scholarly journals Applications and Trends of Machine Learning in Genomics and Phenomics for Next-Generation Breeding

Plants ◽  
2019 ◽  
Vol 9 (1) ◽  
pp. 34 ◽  
Author(s):  
Salvatore Esposito ◽  
Domenico Carputo ◽  
Teodoro Cardi ◽  
Pasquale Tripodi
Keyword(s):  
Machine Learning ◽  
Crop Production ◽  
Target Genes ◽  
Raw Materials ◽  
Relevant Information ◽  
Plant Diseases ◽  
Next Generation ◽  
Food Shortages ◽  
Computational Sciences ◽  
Adverse Environmental Conditions

Crops are the major source of food supply and raw materials for the processing industry. A balance between crop production and food consumption is continually threatened by plant diseases and adverse environmental conditions. This leads to serious losses every year and results in food shortages, particularly in developing countries. Presently, cutting-edge technologies for genome sequencing and phenotyping of crops combined with progress in computational sciences are leading a revolution in plant breeding, boosting the identification of the genetic basis of traits at a precision never reached before. In this frame, machine learning (ML) plays a pivotal role in data-mining and analysis, providing relevant information for decision-making towards achieving breeding targets. To this end, we summarize the recent progress in next-generation sequencing and the role of phenotyping technologies in genomics-assisted breeding toward the exploitation of the natural variation and the identification of target genes. We also explore the application of ML in managing big data and predictive models, reporting a case study using microRNAs (miRNAs) to identify genes related to stress conditions.

scholarly journals Frontiers in the Solicitation of Machine Learning Approaches in Vegetable Science Research

2021 ◽  
Vol 13 (15) ◽  
pp. 8600
Author(s):  
Meenakshi Sharma ◽  
Prashant Kaushik ◽  
Aakash Chawade
Keyword(s):  
Machine Learning ◽  
Genome Sequencing ◽  
Crop Production ◽  
Seed Quality ◽  
Raw Materials ◽  
Science Research ◽  
Machine Learning Algorithms ◽  
Plant Diseases ◽  
Vegetable Production ◽  
Learning Approaches

Along with essential nutrients and trace elements, vegetables provide raw materials for the food processing industry. Despite this, plant diseases and unfavorable weather patterns continue to threaten the delicate balance between vegetable production and consumption. It is critical to utilize machine learning (ML) in this setting because it provides context for decision-making related to breeding goals. Cutting-edge technologies for crop genome sequencing and phenotyping, combined with advances in computer science, are currently fueling a revolution in vegetable science and technology. Additionally, various ML techniques such as prediction, classification, and clustering are frequently used to forecast vegetable crop production in the field. In the vegetable seed industry, machine learning algorithms are used to assess seed quality before germination and have the potential to improve vegetable production with desired features significantly; whereas, in plant disease detection and management, the ML approaches can improve decision-support systems that assist in converting massive amounts of data into valuable recommendations. On similar lines, in vegetable breeding, ML approaches are helpful in predicting treatment results, such as what will happen if a gene is silenced. Furthermore, ML approaches can be a saviour to insufficient coverage and noisy data generated using various omics platforms. This article examines ML models in the field of vegetable sciences, which encompasses breeding, biotechnology, and genome sequencing.

An IoT-based agriculture maintenance using pervasive computing with machine learning technique

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Swathi Kailasam ◽  
Sampath Dakshina Murthy Achanta ◽  
P. Rama Koteswara Rao ◽  
Ramesh Vatambeti ◽  
Saikumar Kayam
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Crop Production ◽  
Simulation Analysis ◽  
Research Work ◽  
Similarity Index ◽  
Plant Diseases ◽  
Threshold Segmentation ◽  
Content Type ◽  
Random Forest Classification

PurposeIn cultivation, early harvest offers farmers an opportunity to increase production while decreasing the chances of lower crop production rates, ensuring that the economy remains balanced. The significant reason is to predict the disease in plants and distinguish the type of syndrome with the help of segmentation and random forest optimization classification. In this investigation, the accurate prior phase of crop imagery has been collected from different datasets like cropscience, yesmodes and nelsonwisc . In the current study, the real-time earlier state of crop images has been gathered from numerous data sources similar to crop_science, yes_modes, nelson_wisc dataset.Design/methodology/approachIn this research work, random forest machine learning-based persuasive plants healthcare computing is provided. If proper ecological care is not applied to early harvesting, it can cause diseases in plants, decrease the cropping rate and less production. Until now different methods have been developed for crop analysis at an earlier stage, but it is necessary to implement methods to advanced techniques. So, the detection of plant diseases with the help of threshold segmentation and random forest classification has been involved in this investigation. This implemented design is verified on Python 3.7.8 software for simulation analysis.FindingsIn this work, different methods are developed for crops at an earlier stage, but more methods are needed to implement methods with prior stage crop harvesting. Because of this, a disease-finding system has been implemented. The methodologies like “Threshold segmentation” and RFO classifier lends 97.8% identification precision with 99.3% real optimistic rate, and 59.823 peak signal-to-noise (PSNR), 0.99894 structure similarity index (SSIM), 0.00812 machine squared error (MSE) values are attained.Originality/valueThe implemented machine learning design is outperformance methodology, and they are proving good application detection rate.

scholarly journals A Smartphone-based Plant Disease Detection and Treatment Recommendation System using Machine Learning Techniques

2022 ◽  
Vol 10 (1) ◽  
pp. 1-8
Author(s):  
Folasade Isinkaye
Keyword(s):  
Machine Learning ◽  
Crop Production ◽  
Plant Disease ◽  
Recommendation System ◽  
Research Effort ◽  
Plant Diseases ◽  
Machine Learning Techniques ◽  
Treatment Recommendation ◽  
Disease Detection ◽  
Recommended Treatment

Plant diseases cause major crop production losses worldwide, and a lot of significant research effort has been directed toward making plant disease identification and treatment procedures more effective. It would be of great benefit to farmers to be able to utilize the current technology in order to leverage the challenges facing agricultural production and hence improve crop production and operation profitability. In this work, we designed and implemented a user-friendly smartphone-based plant disease detection and treatment recommendation system using machine learning (ML) techniques. CNN was used for feature extraction while the ANN and KNN were used to classify the plant diseases; a content-based filtering recommendation algorithm was used to suggest relevant treatments for the detected plant diseases after classification. The result of the implementation shows that the system correctly detected and recommended treatment for plant diseases

CLASS-BASED MACHINE LEARNING FOR NEXT GENERATION WELLBORE DATA PROCESSING AND INTERPRETATION

2019 ◽  
Author(s):  
Vikas Jain ◽  
Po-Yen Wu ◽  
Ridvan Akkurt ◽  
Brook Hodenfield ◽  
Tianmin Jiang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Data Processing ◽  
Next Generation

scholarly journals Microbial Biocontrol as an Alternative to Synthetic Fungicides: Boundaries between Pre- and Postharvest Applications on Vegetables and Fruits

Fermentation ◽  
2021 ◽  
Vol 7 (2) ◽  
pp. 60
Author(s):  
Vincenzo Michele Sellitto ◽  
Severino Zara ◽  
Fabio Fracchetti ◽  
Vittorio Capozzi ◽  
Tiziana Nardi
Keyword(s):  
Crop Production ◽  
Fruits And Vegetables ◽  
Control Plant ◽  
Agricultural Practices ◽  
Plant Diseases ◽  
Postharvest Quality ◽  
Crucial Point ◽  
Production Chain ◽  
Vegetables And Fruits ◽  
And Storage

From a ‘farm to fork’ perspective, there are several phases in the production chain of fruits and vegetables in which undesired microbial contaminations can attack foodstuff. In managing these diseases, harvest is a crucial point for shifting the intervention criteria. While in preharvest, pest management consists of tailored agricultural practices, in postharvest, the contaminations are treated using specific (bio)technological approaches (physical, chemical, biological). Some issues connect the ‘pre’ and ‘post’, aligning some problems and possible solution. The colonisation of undesired microorganisms in preharvest can affect the postharvest quality, influencing crop production, yield and storage. Postharvest practices can ‘amplify’ the contamination, favouring microbial spread and provoking injures of the product, which can sustain microbial growth. In this context, microbial biocontrol is a biological strategy receiving increasing interest as sustainable innovation. Microbial-based biotools can find application both to control plant diseases and to reduce contaminations on the product, and therefore, can be considered biocontrol solutions in preharvest or in postharvest. Numerous microbial antagonists (fungi, yeasts and bacteria) can be used in the field and during storage, as reported by laboratory and industrial-scale studies. This review aims to examine the main microbial-based tools potentially representing sustainable bioprotective biotechnologies, focusing on the biotools that overtake the boundaries between pre- and postharvest applications protecting quality against microbial decay.

scholarly journals Analysis of Quality of Backyard Compost and Its Potential Utilization as a Circular Bio-Waste Source

2021 ◽  
Vol 11 (10) ◽  
pp. 4392
Author(s):  
Apolka Ujj ◽  
Kinga Percsi ◽  
Andras Beres ◽  
Laszlo Aleksza ◽  
Fernanda Ramos Diaz ◽  
...  
Keyword(s):  
Crop Production ◽  
Basic Education ◽  
Raw Materials ◽  
Nutrient Content ◽  
Quality Analysis ◽  
Weed Seed ◽  
Quality Properties ◽  
Seed Dispersion ◽  
Materials Used

The use and quality analysis of household compost have become very important issues in recent years due to the increasing interest in local food production and safe, self-produced food. The phenomenon was further exacerbated by the COVID-19 pandemic quarantine period, which gave new impetus to the growth of small home gardens. However, the knowledge associated with making high-quality compost is often lacking in home gardeners. Therefore, the objective of this research was to find answers to the following questions: can the quality of backyard compost be considered safe in terms of toxicity and nutrient content? Can weed seed dispersion affect the usability of backyard compost? In general, can the circulation of organic matter be increased with the spread of home composting? In this study, 16 different house composts were analysed for stability, weed seed contamination, toxic elements, and nutrient content using analysis of variance. The results of the research showed that the quality properties of the composts (including their weed seed dispersion effect) were greatly influenced by the different techniques and raw materials used. The toxicity levels, as well as the content of macro and microelements, were within the parameters of safe-quality compost. The specific macronutrient (Ca, Mg) and micronutrient (Fe, Mn) contents of the tested composts have a similar and, in some cases, more favorable nutrient supply capacity in crop production than the frequently-used cow manure-based composts. With a plan of basic education on composting, there is potential to encourage farmyard composting.

scholarly journals Metabolomics-Guided Elucidation of Plant Abiotic Stress Responses in the 4IR Era: An Overview

Metabolites ◽  
2021 ◽  
Vol 11 (7) ◽  
pp. 445
Author(s):  
Morena M. Tinte ◽  
Kekeletso H. Chele ◽  
Justin J. J. van der Hooft ◽  
Fidele Tugizimana
Keyword(s):  
Machine Learning ◽  
Abiotic Stress ◽  
Stress Responses ◽  
Abiotic Stresses ◽  
Industrial Revolution ◽  
Chemical Space ◽  
Big Data Analytics ◽  
Plant Responses ◽  
Next Generation ◽  
Computational Tools

Plants are constantly challenged by changing environmental conditions that include abiotic stresses. These are limiting their development and productivity and are subsequently threatening our food security, especially when considering the pressure of the increasing global population. Thus, there is an urgent need for the next generation of crops with high productivity and resilience to climate change. The dawn of a new era characterized by the emergence of fourth industrial revolution (4IR) technologies has redefined the ideological boundaries of research and applications in plant sciences. Recent technological advances and machine learning (ML)-based computational tools and omics data analysis approaches are allowing scientists to derive comprehensive metabolic descriptions and models for the target plant species under specific conditions. Such accurate metabolic descriptions are imperatively essential for devising a roadmap for the next generation of crops that are resilient to environmental deterioration. By synthesizing the recent literature and collating data on metabolomics studies on plant responses to abiotic stresses, in the context of the 4IR era, we point out the opportunities and challenges offered by omics science, analytical intelligence, computational tools and big data analytics. Specifically, we highlight technological advancements in (plant) metabolomics workflows and the use of machine learning and computational tools to decipher the dynamics in the chemical space that define plant responses to abiotic stress conditions.

scholarly journals Next-Generation Smart Response Web (NG-SRW): An Operational Spatial Decision Support System for Maritime Oil Spill Emergency Response in the Gulf of Finland (Baltic Sea)

2021 ◽  
Vol 13 (12) ◽  
pp. 6585
Author(s):  
Mihhail Fetissov ◽  
Robert Aps ◽  
Floris Goerlandt ◽  
Holger Jänes ◽  
Jonne Kotta ◽  
...  
Keyword(s):  
Baltic Sea ◽  
Oil Spill ◽  
Situational Awareness ◽  
Oil Spills ◽  
Relevant Information ◽  
Response Strategy ◽  
Spatial Decision Support System ◽  
Next Generation ◽  
Oil Spill Response ◽  
The Baltic

The Baltic Sea is a unique and sensitive brackish-water ecosystem vulnerable to damage from shipping activities. Despite high levels of maritime safety in the area, there is a continued risk of oil spills and associated harmful environmental impacts. Achieving common situational awareness between oil spill response decision makers and other actors, such as merchant vessel and Vessel Traffic Service center operators, is an important step to minimizing detrimental effects. This paper presents the Next-Generation Smart Response Web (NG-SRW), a web-based application to aid decision making concerning oil spill response. This tool aims to provide, dynamically and interactively, relevant information on oil spills. By integrating the analysis and visualization of dynamic spill features with the sensitivity of environmental elements and value of human uses, the benefits of potential response actions can be compared, helping to develop an appropriate response strategy. The oil spill process simulation enables the response authorities to judge better the complexity and dynamic behavior of the systems and processes behind the potential environmental impact assessment and thereby better control the oil combat action.

scholarly journals AI-Driven Framework for Recognition of Guava Plant Diseases through Machine Learning from DSLR Camera Sensor Based High Resolution Imagery

Sensors ◽  
2021 ◽  
Vol 21 (11) ◽  
pp. 3830
Author(s):  
Ahmad Almadhor ◽  
Hafiz Tayyab Rauf ◽  
Muhammad Ikram Ullah Lali ◽  
Robertas Damaševičius ◽  
Bader Alouffi ◽  
...  
Keyword(s):  
Machine Learning ◽  
High Resolution ◽  
Color Difference ◽  
Plant Diseases ◽  
Economic Losses ◽  
Production Loss ◽  
Postharvest Diseases ◽  
Tropical Regions ◽  
Guava Fruit ◽  
Tree Classifier

Plant diseases can cause a considerable reduction in the quality and number of agricultural products. Guava, well known to be the tropics’ apple, is one significant fruit cultivated in tropical regions. It is attacked by 177 pathogens, including 167 fungal and others such as bacterial, algal, and nematodes. In addition, postharvest diseases may cause crucial production loss. Due to minor variations in various guava disease symptoms, an expert opinion is required for disease analysis. Improper diagnosis may cause economic losses to farmers’ improper use of pesticides. Automatic detection of diseases in plants once they emerge on the plants’ leaves and fruit is required to maintain high crop fields. In this paper, an artificial intelligence (AI) driven framework is presented to detect and classify the most common guava plant diseases. The proposed framework employs the ΔE color difference image segmentation to segregate the areas infected by the disease. Furthermore, color (RGB, HSV) histogram and textural (LBP) features are applied to extract rich, informative feature vectors. The combination of color and textural features are used to identify and attain similar outcomes compared to individual channels, while disease recognition is performed by employing advanced machine-learning classifiers (Fine KNN, Complex Tree, Boosted Tree, Bagged Tree, Cubic SVM). The proposed framework is evaluated on a high-resolution (18 MP) image dataset of guava leaves and fruit. The best recognition results were obtained by Bagged Tree classifier on a set of RGB, HSV, and LBP features (99% accuracy in recognizing four guava fruit diseases (Canker, Mummification, Dot, and Rust) against healthy fruit). The proposed framework may help the farmers to avoid possible production loss by taking early precautions.

scholarly journals Agro-Nanotechnology as an Emerging Field: A Novel Sustainable Approach for Improving Plant Growth by Reducing Biotic Stress

2021 ◽  
Vol 11 (5) ◽  
pp. 2282
Author(s):  
Masudulla Khan ◽  
Azhar U. Khan ◽  
Mohd Abul Hasan ◽  
Krishna Kumar Yadav ◽  
Marina M. C. Pinto ◽  
...  
Keyword(s):  
Plant Growth ◽  
Crop Yield ◽  
Biotic Stress ◽  
Crop Production ◽  
Plant Disease ◽  
Yield Loss ◽  
Growth Parameters ◽  
Plant Diseases ◽  
High Yield ◽  
Plant Disease Management

In the present era, the global need for food is increasing rapidly; nanomaterials are a useful tool for improving crop production and yield. The application of nanomaterials can improve plant growth parameters. Biotic stress is induced by many microbes in crops and causes disease and high yield loss. Every year, approximately 20–40% of crop yield is lost due to plant diseases caused by various pests and pathogens. Current plant disease or biotic stress management mainly relies on toxic fungicides and pesticides that are potentially harmful to the environment. Nanotechnology emerged as an alternative for the sustainable and eco-friendly management of biotic stress induced by pests and pathogens on crops. In this review article, we assess the role and impact of different nanoparticles in plant disease management, and this review explores the direction in which nanoparticles can be utilized for improving plant growth and crop yield.

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