Comprehensive Account of Inoculation and Coinoculation in Soybean

This chapter elaborates dependency of leguminous plants on rhizobia to carry out dynamic process of nitrogen fixation. Soybean, an extensively grown leguminous crop with 30% share in world’s vegetable oil, is taken into account to understand its symbiotic relationship with plant growth-promoting rhizobacteria (PGPRs). This chapter narrates colonization of PGPRs on soybean roots and single and mixed inoculation and coinoculation of certain strains of specialized bacteria with rhizobia. PGPRs’ coinoculation seemed more effective than mono-inoculation and is discussed in Ref. to nodulation rate. Moreover, dynamic linear models for quantification of leguminous biological nitrogen fixation (BNF) are reviewed. This chapter further uncoils the relevance of foliar application to the release of phytohormones by PGPRs, resulting in situ biosynthesis of active metabolites in phyllosphere. Inoculation of phytohormones is compared to their exogenous application for nodule organogenesis. Finally, the influence of coinoculation on enhanced micronutrient bioavailability is relayed. The chapter is concluded with technical and economic aspects of coinoculation in soybean

Unmanned surface vehicle for intelligent water quality assessment to promote sustainable human health

Deteriorating water quality poses significant health risks globally, with billions at risk of waterborne diseases due to contamination. Limited data on water quality heightens these risks as conventional monitoring methods lack comprehensive coverage. While technologies like Internet of Things and machine learning offer real-time monitoring capabilities, they often provide point data insufficient for assessing entire water bodies. Remote sensing, though useful, has limitations such as measuring only optical parameters and being affected by climate and resolution issues. To address these challenges, an unmanned surface vehicle named ‘AquaDrone’ has been developed. AquaDrone traverses water bodies, collecting data on four key parameters (pH, dissolved oxygen, electrical conductivity, and temperature) along with GPS coordinates. These data are transmitted to a web portal via LoRa communication and Wi-Fi, where visualizations like trendlines and color-coded heatmaps are generated. A multilayer perceptron classifies water quality into five categories, aiding in real-time assessment. The AquaDrone system offers a feasible solution for monitoring small to medium-sized water bodies, crucial for safeguarding public health

Bioinoculants in Technological Alleviation of Climatic Stress in Plants

Global climate change is leading to a series of frequent onset of environmental stresses such as prolonged drought periods, dynamic precipitation patterns, heat stress, and cold stress on plants and commercial crops. The increasing severity of such stresses is not only making agriculture and related economic sector vulnerable but also negatively influences plant diversity patterns. The global temperature of planet Earth has risen to 1.1°C since the last 19th century. An increase in surface temperature leads to an increase in soil temperature which ultimately reduces water content in the soil, thereby, reducing crop growth and yield. Moreover, this situation is becoming more intense for agricultural practices in arid and semi-arid regions. To overcome climatically induced stresses, acclimatization of plant species via bioinoculation with Plant Growth Promoting Rhizobacteria (PGPR) is becoming an effective approach. The PGPR are capable of colonizing rhizosphere (exophytes) as well as plant organs (endophytes), where they trigger an accumulation of osmolytes for osmoregulation or improving gene expression of heat or cold stress proteins, or by signaling the synthesis of phytohormones, metabolites, proteins, and antioxidants to scavenge reactive oxygen species. Thus, PGPR exhibiting multiple plant growth-promoting traits can be employed via bioinoculants to improve the plant’s tolerance against unfavorable stress conditions

How IoT-Driven Citizen Science Coupled with Data Satisficing Can Promote Deep Citizen Science

To study and understand the importance of Internet of Things-driven citizen science (IoTCS) combined with data satisficing, we set up and undertook a citizen science experiment for air quality (AQ) in four Pakistan cities using twenty-one volunteers. We used quantitative methods to analyse the AQ data. Three research questions (RQ) were posed as follows: Which factors affect CS IoT-CS AQ data quality (RQ1)? How can we make science more inclusive by dealing with the lack of scientists, training and high-quality equipment (RQ2)? Can a lack of calibrated data readings be overcome to yield otherwise useful results for IoT-CS AQ data analysis (RQ3)? To address RQ1, an analysis of related work revealed that multiple causal factors exist. Good practice guidelines were adopted to promote higher data quality in CS studies. Additionally, we also proposed a classification of CS instruments to help better understand the data quality challenges. To answer RQ2, user engagement workshops were undertaken as an effective method to make CS more inclusive and also to train users to operate IoT-CS AQ devices more understandably. To address RQ3, it was proposed that a more feasible objective is that citizens leverage data satisficing such that AQ measurements can detect relevant local variations. Additionally, we proposed several recommendations. Our top recommendations are that: a deep (citizen) science approach should be fostered to support a more inclusive, knowledgeable application of science en masse for the greater good; It may not be useful or feasible to cross-check measurements from cheaper versus more expensive calibrated instrument sensors in situ. Hence, data satisficing may be more feasible; additional cross-checks that go beyond checking if co-located low-cost and calibrated AQ measurements correlate under equivalent conditions should be leveraged

Precision Agriculture Techniques and Practices: From Considerations to Applications

Internet of Things (IoT)-based automation of agricultural events can change the agriculture sector from being static and manual to dynamic and smart, leading to enhanced production with reduced human efforts. Precision Agriculture (PA) along with Wireless Sensor Network (WSN) are the main drivers of automation in the agriculture domain. PA uses specific sensors and software to ensure that the crops receive exactly what they need to optimize productivity and sustainability. PA includes retrieving real data about the conditions of soil, crops and weather from the sensors deployed in the fields. High-resolution images of crops are obtained from satellite or air-borne platforms (manned or unmanned), which are further processed to extract information used to provide future decisions. In this paper, a review of near and remote sensor networks in the agriculture domain is presented along with several considerations and challenges. This survey includes wireless communication technologies, sensors, and wireless nodes used to assess the environmental behaviour, the platforms used to obtain spectral images of crops, the common vegetation indices used to analyse spectral images and applications of WSN in agriculture. As a proof of concept, we present a case study showing how WSN-based PA system can be implemented. We propose an IoT-based smart solution for crop health monitoring, which is comprised of two modules. The first module is a wireless sensor network-based system to monitor real-time crop health status. The second module uses a low altitude remote sensing platform to obtain multi-spectral imagery, which is further processed to classify healthy and unhealthy crops. We also highlight the results obtained using a case study and list the challenges and future directions based on our work

Attitude Determination by Exploiting Geometric Distortions in Stereo Earth Images.

The growing interest in the development of small satellites and the demand for high resolution imaging has made the pointing and drift rate requirements of a satellite more stringent. To achieve high pointing accuracy star sensors can be used but their cost are too large for small satellites. The need for keeping the overall cost of the spacecraft down and still achieve adequate pointing accuracies has provoked the development of relatively inexpensive and high performance attitude systems that can provide competitive pointing accuracies during imaging operations. In order to realize such an attitude system, this research has exploited the offsets present between pairs of ground images for determining the orientation of the spacecraft. The approach is based on a pair of canted Earth pointing pushbroom sensor. The configuration of the sensors induces temporal and perspective distortions in the images. These geometric distortions give rise to interimage offsets. A mathematical model has been developed to establish a relationship between the attitude and the image offsets. The mathematical modelling of the attitude system is based on the camera system of DMC satellites. However, this model is also applicable to other satellites with similar sensor configuration. To evaluate the performance of the attitude model with images, simulated satellite images have been created with known ground truth. These simulated images have been generated by an extension of the attitude model. The offsets between the images have been found in Fourier domain using Singular Value Decomposition (SVD) factorization. To determine the attitude from the image shifts, the attitude model has been inverted. In order to quantify the model with images, attitude has been estimated from a number of synthetic images generated with an angular separation, α, of 0.045° between the sensors. It has been found that with an α of 0.045° between the sensors, this algorithm gives an accuracy of ±0.16°(1σ). However, increasing the angular separation to 0.5°, the accuracy of 0.034°(1σ) could be attained. The attitude model has also been tested with different experimental images of DMC satellites and the results are compared with the onboard ADCS data. The onboard vibrations are also determined for UK DMC, ALSat-1 and Nigeria Sat. The experimental results have shown the potential of the image based attitude model to complement the need for low cost, low mass, and highly accurate attitude system for small satellites

Attitude Determination by Exploiting Geometric Distortions in Stero Earth Images

EThOS - Electronic Theses Online ServiceGBUnited Kingdo

An Enhanced Water Quality Index for Water Quality Monitoring Using Remote Sensing and Machine Learning

Water quality deterioration is a serious problem with the increase in the urbanization rate. However, water quality monitoring uses grab sampling of physico-chemical parameters and a water quality index method to assess water quality. Both processes are lengthy and expensive. These traditional indices are biased towards the physico-chemical parameters because samples are only collected from certain sampling points. These limitations make the current water quality index method unsuitable for any water body in the world. Thus, we develop an enhanced water quality index method based on a semi-supervised machine learning technique to determine water quality. This method follows five steps: (i) parameter selection, (ii) sub-index calculation, (iii) weight assignment, (iv) aggregation of sub-indices and (v) classification. Physico-chemical, air, meteorological and hydrological, topographical parameters are acquired for the stream network of the Rawal watershed. Min-max normalization is used to obtain sub-indices, and weights are assigned with tree-based techniques, i.e., LightGBM, Random Forest, CatBoost, AdaBoost and XGBoost. As a result, the proposed technique removes the uncertainties in the traditional indexing with a 100% classification rate, removing the necessity of including all parameters for classification. Electric conductivity, secchi disk depth, dissolved oxygen, lithology and geology are amongst the high weighting parameters of using LightGBM and CatBoost with 99.1% and 99.3% accuracy, respectively. In fact, seasonal variations are observed for the classified stream network with a shift from 55:45% (January) to 10:90% (December) ratio for the medium to bad class. This verifies the validity of the proposed method that will contribute to water management planning globally

An Enhanced Water Quality Index for Water Quality Monitoring Using Remote Sensing and Machine Learning

Water quality deterioration is a serious problem with the increase in the urbanization rate. However, water quality monitoring uses grab sampling of physico-chemical parameters and a water quality index method to assess water quality. Both processes are lengthy and expensive. These traditional indices are biased towards the physico-chemical parameters because samples are only collected from certain sampling points. These limitations make the current water quality index method unsuitable for any water body in the world. Thus, we develop an enhanced water quality index method based on a semi-supervised machine learning technique to determine water quality. This method follows five steps: (i) parameter selection, (ii) sub-index calculation, (iii) weight assignment, (iv) aggregation of sub-indices and (v) classification. Physico-chemical, air, meteorological and hydrological, topographical parameters are acquired for the stream network of the Rawal watershed. Min-max normalization is used to obtain sub-indices, and weights are assigned with tree-based techniques, i.e., LightGBM, Random Forest, CatBoost, AdaBoost and XGBoost. As a result, the proposed technique removes the uncertainties in the traditional indexing with a 100% classification rate, removing the necessity of including all parameters for classification. Electric conductivity, secchi disk depth, dissolved oxygen, lithology and geology are amongst the high weighting parameters of using LightGBM and CatBoost with 99.1% and 99.3% accuracy, respectively. In fact, seasonal variations are observed for the classified stream network with a shift from 55:45% (January) to 10:90% (December) ratio for the medium to bad class. This verifies the validity of the proposed method that will contribute to water management planning globally

Analysis of meteorological variations on wheat yield and its estimation using remotely sensed data. A case study of selected districts of Punjab Province, Pakistan (2001-14)

Land management for crop production is an essential human activity that supports life on Earth. The main challenge to be faced by the agriculture sector in coming years is to feed the rapidly growing population while maintaining the key resources such as soil fertility, efficient land use, and water. Climate change is also a critical factor that impacts agricultural production. Among others, a major effect of climate change is the potential alterations in the growth cycle of crops which would likely lead to a decline in the agricultural output. Due to the increasing demand for proper agricultural management, this study explores the effects of meteorological variation on wheat yield in Chakwal and Faisalabad districts of Punjab, Pakistan and used normalised difference vegetation index (NDVI) as a predictor for yield estimates. For NDVI data (2001-14), the NDVI product of Moderate Resolution Imaging spectrometer (MODIS) 16-day composites data has been used. The crop area mapping has been realised by classifying the satellite data into different land use/land covers using iterative self-organising (ISO) data clustering. The land cover for the wheat crop was mapped using a crop calendar. The relation of crop yield with NDVI and the impact of meteorological parameters on wheat growth and its yield has been analysed at various development stages. A strong correlation of rainfall and temperature was found with NDVI data, which determined NDVI as a strong predictor of yield estimation. The wheat yield estimates were obtained by linearly regressing the reported crop yield against the time series of MODIS NDVI profiles. The wheat NDVI profiles have shown a parabolic pattern across the growing season, therefore parabolic least square fit (LSF) has been applied prior to linear regression. The coefficients of determination (R2) between the reported and estimated yield was found to be 0.88 and 0.73, respectively, for Chakwal and Faisalabad. This indicates that the method is capable of providing yield estimates with competitive accuracies prior to crop harvest, which can significantly aid the policy guidance and contributes to better and timely decisions