This article is an open access article distributed under the terms and conditions of the Creative... more This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY
In this paper, the names of the fifth author and 12th author were incorrectly spelled. The correc... more In this paper, the names of the fifth author and 12th author were incorrectly spelled. The correct spelling of the authors' names are Niharika Sheoran and Sheetal Dhariwal, respectively. The corrected version of author list is as follows. The authors would like to apologize for any inconvenience caused.
Plant pathology journal/The plant pathology journal, Feb 1, 2024
The oldest and most extensively cultivated form of millet, known as pearl millet (Pennisetum glau... more The oldest and most extensively cultivated form of millet, known as pearl millet (Pennisetum glaucum (L.) R. Br. Syn. Pennisetum americanum (L.) Leeke), is raised over 312.00 lakh hectares in Asian and African countries. India is regarded as the significant hotspot for pearl millet diversity. In the Indian state of Haryana, where pearl millet is grown, a new and catastrophic bacterial disease known as stem rot of pearl millet spurred by the bacterium Klebsiella aerogenes (formerly Enterobacter) was first observed during fall 2018. The disease appears in form of small to long streaks on leaves, lesions on stem, and slimy rot appearance of stem. The associated bacterium showed close resemblance to Klebsiella aerogenes that was confirmed by a molecular evaluation based on 16S rDNA and gyrA gene nucleotide sequences. The isolates were also identified to be Klebsiella aerogenes based on biochemical assays, where Klebsiella isolates differed in D-trehalose and succinate alkalisation tests. During fall 2021-2023, the disease has spread all the pearl millet-growing districts of the state, extending up to 70% disease incidence in the affected fields. The disease is causing considering grain as well as fodder losses. The proposed scale, consisting of six levels (0-5), is developed where scores 0, 1, 2, 3, 4, and 5 have been categorized as highly resistant, resistant, moderately resistant, moderately susceptible, susceptible, and highly susceptible disease reaction, respectively. The disease cycle, survival of pathogen, and possible losses have also been studied to understand other features of the disease.
Over the last several decades, large wildfires are increasingly common across the United States c... more Over the last several decades, large wildfires are increasingly common across the United States causing disproportionate impact on forest health and function, human well-being, and economy. Here, we examine the severity of large wildfires across the Contiguous United States over the past decade (2011-2020) using a wide array of meteorological, vegetational, and topographical features in the Deep Neural Network model. A total of 4,538 wildfire incidents were used in the analysis covering 87,305 square miles of burned area. We observed the highest number of large wildfires in California, Texas, and Idaho, with lightning causing 43 % of these incidents. Importantly, results indicate that the severity of wildfire occurrences is highly correlated with the climatological forcings, land cover, location, and elevation of the ecosystem. Overall, results may serve useful guide in managing landscapes under changing climate and disturbance regimes.
The paper needs to be specific about hiow the nutrient concentrations are regulated in an aquapon... more The paper needs to be specific about hiow the nutrient concentrations are regulated in an aquaponic system, which nutrients are important for growth of symbiotic growth of plants and fish in a single setup and how they are chosen.
Neural networks were treated as black boxes for a long time. Previous works have unearthed what a... more Neural networks were treated as black boxes for a long time. Previous works have unearthed what aspects of an image were important for convolutional layers at different positions in the network. This was done using deconvolutional networks. In this paper, we examine how well a convolutional neural network performs when those convolutional layers which are relatively unimportant for a particular image (i.e., the image does not produce one of the strongest activations) are skipped in the training, validating, and testing process.
2023 IEEE/ACIS 21st International Conference on Software Engineering Research, Management and Applications (SERA)
Computer vis10n and image processing algorithms work well under strong assumptions. Computer visi... more Computer vis10n and image processing algorithms work well under strong assumptions. Computer vision algorithms are not expected to do well on all kinds of inputs. For instance, excessively noisy images may not fetch optimal results for most computer vision algorithms. Unexpected outputs from the computer vision module can have negative downstream consequences for other modules in the pipeline. To mitigate such consequences, we use a predictor framework that was simultaneously trained with a Hardness Predictor network. This framework guarantees improved performance over those images with lower "hardness" values. The proposed predictor framework, when applied to the input data, would result in a relatively lower variance estimator when the size of the training set is large, both in the domain of semantic segmentation as well as regression analysis.
Climate change, land degradation, and limited land and water resources have challenged our abilit... more Climate change, land degradation, and limited land and water resources have challenged our ability to meet the food demand of a rapidly growing population. To tackle this challenge, modern agricultural systems are relying on new technologies like the Internet of Things (IoT) to improve agricultural productivity and resource use efficiency. Although IoT has gained considerable attention in the last few years, the key concepts of IoT and their applicability across different domains of agriculture are still new to many researchers, practitioners, managers, and policymakers. In this paper, we provide a comprehensive review of the use of different IoT platforms, wireless sensor networks, and other associated technologies like remote sensing, cloud computing, and big data analytics in digital agriculture. The review also explores the use of communication technologies, microcontrollers, and machine learning in smart irrigation and decision support systems. The necessity of interoperability (data transfer and communication without human interference) among devices is discussed in detail with regard to facilitating and exchanging agricultural data more effectively. The discussion also includes opportunities and challenges in standardizing
The purpose of this research is to create a deep learning model capable of predicting the day of ... more The purpose of this research is to create a deep learning model capable of predicting the day of harvest for soybeans growing in hydroponic conditions. The algorithm uses feature extraction to calculate the day of growth for each annotated picture fed into the model. The recorded photos in this study were tagged using the Computer Vision Annotation Tool (CVAT), which was then used to train a five-layer Convolutional Neural Network (CNN) to predict the range of cultivation days. This pre-trained model was then deployed on the backend using Flask, and for each picture provided as input to the model, a Graphical User Interface (GUI) was created to accept a taken image as input and estimate the day of cultivation for real-time application.
Heavy metal concentrations that must be maintained in aquaponic environments for plant growth hav... more Heavy metal concentrations that must be maintained in aquaponic environments for plant growth have been a source of concern for many decades, as they cannot be completely eliminated in a commercial set-up. Our goal was to create a low-cost real-time smart sensing and actuation system for controlling heavy metal concentrations in aquaponic solutions. Our solution entails sensing the nutrient concentrations in the hydroponic solution, specifically calcium, sulfate, and phosphate, and sending them to a Machine Learning (ML) model hosted on an Android application. The ML algorithm used in this case was a Linear Support Vector Machine (Linear-SVM) trained on top three nutrient predictors chosen after applying a pipeline of Feature Selection methods namely a pairwise correlation matrix, ExtraTreesClassifier and Xgboost classifier on a dataset recorded from three aquaponic farms from South-East Texas. The ML algorithm was then hosted on a cloud platform which would then output the maximum ...
Estimating forest carbon storage is crucial for understanding sink capacities to facilitate carbo... more Estimating forest carbon storage is crucial for understanding sink capacities to facilitate carbon crediting and mitigate climate change. Images captured with RGB or LiDARÂ cameras, mounted on drones, could be used to derive forest structural parameters such as canopy area, height, and tree diameter. Further, these data could be used in Machine Learning models and allometric equations to rapidly and precisely estimate and model carbon storage in their living biomass. Graphical Abstract
With the recent advances in the field of alternate agriculture, there has been an ever-growing de... more With the recent advances in the field of alternate agriculture, there has been an ever-growing demand for aquaponics as a potential substitute for traditional agricultural techniques for improving sustainable food production. However, the lack of data-driven methods and approaches for aquaponic cultivation remains a challenge. The objective of this research is to investigate statistical methods to make inferences using small datasets for nutrient control in aquaponics to optimize yield. In this work, we employed the Density-Based Synthetic Minority Over-sampling TEchnique (DB-SMOTE) to address dataset imbalance, and ExtraTreesClassifer and Recursive Feature Elimination (RFE) to choose the relevant features. Synthetic data generation techniques such as the Monte-Carlo (MC) sampling techniques were used to generate enough data points and different feature engineering techniques were used on the predictors before evaluating the performance of kernel-based classifiers with the goal of c...
Nutrient regulation in aquaponic environments has been the topic of research for many years. Most... more Nutrient regulation in aquaponic environments has been the topic of research for many years. Most have focused on appropriate control of nutrients in an aquaponic set-up, but very little research has been done on commercial scale applications. In our model, the input data was sourced on a weekly basis from three commercial aquaponic farms in South-East Texas over the course of a year. Due to limited number of data points, dimensionality reduction techniques like pair-wise correlation matrix was used to remove the highly correlated predictors. Feature selection techniques like the XGBoost classifier and Recursive Feature Elimination with ExtraTreesClassifier were used to rank the features in order of their relative importance. Ammonium and calcium were found to be the top two nutrient predictors and based on the months in which lettuce was cultivated, the median of these nutrient values from the historical dataset served as the optimal concentrations to be maintained in the aquaponic...
This article is an open access article distributed under the terms and conditions of the Creative... more This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY
In this paper, the names of the fifth author and 12th author were incorrectly spelled. The correc... more In this paper, the names of the fifth author and 12th author were incorrectly spelled. The correct spelling of the authors' names are Niharika Sheoran and Sheetal Dhariwal, respectively. The corrected version of author list is as follows. The authors would like to apologize for any inconvenience caused.
Plant pathology journal/The plant pathology journal, Feb 1, 2024
The oldest and most extensively cultivated form of millet, known as pearl millet (Pennisetum glau... more The oldest and most extensively cultivated form of millet, known as pearl millet (Pennisetum glaucum (L.) R. Br. Syn. Pennisetum americanum (L.) Leeke), is raised over 312.00 lakh hectares in Asian and African countries. India is regarded as the significant hotspot for pearl millet diversity. In the Indian state of Haryana, where pearl millet is grown, a new and catastrophic bacterial disease known as stem rot of pearl millet spurred by the bacterium Klebsiella aerogenes (formerly Enterobacter) was first observed during fall 2018. The disease appears in form of small to long streaks on leaves, lesions on stem, and slimy rot appearance of stem. The associated bacterium showed close resemblance to Klebsiella aerogenes that was confirmed by a molecular evaluation based on 16S rDNA and gyrA gene nucleotide sequences. The isolates were also identified to be Klebsiella aerogenes based on biochemical assays, where Klebsiella isolates differed in D-trehalose and succinate alkalisation tests. During fall 2021-2023, the disease has spread all the pearl millet-growing districts of the state, extending up to 70% disease incidence in the affected fields. The disease is causing considering grain as well as fodder losses. The proposed scale, consisting of six levels (0-5), is developed where scores 0, 1, 2, 3, 4, and 5 have been categorized as highly resistant, resistant, moderately resistant, moderately susceptible, susceptible, and highly susceptible disease reaction, respectively. The disease cycle, survival of pathogen, and possible losses have also been studied to understand other features of the disease.
Over the last several decades, large wildfires are increasingly common across the United States c... more Over the last several decades, large wildfires are increasingly common across the United States causing disproportionate impact on forest health and function, human well-being, and economy. Here, we examine the severity of large wildfires across the Contiguous United States over the past decade (2011-2020) using a wide array of meteorological, vegetational, and topographical features in the Deep Neural Network model. A total of 4,538 wildfire incidents were used in the analysis covering 87,305 square miles of burned area. We observed the highest number of large wildfires in California, Texas, and Idaho, with lightning causing 43 % of these incidents. Importantly, results indicate that the severity of wildfire occurrences is highly correlated with the climatological forcings, land cover, location, and elevation of the ecosystem. Overall, results may serve useful guide in managing landscapes under changing climate and disturbance regimes.
The paper needs to be specific about hiow the nutrient concentrations are regulated in an aquapon... more The paper needs to be specific about hiow the nutrient concentrations are regulated in an aquaponic system, which nutrients are important for growth of symbiotic growth of plants and fish in a single setup and how they are chosen.
Neural networks were treated as black boxes for a long time. Previous works have unearthed what a... more Neural networks were treated as black boxes for a long time. Previous works have unearthed what aspects of an image were important for convolutional layers at different positions in the network. This was done using deconvolutional networks. In this paper, we examine how well a convolutional neural network performs when those convolutional layers which are relatively unimportant for a particular image (i.e., the image does not produce one of the strongest activations) are skipped in the training, validating, and testing process.
2023 IEEE/ACIS 21st International Conference on Software Engineering Research, Management and Applications (SERA)
Computer vis10n and image processing algorithms work well under strong assumptions. Computer visi... more Computer vis10n and image processing algorithms work well under strong assumptions. Computer vision algorithms are not expected to do well on all kinds of inputs. For instance, excessively noisy images may not fetch optimal results for most computer vision algorithms. Unexpected outputs from the computer vision module can have negative downstream consequences for other modules in the pipeline. To mitigate such consequences, we use a predictor framework that was simultaneously trained with a Hardness Predictor network. This framework guarantees improved performance over those images with lower "hardness" values. The proposed predictor framework, when applied to the input data, would result in a relatively lower variance estimator when the size of the training set is large, both in the domain of semantic segmentation as well as regression analysis.
Climate change, land degradation, and limited land and water resources have challenged our abilit... more Climate change, land degradation, and limited land and water resources have challenged our ability to meet the food demand of a rapidly growing population. To tackle this challenge, modern agricultural systems are relying on new technologies like the Internet of Things (IoT) to improve agricultural productivity and resource use efficiency. Although IoT has gained considerable attention in the last few years, the key concepts of IoT and their applicability across different domains of agriculture are still new to many researchers, practitioners, managers, and policymakers. In this paper, we provide a comprehensive review of the use of different IoT platforms, wireless sensor networks, and other associated technologies like remote sensing, cloud computing, and big data analytics in digital agriculture. The review also explores the use of communication technologies, microcontrollers, and machine learning in smart irrigation and decision support systems. The necessity of interoperability (data transfer and communication without human interference) among devices is discussed in detail with regard to facilitating and exchanging agricultural data more effectively. The discussion also includes opportunities and challenges in standardizing
The purpose of this research is to create a deep learning model capable of predicting the day of ... more The purpose of this research is to create a deep learning model capable of predicting the day of harvest for soybeans growing in hydroponic conditions. The algorithm uses feature extraction to calculate the day of growth for each annotated picture fed into the model. The recorded photos in this study were tagged using the Computer Vision Annotation Tool (CVAT), which was then used to train a five-layer Convolutional Neural Network (CNN) to predict the range of cultivation days. This pre-trained model was then deployed on the backend using Flask, and for each picture provided as input to the model, a Graphical User Interface (GUI) was created to accept a taken image as input and estimate the day of cultivation for real-time application.
Heavy metal concentrations that must be maintained in aquaponic environments for plant growth hav... more Heavy metal concentrations that must be maintained in aquaponic environments for plant growth have been a source of concern for many decades, as they cannot be completely eliminated in a commercial set-up. Our goal was to create a low-cost real-time smart sensing and actuation system for controlling heavy metal concentrations in aquaponic solutions. Our solution entails sensing the nutrient concentrations in the hydroponic solution, specifically calcium, sulfate, and phosphate, and sending them to a Machine Learning (ML) model hosted on an Android application. The ML algorithm used in this case was a Linear Support Vector Machine (Linear-SVM) trained on top three nutrient predictors chosen after applying a pipeline of Feature Selection methods namely a pairwise correlation matrix, ExtraTreesClassifier and Xgboost classifier on a dataset recorded from three aquaponic farms from South-East Texas. The ML algorithm was then hosted on a cloud platform which would then output the maximum ...
Estimating forest carbon storage is crucial for understanding sink capacities to facilitate carbo... more Estimating forest carbon storage is crucial for understanding sink capacities to facilitate carbon crediting and mitigate climate change. Images captured with RGB or LiDARÂ cameras, mounted on drones, could be used to derive forest structural parameters such as canopy area, height, and tree diameter. Further, these data could be used in Machine Learning models and allometric equations to rapidly and precisely estimate and model carbon storage in their living biomass. Graphical Abstract
With the recent advances in the field of alternate agriculture, there has been an ever-growing de... more With the recent advances in the field of alternate agriculture, there has been an ever-growing demand for aquaponics as a potential substitute for traditional agricultural techniques for improving sustainable food production. However, the lack of data-driven methods and approaches for aquaponic cultivation remains a challenge. The objective of this research is to investigate statistical methods to make inferences using small datasets for nutrient control in aquaponics to optimize yield. In this work, we employed the Density-Based Synthetic Minority Over-sampling TEchnique (DB-SMOTE) to address dataset imbalance, and ExtraTreesClassifer and Recursive Feature Elimination (RFE) to choose the relevant features. Synthetic data generation techniques such as the Monte-Carlo (MC) sampling techniques were used to generate enough data points and different feature engineering techniques were used on the predictors before evaluating the performance of kernel-based classifiers with the goal of c...
Nutrient regulation in aquaponic environments has been the topic of research for many years. Most... more Nutrient regulation in aquaponic environments has been the topic of research for many years. Most have focused on appropriate control of nutrients in an aquaponic set-up, but very little research has been done on commercial scale applications. In our model, the input data was sourced on a weekly basis from three commercial aquaponic farms in South-East Texas over the course of a year. Due to limited number of data points, dimensionality reduction techniques like pair-wise correlation matrix was used to remove the highly correlated predictors. Feature selection techniques like the XGBoost classifier and Recursive Feature Elimination with ExtraTreesClassifier were used to rank the features in order of their relative importance. Ammonium and calcium were found to be the top two nutrient predictors and based on the months in which lettuce was cultivated, the median of these nutrient values from the historical dataset served as the optimal concentrations to be maintained in the aquaponic...
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