Modification of nutrient requirements for a four Crop-Based cropping system to increase system productivity, maintain soil fertility, and achieve sustainable intensification

Sustainable and resilient cropping intensity is now a global focus to address the food demand and nutrition security of the growing population. For sustainable intensification, maintaining soil fertility is a key concern. The nutrient management for the recently developed four crop-based cropping system in Bangladesh has not yet been studied. Hence, field experiments were conducted on the nutrient management of the four crop-based cropping system [Aus (pre-monsoon rice), Aman (monsoon rice), lentil, and mungbean] in calcareous soil in Bangladesh during the years of 2016/17 and 2017/18 to determine the appropriate fertilizer management package to improve crop productivity and sustain soil fertility. The experiment had six treatments assigned in a randomized complete block design with three replications. The treatments included T1 = control (without synthetic fertilizer), T2 = 50% recommended dose of fertilizer (RDF), T3 = 75% RDF, T4 = 100% RDF, T5 = 125% RDF, and T6 = farmers’ practice (FP). The results revealed that the 125% RDF significantly contributed to higher yields of all four crops. The rice equivalent yield (REY) was the highest for the fertilizer management of 125% RDF, which was 45.5%, 9.4%, and 12.2% higher than the control (T1), 100% RDF (T4), and FP, respectively. Considering the uptake of nutrients (N, P, K, S, Zn, and B) by the crops in the cropping system, the 125% RDF was superior to the other treatments. The nutrient management practices had a positive influence on the apparent nutrient recovery (ANR) efficiency of the cropping system. The fertilizer management of 125% RDF was also economically more profitable due to the increment in the cost–benefit ratio of 26.8%, 4.4%, and 4.9% over the control, 100% RDF, and FP, respectively. The results indicate that the current fertilizer recommendations and FP for aus, aman, lentil, and mungbean are not adequate for the change from the three crop to the four crop-based pattern, and an increased dose of fertilizer is required to increase the yield of each individual crop as well as the total system’s productivity. The fertilizer use efficiency is also higher for 125% RDF than the 100% RDF and FP indicating that to sustain the soil fertility in the four crop-based system, the current RDF and FP are not sufficient. This finding will help intensive cropping areas in preventing nutrient deficiencies that would lead to a reduction in the crop yield

Autonomous Driving Vehicle System using LiDAR sensor

An overview of light detection and ranging (LiDAR) sensor technology for autonomous vehicles is presented in this paper. The sensor called LiDAR sensors is a key component of autonomous driving’s for the upcoming generation as an assistance function. LiDAR technology is discussed, including its characteristics, a technical overview, prospects as well as limitations in relation to other sensors available in the industry. Comparison and comment on sensor quality are based on factory parameters. The basic components of a LiDAR system from the laser transmitter to the beam scanning mechanism are explained

Canopy spectral reflectance indices correlate with yield traits variability in bread wheat genotypes under drought stress

Drought stress is a major issue impacting wheat growth and yield worldwide, and it is getting worse as the world’s climate changes. Thus, selection for drought-adaptive traits and drought-tolerant genotypes are essential components in wheat breeding programs. The goal of this study was to explore how spectral reflectance indices (SRIs) and yield traits in wheat genotypes changed in irrigated and water-limited environments. In two wheat-growing seasons, we evaluated 56 preselected wheat genotypes for SRIs, stay green (SG), canopy temperature depression (CTD), biological yield (BY), grain yield (GY), and yield contributing traits under control and drought stress, and the SRIs and yield traits exhibited higher heritability (H2) across the growing years. Diverse SRIs associated with SG, pigment content, hydration status, and aboveground biomass demonstrated a consistent response to drought and a strong association with GY. Under drought stress, GY had stronger phenotypic correlations with SG, CTD, and yield components than in control conditions. Three primary clusters emerged from the hierarchical cluster analysis, with cluster I (15 genotypes) showing minimal changes in SRIs and yield traits, indicating a relatively higher level of drought tolerance than clusters II (26 genotypes) and III (15 genotypes). The genotypes were appropriately assigned to distinct clusters, and linear discriminant analysis (LDA) demonstrated that the clusters differed significantly. It was found that the top five components explained 73% of the variation in traits in the principal component analysis, and that vegetation and water-based indices, as well as yield traits, were the most important factors in explaining genotypic drought tolerance variation. Based on the current study’s findings, it can be concluded that proximal canopy reflectance sensing could be used to screen wheat genotypes for drought tolerance in water-starved environments. Copyright 2022 Mohi-Ud-Din et al.Open access journalThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]

Study on Advanced Image Processing Techniques for Remote Sensor Data Analysis

Image processing is the act of altering photographs with arithmetic computations for a variety of objectives including erasing clean lines, dividing groups of objects, noise removal from photos, detecting edges, and so on. Remote sensing is a method for observing the planet's surface or environment through satellites or from the air (via airplanes). Electromagnetic radiation reflected or radiated by the earth's surface is recorded using this approach. Numerous techniques and technologies can be used in remote sensing to monitor electromagnetic waves of different wave lengths, including visual, infrared, heat infrared, and so forth. In this study, digital cameras are utilized to acquire photos in the visible range and then used advanced image processing and filtering algorithms to the raw data. Homomorphic filtering, median filtering, Gaussian filtering, histogram-based thresholding, contrast stretching, and other techniques are used. The results of this research will be valuable for enhanced picture analysis