable food systems by highlighting its agricultural advantages, health benefits and applications in the food, packaging, and brewing industry. The global population surge and rapidly transitioning climatic conditions necessitate the maximization of nutritional crop yield to mitigate malnutrition resulting from food and nutrition insecurity. The modern agricultural practices adopted to maximize the yield of the conventional staple crops are heavily contingent on the depleting natural resources and are contributing extensively to the contamination of these natural resources. Furthermore, these agricultural practices are also causing detrimental effects on the environment like rising emission of greenhouse gasses and increased water footprints. To address these challenges while ensuring sustainable nutrient-rich crop production, it is imperative to utilize underutilized crops like Amaranthus. Amaranth grains are gluten-free pseudo-cereals that are gaining much prominence owing to their abundance in vital nutrients and bio-active components, potential health benefits, resilience to adverse climatic and soil conditions, minimum agricultural input requirements, potential of generating income for small holder farmers as well as various applications across the sustainable value chain. However, due to the limited awareness of these potential benefits of the amaranth grains among the consumers, researchers, and policymakers, they have remained untapped. This review paper enunciates the nutritional composition and potential health benefits of the grains while briefly discussing their various applications in food and beverage industries and accentuating the need to explore further possibilities of valorizing amaranth grains to maximize their utilization along the value chain.

Comparison of fruit and quality parameters of tomato and capsicum by using different amount of nano ZnO through foliar and seed application

Selecting the appropriate dosage of nano zinc oxide for tomato and capsicum plants is crucial since higher concentration may negatively impact crop productivity and quality. Maintaining soil health helps in increasing crop output and quality without degrading the soil from overfertilization. The main objective of the study was to determine the physiological responses of both crops and ideal dose of nano zinc oxide (ZnO) for yield maximization and quality enhancement under controlled conditions. In present study, nano ZnO had noticeable effect on the fruit and quality parameters of both Solanaceous crops. Plants were treated with seed treatment and foliar applications of nano ZnO @ 50, 100, and 150 parts per million (ppm) along with 100% recommended dose of nutrients (RDN) through vermicompost; and 100% RDN through integration of vermicompost and fertilizers. In addition, nano zinc oxide was not supplied to the control treatment plants (T1). The results of the study revealed that 50 ppm of nano ZnO as foliar spray increased fruit and quality parameters in tomato and capsicum. In case of protein content, treatment comprised of 50 ppm of nano ZnO as seed treatment + 100% RDN through vermicompost and fertilizers, produced higher protein content over control. Furthermore, the application of nano ZnO lowered ascorbic acid levels in tomato and capsicum, while the control had the greatest levels. These results suggested that lower concentration of ZnO nanoparticles (50 ppm) is an ideal dosage and could be used in tomato and capsicum cultivation to improve yield and quality indices.

Effect of different agro-techniques on yield of Bt cotton

A field experiment was conducted during the 2 consecutive kharif seasons of 2019 and 2020 at Agronomy Instructional Farm, Chimanbhai Patel College of Agriculture, Sardarkrushinagar Dantiwada Agricultural University, Sardarkrushinagar, to study the yield maximization of Bt cotton through agro-techniques in loamy sand. The experiment consisted of four factors viz; spacing, detopping, intercropping and sowing time with two levels in each treatment. The results of pooled data of 2 years indicated that Bt cotton sown at spacing of 60 cm × 45 cm recorded significantly higher plant height, crop growth rate as well as seed cotton (3077 kg/ha) and stalk yields (5930 kg/ha), seed cotton equivalent yield (3512 kg/ha) and oil yield (335 kg/ha). However, wider spacing of 120 cm × 60 cm produced remarkably higher number of sympodial branches per plant, relative growth rate, number of open bolls per plant, weight of seed cotton per boll and seed cotton yield per plant. Topping carried out at 75 DAS remarkably improved growth and yield attributes, seed cotton and stalk yields, seed cotton equivalent yield, oil yield. All growth and yield attributes as well as seed cotton yield were recorded significantly higher without intercrop in Bt cotton. Though intercropping of greengram (additive series) in Bt cotton produced significantly higher seed cotton equivalent yield than no intercropping in pooled mean. Advance sowing of Bt cotton in last week of May produced significantly higher seed cotton yield and seed cotton equivalent S1 T2I2 D1 [Advance sowing (Last week of May)] at plant spacing of 60 cm × 45 cm along with detopping at 75 DAS and intercropping with greengram] gave significantly higher seed cotton equivalent yield as well as net returns and B: C ratio of Bt cotton.

Optimizing bio-vanillin synthesis from ferulic acid via Pediococcus acidilactici: A systematic approach to process enhancement and yield maximization

The use of lignocellulosic biomass to create natural flavor has drawn attention from researchers. A key flavoring ingredient that is frequently utilized in the food industry is vanillin. In this present study, Pediococcus acidilactici PA VIT effectively involved in the production of bio-vanillin by using Ferulic acid as an intermediate with a yield of 11.43 µg/mL. The bio-vanillin produced by Pediococcus acidilactici PA VIT was examined using FTIR, XRD, HPLC, and SEM techniques. These characterizations exhibited a unique fingerprinting signature like that of standard vanillin. Additionally, the one variable at a time method, placket Burmann method, and response surface approach, were employed to optimize bio-vanillin. Based on the central composite rotary design, the most important process factors were determined such as agitation speed, substrate concentration, and inoculum size. After optimization, bio-vanillin was found to have tenfold increase, with a maximum yield of 376.4 µg/mL obtained using the response surface approach. The kinetic study was performed to analyze rate of reaction and effect of metal ions in the production of bio-vanillin showing Km of 10.25, and Vmax of 1250 were required for the reaction. The metal ions that enhance the yield of bio-vanillin are Ca2+, k+, and Mg2+ and the metal ions that affects the yield of bio-vanillin are Pb+ and Cr+ were identified from the effect of metal ions in the bio-vanillin production.

Anaerobic Two-Phase Co-Digestion for Renewable Energy Production: Estimating the Effect of Substrate Pretreatment, Hydraulic Retention Time and Participating Microbial Consortia

Green and sustainable economies have recently become a key issue in long-term growth and well-being. Co-digestion of various waste materials in an eco-friendly way through biogas production has become the preferred method for their utilization and valorization. The possibility of hydrogen and methane yield maximization depends on the most suitable alkali reagent for pretreatment of waste lignocellulosic material, which was revealed in batch tests to determine the hydrogen production potential. The mixture for digestion consisted of pretreated wheat straw mixed with waste algal biomass in a ratio of 80:20 (w/w). The maximum hydrogen yield was achieved after applying sodium hydroxide thermoalkaline pretreatment, with a two-fold higher yield than the untreated control. Hydrogen production was stable and methane was not present in the resultant gas. The influence of the hydraulic retention time (HRT) on the maintenance of cascade installation was studied. The maximum daily concentration of hydrogen was achieved at an HRT of 2 days—42.5% H2—and the maximum concentration of methane was 56.1% at an HRT of 6 days. Accumulation of volatile fatty acids was registered in the first step and their depletion was noted in the second one. The obtained values of the cellulose content demonstrated that it was utilized by up to 2.75% in the methanogenic bioreactor at the end of the process. Metagenomics analyses revealed the bacteria Thermocaproicibacter melissae (44.9%) and Clostridium cellulosi (41.9%) participated in the consortium, accomplishing substrate hydrolysis and acidogenesis in the first stage. Less in abundance were Thermoanaerobacterium butyriciformans, Calorimonas adulescens, Pseudomonas aeruginosa and Anaerocolumna chitinilytica. Methanogenesis was performed by an archaeon closely related to Bathyarchaeota (99.5%) and Methanobacterium formicicum. The most abundant bacterial strains in the methanogenic fermenter were Abyssalbus ytuae (30%), Proteiniphilum acetatigenes (26%) and Ruficoccus amylovorans (13%).

A comparative study of membrane reactor and cylindrical reactor using multi-objective optimization for methane reforming process

Multi-objective optimization (MOO) of methane reforming process has been performed for hydrogen and oxygen perm-selective membrane reactor (MR) considering three objectives, namely H2 yield maximization, CO2 minimization and reactor length minimization. The performance of the MR has been compared with that of the cylindrical reactor (CR) using the same objective functions. Four MOO problems have been formulated and solved in this work. Among these, three problems consist of 2-objectives, while one has 3-objectives. Comparative results and discussions of the two reactors are presented based on the multi-objective pareto curve analysis and the trends of decision variables. The results in this work indicate that MR performs better than CR in all the four multi-objective scenarios. We further optimize the reactor with the above mentioned all the three objectives to produce syngas with different (H2/CO) ratios in the product stream. This is especially useful for producing the syngas targeting a specific end application.

Geographical adaptability for optimizing the recommendation of soybean cultivars in the Brazilian Cerrado

Yield multi-location trials associated to geostatistical techniques with environmental covariables can provide a better understanding of G x E interactions and, consequently, adaptation limits of soybean cultivars. Thus, the main objective of this study is understanding the environmental covariables effects on soybean adaptation, as well as predicting the adaptation of soybean under environmental variations and then recommend each soybean cultivar to favorable environments aiming maximize the average yield. The trials were carried out in randomized block design (RBD) with three replicates over three years, in 28 locations. Thirty-two genotypes (commercial and pre-commercial) representing different maturity groups (7.5–8.5) were evaluated in each trial were covering the Edaphoclimatic Region (REC) 401, 402 and 403. The covariables adopted as environmental descriptors were accumulated rainfall, minimum temperature, mean temperature, maximum temperature, photoperiod, relative humidity, soil clay content, soil water avaibility and altitude. After fitting means through Mixed Linear Model, the Regression-Kriging procedure was applied to spacialize the grain yield using environmental covariables as predictors. The covariables explained 32.54% of the GxE interaction, being the soil water avaibility the most important to the adaptation of soybean cultivars, contributing with 7.80%. Yield maps of each cultivar were obtained and, hence, the yield maximization map based on cultivar recommendation was elaborated.

Yield Maximization of Rice (Oryza sativa) through Integrated Nutrient Management

This experiment was conducted on a permanent layout at the west byde of BRRI farm, Gazipur during T. Aman 2019 to Boro 2021-22 to find out the suitable management practices for yield maximization of rice and soil health. Seven treatments in Randomized Complete Block (RCB) Design with three replications were imposed and each treatment was assigned in 5m × 4m sized plot. The treatments combinations were T1= Absolute Control (No nutrient supply), T2 = BRRI dose N-P-K-S@83-17-53-12 kg ha-1 in T. Aman and 138-21-75-21 kg ha-1 in Boro season, T3= Soil Test Based (STB) Fertilizer Dose N-P-K-S @ 67-10-40-10 kg ha-1 in T. Aman and 134-16-75-10 kg ha-1 in Boro, T4 = STB dose + 1 t ha-1 Cowdung, T5 = STB dose + 1 t ha-1 Poultry manure, T6 = STB dose + 1 t ha-1 Vermicompost and T7 = STB dose + 0.33 t ha-1 CD + 0.33 t ha-1 PM + 0.33 t ha-1 VC. Thirty-days old seedling of BRRI dhan87 in T. Aman and fourty-two days old seedling of BRRI dhan89 in Boro season were transplanted at 20 cm × 20 cm spacing in both seasons. All manures, soil and plant samples analysis were done by the help of soil Science Division BRRI, Gazipur. Initial soil (0-15 cm depth) properties were: soil texture, clay loam; pH, 6.94; organic Carbon, 1.59%; Nitrogen, 0.18%; Phosphorus, 21.88 ppm and Potassium, 0.19meq/100g soil. Thirty days old seedling of BRRI dhan87 in T. Aman and 42 days old seedling of BRRI dhan89 in Boro season were transplanted at 20cm x 20cm spacing. Grain yield, tiller number, panicle number, plant height and grain number were significantly affected by the different integrated nutrient management during both T. Aman and Boro season. Grain yield, tiller number, panicle number, plant height and grain number were significantly affected by the different nutrient management in both T. Aman and Boro season. Poultry manure related treatments and BRRI recommended dose performed better than the others in all the parameter except 1000-grain weight. On the other hand, Absolute Control (No nutrient supply) produced the lowest result. Every parameter, Poultry manure treatments have been performed the best. STB dose with one t ha-1 poultry manure is better for maximization of rice yield.

Maximization of brackish water productivity for the sustainable production of striped catfish (Pangasianodon hypophthalmus) and grain sorghum (Sorghum bicolor (L.) Moench) cultivated under an integrated aquaculture–agriculture system

Freshwater scarcity, salinity, and poor soil fertility are the major challenges affecting both food and feed productions in arid and semi-arid regions of the world. Utilization of brackish water in the production of saline-tolerant fish and valuable field crops under an integrated system is promising in the maximization of yield per crop. The aim of this study, therefore, was to (1) assess the effect of saline aquaculture wastewater on the growth, yield, forage quality, and nutritive composition of sorghum seeds and (2) assess the effect of different water qualities on the survival, growth performance, and health status of Pangasianodon hypophthalmus. The experiment was conducted in a randomized completely block design of four salinity treatments with three replicates, i.e., control (freshwater mixed with inorganic fertilizers), 5000 ppm, 10,000 ppm, and 15,000 ppm. Our results indicated that although the control exhibited the highest growth (plant height, leaf number, internode number, leaf area, and soil–plant analysis development), grain, and forage yield, no significant differences were noted among the treatments. Likewise, no significant difference in the grain nutrient composition was noted among all the treatments. Assessment of the forage quality revealed improved crude protein content in the control compared to the saline treatments. However, no significant differences in the leaves and stalks fiber fractions were noted among all the treatments. Furthermore, rumen fermentation in terms of in vitro digestibility indicated no significant differences in the in vitro digestible dry matter, digestible organic matter, metabolic energy, net energy, microbial protein, short-chain fatty acids, and total dissolved nutrients among the treatments. However, rearing P. hypophthalmus in water salinities exceeding 10,000 ppm reduced the growth performance and health status of fish. Therefore, the integration of sorghum and P. hypophthalmus production in water salinities not exceeding 5000 ppm is a viable alternative to maximize brackish water productivity in freshwater-scarce regions.

The future of wheat yield maximization: Integrating advancements and addressing challenges

The future of wheat yield maximization: Integrating advancements and addressing challenges

Deep Learning: A Climate Smart Agriculture Tool for Groundnut Farmers

Groundnut is an oil seed crop, which is grown widely in the country, and approximately 80% of groundnut is produced in the rainfed condition. Unlike weather factors even the prices of agriculture commodities are volatile in nature and the groundnut prices also behaves in an unusual pattern. A traditional farmer faces recurrent challenges. Deep learning methods and the accessibility of satellite imagery have, however, created new opportunities for more accurate and effective agricultural yield estimates. Large-scale yield estimation and understanding the impact of the variability of agricultural growing circumstances are critical due to the increased frequency of extreme climate occurrences. Crop growth condition models can be utilized with time series of spatially explicit information from satellite remote sensing (RS). For efficient agricultural management, guaranteeing food security, and making wise decisions about resource allocation and market forecasting, accurate and timely estimation of crop yields is essential. Crop yield estimation has historically depended on time-consuming field surveys and statistical models. Machine Learning (ML) is an exciting application of Artificial Intelligence. It provides the ability to learn by experiences without any explicit. The proposed model is based on simple\and cost-effective hardware that can be used by agriculture officers and farmers to get good productivity of crops. SCS model is trained by classifying dataset and tested subsequently. The accuracy and performance of an ML classifier depend only on the type and size of the dataset. Crop selection by real-time sensing data and soil analysis attributes is a big contribution in research of smart agriculture. A model was proposed basing on three modules: crop selection, crop management, and crop maturity. It used parameters soil moisture, temperature, humidity, air pressure, and air quality with weather conditions for better crop selection and health monitoring. A real-time sensory data was used for analysis on Thing Speak application with KNN algorithm. Some data mining techniques are applied for data preprocessing and comparing real-time data with trained data for crop prediction. It also considered crop prices for crop prediction, listed on National Commodity and Derivative Exchange. The KNN classifier is applied for data analysis has focused on IoT-oriented agricultural methods for weather monitoring. The prediction methods are investigated for commercial and scientific perspectives, cost of IoT components, Think Speak application is used for data analysis. An Android application is also designed to intimate the farmers about required water level of fields. IoT framework best use of land to improve farming methods and increase crop production with profit maximization. A wireless sensor network was deployed in the field to sense data for different parameters and for proper monitoring of field. It proposed a crop prediction method for crop yield maximization and quality of crops by considering real-time data of metrological factors using ML algorithms: precipitation, temperature, humidity, and solar light. Soft computing techniques can be employed to estimate the yield of various crops. As a result of rapid advancements in technology, crop models and decision tools have emerged as vital components of precision agriculture worldwide. These models and tools utilize linear regression techniques, nonlinear simulations, expert systems, Adaptive Neuro-Fuzzy Interference Systems, Support Vector Machines, Data Mining, Genetic Programming, and Artificial Neural Network (ANN) to predict harvest outcomes particularly under the influence of climate change. These prediction methods play a significant role in improving the accuracy and reliability of yield estimation in agricultural systems. ANNs successfully address identification, classification, and regression challenges in crop disease identification, harvest mechanization and product quality sorting. Multiple linear regression and discriminant function analysis were employed to construct a groundnut yield forecasting model, utilizing weather indices including maximum temperature, minimum temperature, total rainfall, morning relative humidity, and evening relative humidity. Employing techniques such as stepwise multiple linear regression, principal component analysis was combined with stepwise multiple linear regression, ANN, and penalized regressions like least absolute shrinkage and selection operator and elastic net. The models, particularly least absolute shrinkage and selection operator and elastic net, demonstrated remarkable accuracy, boasting a normalized root mean square error of under 10% across most test locations. Farmers using traditional methods in agriculture face problems such as low crop yield due to unpredicted weather, wrong amount of water and nutrients, and wrong selection of crop. In previous research work, limited parameters were used that are insufficient for high yield of crops. Our research is aimed at maximizing the crop yield by selecting suitable crop. We tackle this issue by applying technology methodically and evidencebased analysis. For instance, adding required amount of nutrients gives improved yields. Our work is based on selection of the influential parameters. Deep learning techniques used to give improved accuracy with less computational cost as compared to previous research.

An Analysis of Fixed-Bed Catalytic Reactors Performances for One-Stage Butadiene Synthesis from Ethanol

The present study assesses, using a theoretical approach, the performances of two widely used fixed-bed reactors for the ethanol-to-butadiene (ETB) one-stage (Lebedev) process: multibed adiabatic reactors (MBAR) with inter-bed heating and a multitubular reactor (MTR). The mathematical model consists of mass and heat conservation equations at catalyst particle and particle bed scales, coupled with a published kinetic model specific for a modified MgO–SiO2 catalyst. The process simulations at the level of catalyst particle have shown that the inter-phase concentration and temperature gradients are negligible, the only physical step with a limiting influence on process kinetics being the internal diffusion. A simplified version of the reactor model was also formulated, including empirical expressions developed in the study for the calculation of internal effectiveness factors. The reactor simulations highlighted that the MBAR with inter-bed heating by injection of hot reactants provides worse butadiene (BD) yields as a consequence of reduced reaction times (due to reactant by-pass of catalyst beds). The MBAR with inter-bed heating using heat exchangers provides better performances but inferior to those of MTR if the number of catalyst beds is less than six. A preliminary optimization study for the MBAR and MTR is also presented. The results showed that the maximization of BD yield has the drawback of a low BD production rate, whereas the maximization of BD production rate corresponds to a relatively low BD yield and high ethanol and inert recycles. A trade-off between the two extreme cases can be attained by maximizing the income generated by reactor operation.