Deep Learning and Computer Vision in Smart Agriculture

2020 ◽  
pp. 66-88
Author(s):  
Shiv Kumar ◽  
Agrima Yadav ◽  
Deepak Kumar Sharma
Keyword(s):  
Computer Vision ◽  
Deep Learning ◽  
Life Cycle ◽  
World Population ◽  
Traditional Methods ◽  
Demand And Supply ◽  
The World ◽  
Increasing Demand ◽  
Smart Agriculture ◽  
The Many

The exponential growth in the world population has led to an ever-increasing demand for food supplies. This has led to the realization that conventional and traditional methods alone might not be able to keep up with this demand. Smart agriculture is being regarded as one of the few realistic ways that, together with the traditional methods, can be used to close the gap between the demand and supply. Smart agriculture integrates the use of different technologies to better monitor, operate, and analyze different activities involved in different phases of the agricultural life cycle. Smart agriculture happens to be one of the many disciplines where deep learning and computer vision are being realized to be of major impact. This chapter gives a detailed explanation of different deep learning methods and tries to provide a basic understanding as to how these techniques are impacting different applications in smart agriculture.

Smart Farming Using Internet of Things

2021 ◽  
pp. 236-250
Author(s):  
Rishabh Verma ◽  
Latika Kharb
Keyword(s):  
Deep Learning ◽  
Internet Of Things ◽  
Water System ◽  
Field Conditions ◽  
Learning Models ◽  
System Framework ◽  
Smart Farming ◽  
The World ◽  
Crop Types ◽  
Smart Agriculture

Smart farming through IoT technology could empower farmers to upgrade profitability going from the amount of manure to be used to the quantity of water for irrigating their fields and also help them to decrease waste. Through IoT, sensors could be used for assisting farmers in the harvest field to check for light, moistness, temperature, soil dampness, etc., and robotizing the water system framework. Moreover, the farmers can screen the field conditions from anyplace and overcome the burden and fatigue to visit farms to confront problems in the fields. For example, farmers are confronting inconvenience while utilizing right quantity and time to use manures and pesticides in their fields as per the crop types. In this chapter, the authors have introduced a model where farmers can classify damaged crops and healthy crops with the help of different sensors and deep learning models. (i.e., The idea of implementing IoT concepts for the benefit of farmers and moving the world towards smart agriculture is presented.)

scholarly journals A Cost-Effective Person-Following System for Assistive Unmanned Vehicles with Deep Learning at the Edge

Machines ◽  
2020 ◽  
Vol 8 (3) ◽  
pp. 49
Author(s):  
Anna Boschi ◽  
Francesco Salvetti ◽  
Vittorio Mazzia ◽  
Marcello Chiaberge
Keyword(s):  
Deep Learning ◽  
Cost Effective ◽  
Vital Statistics ◽  
Unmanned Vehicles ◽  
Optimization Techniques ◽  
World Population ◽  
Service Robotics ◽  
Power Efficient ◽  
Person Following ◽  
The World

The vital statistics of the last century highlight a sharp increment of the average age of the world population with a consequent growth of the number of older people. Service robotics applications have the potentiality to provide systems and tools to support the autonomous and self-sufficient older adults in their houses in everyday life, thereby avoiding the task of monitoring them with third parties. In this context, we propose a cost-effective modular solution to detect and follow a person in an indoor, domestic environment. We exploited the latest advancements in deep learning optimization techniques, and we compared different neural network accelerators to provide a robust and flexible person-following system at the edge. Our proposed cost-effective and power-efficient solution is fully-integrable with pre-existing navigation stacks and creates the foundations for the development of fully-autonomous and self-contained service robotics applications.

scholarly journals Ibsen in Russia Revisited: The Ibsenian Legacy in the Writings of the Russian Symbolist Andrey Bely

2016 ◽  
Vol 19 ◽  
Author(s):  
Lillian Jorunn Helle
Keyword(s):  
Life Cycle ◽  
Fin De Siecle ◽  
Main Topic ◽  
Significant Source ◽  
Henrik Ibsen ◽  
Creative Development ◽  
The World ◽  
Andrey Bely ◽  
Fin De Siècle ◽  
The Many

The intellectual atmosphere of Russian fin de siècle was characterized by a strong fascination for Norway, its nature, its culture and its literature. A good example is Henrik Ibsen who was a significant source of inspiration for Russian dramatists, writers and poets. The Russian symbolists in particular saw Ibsen as a tutelary spirit and not least the “younger” symbolist Andrey Bely regarded his works and thoughts as a prefiguration and a foreshadowing of his own. Ibsen was important to Bely through all his various stages of intellectual, artistic and spiritual seeking and in accordance with Bely’s highly interpretative, hermeneutical approach to the world, in which everything he experienced was transformed to confirm his own symbolist Weltanschauung, also Ibsen was transformed in much the same manner. And the very intriguing way in which Bely rewrites the Norwegian playwright into his own writings will be the main topic of this article, illustrating how the Russian symbolist refigures the Norwegian dramatist to make him fit into his own continuous search for new and meaningful perspectives and positions. Moreover and even still more remarkable, this search convincingly demonstrates how the Ibsenian legacy throughout the many different phases of Bely’s creative development keeps it crucial place within Bely’s life cycle, thereby establishing a most interesting thread in the complex web of Ibsen’s Wirkungsgeschichte.

scholarly journals Domestication reprogrammed the budding yeast life cycle

2020 ◽  
Author(s):  
Matteo De Chiara ◽  
Benjamin Barré ◽  
Karl Persson ◽  
Amadi Onyetuga Chioma ◽  
Agurtzane Irizar ◽  
...  
Keyword(s):  
Life Cycle ◽  
Evolutionary History ◽  
Budding Yeast ◽  
World Population ◽  
Model Species ◽  
Quiescent Cells ◽  
Gamete Formation ◽  
The World ◽  
Wild Yeasts ◽  
Yeast Evolution

AbstractDomestication of plants and animals is the foundation for feeding the world population. We report that domestication of the model yeast S. cerevisiae reprogrammed its life cycle entirely. We tracked growth, gamete formation and cell survival across many environments for nearly 1000 genome sequenced isolates and found a remarkable dichotomy between domesticated and wild yeasts. Wild yeasts near uniformly trigger meiosis and sporulate when encountering nutrient depletions, whereas domestication relaxed selection on sexual reproduction and favoured survival as quiescent cells. Domestication also systematically enhanced fermentative over respiratory traits while decreasing stress tolerance. We show that this yeast domestication syndrome was driven by aneuploidies and gene function losses that emerged independently in multiple domesticated lineages during the specie’s recent evolutionary history. We found domestication to be the most dramatic event in budding yeast evolution, raising questions on how much domestication has distorted our understanding of this key model species.

Future of Life-Cycle Assessment in a Smart and/or Sustainable World

2020 ◽  
pp. 177-207
Author(s):  
Aryan Shahabian ◽  
Alireza Fadai ◽  
Thomas Peruzzi
Keyword(s):  
Life Cycle Assessment ◽  
Life Cycle ◽  
Input Data ◽  
Developed Countries ◽  
World Population ◽  
Scientific Texts ◽  
The Sustainable Development ◽  
Complex Process ◽  
The World ◽  
Development Goals

Today, even in developed countries, performing life-cycle assessment (LCA) is still a challenging and complex process, mixed with the possibility of significant errors—namely due to unreliable input data derived from unrepresentative sampling. Some scientific texts illustrate the so-called Smart World—where such errors are minimized via the exchange of information between everything globally. This may sound contradictory to the fact that now almost half the world population do not even have internet access. However, this chapter shows—by reasoning, review, and synthesis of the literature, theories, and data—that the emergence of the Smart World is plausible. Yet, it will not necessarily be sustainable, unless “smartness” is (re)defined in line with the Sustainable Development Goals. Otherwise, also, LCA might become obsolete, or its goals may transmute to non-sustainable ones. Focusing on examples from the construction industry and their interactions with other sectors, some shortcuts are also suggested to facilitate innovations and development of LCA and decision-making procedures.

scholarly journals Traffic Sign Classification Using Convolutional Neural Networks and Computer Vision

2021 ◽  
Vol 12 (3) ◽  
pp. 4244-4250
Author(s):  
Anuraag Velamati Et.al
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Neural Networks ◽  
Computer Vision ◽  
Deep Learning ◽  
Image Classification ◽  
Human Beings ◽  
Traffic Sign ◽  
The World ◽  
Traffic Sign Classification

The world is quickly and continuously advancing towards better technological advancements that will make life quite easier for us, human beings [22]. Humans are looking for more interactive and advanced ways to improve their learning. One such dream is making a machine think like a computer, which lead to innovations like AI and deep learning [25]. The world is running at a higher pace in the domain of AI, deep learning, robotics and machine learning Using this knowledge and technology, we could develop anything right now [36]. As a part of sub-domain, the introduction of Convolution Neural Networks made deep learning extensively strong in the domain of image classification and detection [1]..The research that we have conducted is one of its kind. Our research used Convolution Neural Network, TensorFlow and Keras.

scholarly journals Impact of Doubling Farmers’ Income on Area, Production and Productivity of Pulses in India

2020 ◽  
pp. 73-86
Author(s):  
S. B. Ramya Lakshmi ◽  
Priyanka Patra ◽  
K. C. Gummagolmath
Keyword(s):  
Slow Growth ◽  
Secondary Data ◽  
Lessons Learned ◽  
Policy Implications ◽  
Demand And Supply ◽  
The World ◽  
Increasing Trend ◽  
Support Price ◽  
Increasing Demand ◽  
Made In

India is the largest producer and consumer of pulses in the world contributing around 25-28% of the total global production. The increasing demand and supply gap of pulses has led to an increasing trend in their import by India. In view of this gap, one of the strategies for “Doubling Farmers’ Income by 2022” was to enhance area, production and productivity of pulses in India by incentivizing the farmers with right price. Hence an attempt has been made in this study to analyze the growth of pulses over a period of time i.e. from 1980-81 to 2017-18 in India using secondary data. The average annual growth rates and percentages were used as tools to carry out analysis. The results revealed that area, production and productivity of total pulses was positive but indicating a slow growth of 1.36 per cent, 3.29 per cent and 2.04 per cent respectively per annum. Due to steep hike in Minimum Support Price (MSP) during 2015-16, it was noticed that area, production and yield have recorded higher growth than previous years. The percentage change in area, production and productivity for major pulses during three periods indicated that there was a marginal increase in area and production of gram in major producing states and the same trend was noticed in red gram and black gram. It was also observed that in some of the states the growth in period- III for major pulses was higher compared to period-II. Policy implications or lessons learned are missing!

Biofuels

2014 ◽  
Author(s):  
David Pimentel ◽  
Michael Burgess
Keyword(s):  
Fossil Fuels ◽  
Director General ◽  
World Population ◽  
Food And Agriculture ◽  
Food And Agriculture Organization ◽  
The World ◽  
Food Shortages ◽  
Food And Feed ◽  
Food Grain ◽  
Increasing Demand

A rapidly growing world population and an even more rapidly growing consumption of fossil fuels are increasing demand for both food and biofuels, which will exaggerate both the food and fuel shortages around the world. Producing biofuels requires huge amounts of both fossil energy and food resources, which will intensify conflicts over these resources. Using food crops to produce ethanol raises major nutritional and ethical concerns. More than 66% of the world human population is currently malnourished, so the need for grains and other basic foods is critical. Growing crops for fuel squanders land, water, and energy resources vital for the production of food for people. Using food and feed crops for ethanol production has brought increases in the prices of US beef, chicken, pork, eggs, breads, cereals, and milk of 10% to 20%. In addition, Jacques Diouf, Director General of the UN Food and Agriculture Organization (FAO) reports that using food grains to produce biofuels is already causing food shortages for the poor of the world. Growing crops for biofuel ignores the need to reduce natural resource consumption and exacerbates the problem of malnourishment worldwide by turning food grain into biofuel.

scholarly journals Deep Learning-Enabled Semantic Inference of Individual Building Damage Magnitude from Satellite Images

Algorithms ◽  
2020 ◽  
Vol 13 (8) ◽  
pp. 195 ◽  
Author(s):  
Bradley J. Wheeler ◽  
Hassan A. Karimi
Keyword(s):  
Computer Vision ◽  
Deep Learning ◽  
Natural Disasters ◽  
Satellite Images ◽  
Management Decision ◽  
Semantic Inference ◽  
The World ◽  
Major Bottleneck ◽  
Post Disaster ◽  
Management Decision Support

Natural disasters are phenomena that can occur in any part of the world. They can cause massive amounts of destruction and leave entire cities in great need of assistance. The ability to quickly and accurately deliver aid to impacted areas is crucial toward not only saving time and money, but, most importantly, lives. We present a deep learning-based computer vision model to semantically infer the magnitude of damage to individual buildings after natural disasters using pre- and post-disaster satellite images. This model helps alleviate a major bottleneck in disaster management decision support by automating the analysis of the magnitude of damage to buildings post-disaster. In this paper, we will show our methods and results for how we were able to obtain a better performance than existing models, especially in moderate to significant magnitudes of damage, along with ablation studies to show our methods and results for the importance and impact of different training parameters in deep learning for satellite imagery. We were able to obtain an overall F1 score of 0.868 with our methods.

scholarly journals IMPACT OF CLIMATE CHANGE ON SUGARCANE CROP AND REMEDIAL MEASURES-A REVIEW

2019 ◽  
Vol 34 (1) ◽  
Author(s):  
Muhammad Chohan
Keyword(s):  
Management Practices ◽  
Behavior Changes ◽  
Changing Environment ◽  
Pollination Services ◽  
Sugarcane Crop ◽  
Yield Production ◽  
The World ◽  
Agroecological Zones ◽  
Increasing Demand ◽  
The Many

Increasing demand of bioenergy, sugar and other byproducts make sugarcane (Sacharum officinarum) very important and valuable crop in world. It is growing on the many diversified agroecological zones of 120 countries of the world. Brazil, India, China, Thailand, Pakistan, Mexico, Colombia, Indonesia, Philippines, and USA (number wise from area and production) are top ten sugarcane producing countries in the world. But yield production per hectare of these countries is ranged from 56-86 t ha-1 is much lower than the achievable potential 100-130 t ha-1. Amongst many other factors responsible for lowering the yield, change in environment is now growing a major factor. It is very difficult to increase area for sugarcane so efforts are made to increase the yield production at the same cultivated area, the per hectare yield production must be improved by adapting modern technologies suited for changing environment. The global warming is increasing due to change in environment by the increasing emission of green house gases. Sugarcane is sensitive for climatic parameters such as temperature, rainfall, sunlight and soil. The research indicated that global temperature will increased from 3-5oC in the end of 21st century. The enormous human activities are cause to accelerate the changing in our ecosystem with low or high rainfall, temperature, severity of pests pressure, unavailability of pollination services, decrease soil fertility, alter the water use efficiencies and crop behavior. Changes in temperature, rainfall, floods, drought, salinity stresses and frosts have been found major factor for lowering production of sugarcane in Pakistan is highly dependent on natural resources. Obtaining the high cane production with high quality juice in future, it is necessary to develop cane varieties and advanced farm management practices which will suitable under changing environment.

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