Comparative studies of knapsack, boom, and drone sprayers for weed management in soybean (Glycine max L.).

The study titled, "Comparative Evaluation of Knapsack, Boom, and Drone Sprayers for Weed Management in Soybean (Glycine max L.)" was carried out during the Kharif season 2021-22 at an experimental farm affiliated with the Department of Agronomy, Vasantrao Naik Marathwada Krishi Vidyapeeth, Parbhani. The primary objective was to evaluate the comparative efficacy of various sprayers in controlling weeds in soybeans and their work efficiency. The Randomized Block Design (RBD) included ten treatments of pre-emergence (PE) and post-emergence (POE) herbicides applied by knapsack, boom, and drone sprayers. Pendimethalin 30% EC @ 750 g a.i ha-1 was used for pre-emergence herbicide application, and Imazamox 35% EC + Imazethapyr 35% WG @ 70 g a.i ha-1 were used for post-emergence. These treatments were tested on soybean Monocot and Dicot weed count, weed dry weight, weed index, and weed control efficiency. The sprayers were compared for time, water, labor, herbicide, and overall work efficiency. A knapsack sprayer showed the best results for pre- and post-emergence herbicide application, with the lowest weed count, dry weight, control efficiency, and weed index. Boom and drone sprayers followed in effectiveness. Herbicide application was faster with the drone sprayer than with hand weeding, cultural practices, boom sprayer, and knapsack sprayer. Compared to knapsack and boom sprayers, the drone sprayer used less water and labour. Drone sprayers work most efficiently, followed by boom and knapsack sprayers. This study focuses on the prevalence of herbicides and their impact on non-target ecosystems. It aims to develop mitigation strategies by optimizing spraying efficiency and reducing herbicide usage during pre and post emergence. The dissemination of efficient weed management practices that reduce environmental impacts and increase the efficiency of soybean cultivation is consistent with Sustainable Development Goal 15: life on land.

Optical and electrical characteristics of perovskite solar cells incorporating MoO3 as an active layer.

Perovskite-based solar cell technologies have sparked much interest in recent decades. A solar cell's efficiency is an essential factor in developing a highly efficient device. The power conversion efficiency (PCE) of Perovskite-based solar cells can be enhanced by adding new materials to the photon-absorbing layer and altering the electron and hole transport layers. Titanium dioxide (TiO2) is commonly used in electron transport layers (ETLs), but it has been shown that replacing TiO2 with molybdenum trioxide (MoO3) improves PCE. We use the OghmaNano software to simulate a perovskite-based solar cell and investigate the PCE for TiO2 and MoO3 ETL layers by altering their thickness. The influence of electron and hole drift diffusion, carrier continuity equations in the position space to describe charge flow within the device, Poisson's equation, and charge carrier recombination have all been investigated in the context of solar cell simulation. It was observed that by substituting the ETL layer of TiO2 with MoO3 in the device, the PCE significantly increases.

Artificial neural network based models for predicting the effluent quality of a combined upflow anaerobic sludge blanket and facultative pond: Performance evaluation and comparison of different algorithms.

The main objective of this work was to test different artificial neural network (ANN) based models, i.e. the ANN feed forward back propagation (ANN-FFBP), deep feed forward backpropagation (DFFBP), and deep cascade forward back propagation (DCFBP) models, for predicting the effluent quality of an upflow anaerobic sludge blanket-facultative pond (UASB-FP) system. The overall removal efficiency in the UASB-FP was >84% at organic loading rates of ∼26 kg d-1. The chemical oxygen demand (COD), ammonical nitrogen (AN), total suspended solids (TSS), biochemical oxygen demand (BOD), total Kjeldahl nitrogen (TKN), and total phosphorus (TP) were inputs to each model, while the water quality characteristics of the UASB-FP effluent was used as the output. The dataset of 180 samples, collected over a one-year period, was utilized to train, test, and validate the developed models. Compared to ANN-FFBP and DFFBP, the DCFBP network demonstrated the strongest capacity for prediction. The correlation coefficient RTrain and the root-mean-squared error (RMSE) for the selected DCFBP model (3 hidden layers and 11 neurons/layer) in the training data set were 0.997 and 6.018, respectively. The sensitivity analysis of the DCFBP model shows that the model's performance is very sensitive to BOD followed by AN, COD, TP, TSS and TKN, respectively. The results of this study will be helpful to wastewater treatment (WWTP) plant managers in their pursuit of data-driven UASB-FP based WWTP management.

Prediction of effluent quality in ICEAS-sequential batch reactor using feedforward artificial neural network.

It is highly essential that municipal wastewater is treated before its discharge and reuse in order to meet the standard requirements for safe marine life and for farming and industries. It is beneficial to use reclaimed water, since availability of fresh water is inadequate. An investigation was conducted on the Jamnagar Municipal Corporation Sewage Treatment Plant (JMC-STP) to develop a feedforward artificial neural network (FF-ANN) model. It is an alternate for the modelling/ prediction of JMC-STP to circumvent over the versatile physical, chemical, and biological treatment process simulations. The models were developed to predict effluent quality parameters through influent characteristics. The parameters are pH, biochemical oxygen demand (BOD), chemical oxygen demand (COD), total suspended solids (TSS), total Kjeldahl nitrogen (TKN), ammonium nitrogen (AN) and total phosphorus (TP). The correlation coefficient RTRAINING and RALL were calculated for all parametric models. The MAD (mean absolute deviation), MSE (mean square error), RMSE (root mean square error) and MAPE (mean absolute percentage error) were evaluated for FF-ANN models. This proves to be a useful tool for the plant management to optimize the treatment quality as it enhances the performance and reliability of the plant. The simulation results were validated through the measured values.

Experimental investigation on the effect of soil solarization incorporating black, silver, and transparent polythene, and straw as mulch, on the microbial population and weed growth.

Soil solarization is a non-chemical method for eliminating crop-threatening weeds and selectively decontaminating soil. The effect of various soil solarization with black, silver, transparent polythene sheet and straw as mulching on the microbial count and weed growth was studied experimentally. The farm investigation comprised six soil solarization treatments: mulching with black, silver, and transparent polyethylene sheets of 25 µm (µ), organic mulch (soybean Straw), weed-free, and control. All six treatments were repeated in four sets in a Randomized Block Design (RBD) plot size of 5.4 m × 4.8 m. Black, silver, and transparent polythene mulches significantly reduced fungal count compared to non-solarized soil. Straw mulch significantly increased soil fungal count. Solarized treatments had much lower bacterial populations than straw mulch, weed-free, and control treatments. Black, silver, straw mulch, and transparent polythene had 18746, 22763, 23999, and 3048 weeds ha-1 at 45 days after transplanting (DAT). The analysis of dry weed weight demonstrated a significantly low weed dry weight of 0.44 t/ha for black polythene (T1) based soil solarization, representing an 86.66% reduction in dry weed biomass. Soil solarization had the lowest weed index (WI), with black polythene mulch (T1) reducing weed competition. Among various soil solarization treatments, black polythene (T1) showed the highest weed control efficiency of 85.84%, indicating its suitability for use in weed control. The results suggest that soil solarization is effective for soil disinfestation and weed control with polyethene mulch and summer heat in central India.