scholarly journals Artificial Intelligence to Support Bulgarian Crop Production

2021 ◽  
Vol LVIII ◽  
Author(s):  
Lyubka Doukovska
Keyword(s):  
Artificial Intelligence ◽  
Crop Production

scholarly journals Remote Control of Greenhouse Vegetable Production with Artificial Intelligence—Greenhouse Climate, Irrigation, and Crop Production

Sensors ◽  
2019 ◽  
Vol 19 (8) ◽  
pp. 1807 ◽  
Author(s):  
Silke Hemming ◽  
Feije de Zwart ◽  
Anne Elings ◽  
Isabella Righini ◽  
Anna Petropoulou
Keyword(s):  
Artificial Intelligence ◽  
Crop Production ◽  
Vegetable Production ◽  
Digital Interface ◽  
Greenhouse Climate ◽  
Fresh Food ◽  
Net Profit ◽  
Greenhouse Vegetable ◽  
Process Computer ◽  
Cucumber Yield

The global population is increasing rapidly, together with the demand for healthy fresh food. The greenhouse industry can play an important role, but encounters difficulties finding skilled staff to manage crop production. Artificial intelligence (AI) has reached breakthroughs in several areas, however, not yet in horticulture. An international competition on “autonomous greenhouses” aimed to combine horticultural expertise with AI to make breakthroughs in fresh food production with fewer resources. Five international teams, consisting of scientists, professionals, and students with different backgrounds in horticulture and AI, participated in a greenhouse growing experiment. Each team had a 96 m2 modern greenhouse compartment to grow a cucumber crop remotely during a 4-month-period. Each compartment was equipped with standard actuators (heating, ventilation, screening, lighting, fogging, CO2 supply, water and nutrient supply). Control setpoints were remotely determined by teams using their own AI algorithms. Actuators were operated by a process computer. Different sensors continuously collected measurements. Setpoints and measurements were exchanged via a digital interface. Achievements in AI-controlled compartments were compared with a manually operated reference. Detailed results on cucumber yield, resource use, and net profit obtained by teams are explained in this paper. We can conclude that in general AI performed well in controlling a greenhouse. One team outperformed the manually-grown reference.

scholarly journals Performance Evaluation of Artificial Intelligence on Soil Property Detection

2018 ◽  
Vol 4 (10) ◽  
pp. 5
Author(s):  
Smriti Singhatiya ◽  
Dr. Shivnath Ghosh
Keyword(s):  
Artificial Intelligence ◽  
Precision Agriculture ◽  
Crop Production ◽  
Nutrient Status ◽  
Soil Testing ◽  
Support Vector ◽  
Soil Test ◽  
New Era ◽  
Agriculture Soil ◽  
Historical Role

Now-a-days there is a need to study the nutrient status in lower horizons of the soil. Soil testing has played historical role in evaluating soil fertility maintenance and in sustainable agriculture. Soil testing shall also play its crucial role in precision agriculture. At present there is a need to develop basic inventory as per soil test basis and necessary information has to be built into the system for translating the results of soil test to achieve the crop production goal in new era. To achieve this goal artificial intelligence approach is used for predicting the soil properties.  In this paper for analysing these properties support vector regression (SVR), ensembled regression (ER) and neural network (NN) are used. The performance is evaluated with respect to MSE and RMSE and it is observed that ER outperforms better with respect to SVR and NN.

scholarly journals Digitalization of Technological Processes of Crop Production in Russia

2019 ◽  
Vol 13 (1) ◽  
pp. 14-20 ◽  
Author(s):  
V. M. Korotchenya ◽  
G. I. Lichman ◽  
I. G. Smirnov
Keyword(s):  
Artificial Intelligence ◽  
Internet Of Things ◽  
Precision Agriculture ◽  
Crop Production ◽  
Agricultural Sector ◽  
Integral Approach ◽  
The Internet ◽  
Digital Agriculture ◽  
The Internet Of Things ◽  
And Robotics

Currently, the influence of program documents on digital agriculture development is rather great in our country. Within the framework of the European Association of Agricultural Mechanical Engineering, a relevant definition of agriculture 4.0 has been elaborated and introduced.Research purpose: offering general recommendations on the digitalization of agriculture in RussiaMaterials and methods. The authors make use of the normative approach: the core of digital agriculture is compared with the current state of the agricultural sector in Russia.Results and discussion. The analysis has found that digital agriculture (agriculture 4.0 and 5.0) is based on developed mechanized technologies (agriculture 2.0), precision agriculture technologies (agriculture 3.0), the use of such digital technologies and technical means as the Internet of things, artificial intelligence, and robotics. The success of introducing digital agriculture depends on the success of all the three levels of the system. However, the problem of the lack of agricultural machinery indicates insufficient development of mechanized technologies;  poor implementation of precision agriculture technologies means the lack of experience of using these technologies by the majority of farms in our country; an insufficient number of leading Russian IT companies (such as Amazon, Apple, Google, IBM, Intel, Microsoft etc.) weakens the country’s capacity in making a breakthrough in the development of the Internet of things, artificial intelligence, and robotics.Conclusions.The authors have identified the need to form scientific approaches to the digitization of technological operations used in the cultivation of agricultural crops and classified precision agriculture technologies. They have underlined that the digitization of agricultural production in Russia must be carried out along with intensified mechanization (energy saturation); also, to introduce technologies of precision agriculture and digital agriculture, it is necessary to organize state-funded centers for training farmers in the use of these technologies. Finally, it is necessary to take measures to strengthen the development of the IT sphere, as well as formulate an integral approach to the problem of digitalization.

scholarly journals Smart technologies and robotic means in agricultural production

2019 ◽  
Vol 89 (5) ◽  
pp. 536-538
Author(s):  
A. Yu. Izmaylov
Keyword(s):  
Artificial Intelligence ◽  
Crop Production ◽  
Crop Yields ◽  
Animal Husbandry ◽  
Climatic Conditions ◽  
Necessary Condition ◽  
Time Data ◽  
Agricultural Technologies ◽  
Monitoring Tools ◽  
Smart Technologies

A necessary condition for a sharp increase in production is the introduction of digital smart technologies. With the use of digital technologies, it is possible to achieve a significant increase in labor productivity and crop yields, reduce energy and material costs. Digital machine technologies should be used in crop production, animal husbandry, power engineering, storage and processing of agricultural products. The effective production development requires a comprehensive system of management of agricultural enterprises, which, based on the obtained data, will ensure timely and correct processing. In digital machines and agricultural technologies, four main areas can be identified: monitoring of environment and parameters of processes; transmission and storage of information; artificial intelligence and cloud technologies; implementation of management decisions by robotic technical means. The main objects of monitoring are soils, plants, animals, weather and climatic conditions, technical means, and technological processes. Ground and air monitoring tools receive and transmit real-time data to the cloud platform. Artificial intelligence optimizes technological operations and gives a command to the actuators using the monitoring data.

scholarly journals Usage of Artificial Intelligence and Remote Sensing as Efficient Devices to Increase Agricultural System Yields

2021 ◽  
Vol 2021 ◽  
pp. 1-17
Author(s):  
Karim Ennouri ◽  
Slim Smaoui ◽  
Yaakoub Gharbi ◽  
Manel Cheffi ◽  
Olfa Ben Braiek ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Remote Sensing ◽  
Crop Production ◽  
Significant Contribution ◽  
New Technology ◽  
Emerging Technology ◽  
Agricultural System ◽  
Task Load ◽  
Agriculture Sector ◽  
Central Factors

Artificial Intelligence is an emerging technology in the field of agriculture. Artificial Intelligence-based tools and equipment have actually taken the agriculture sector to a different level. This new technology has improved crop production and enhanced instantaneous monitoring, processing, and collection. The most recent computerized structures using remote sensing and drones have made a significant contribution to the agro-based domain. Moreover, remote sensing has the capability to support the development of farming applications with the aim of facing this main defy, via giving cyclic records on yield status during studied periods at diverse degrees and for diverse parameters. Various hi-tech, computer-supported structures are created to determine different central factors such as plant detection, yield recognition, crop quality, and several other methods. This paper includes the techniques employed for the analysis of collected information in order to enhance the productivity, forecast eventual threats, and reduce the task load on cultivators.

OVERVIEW OF BREAKTHROUGH TECHNOLOGIES IN DIGITAL CROP PRODUCTION

2021 ◽  
Author(s):  
T.V. Lemeshko ◽  
Keyword(s):  
Artificial Intelligence ◽  
Internet Of Things ◽  
Agricultural Education ◽  
Crop Production ◽  
Digital Technologies ◽  
Agricultural Practice ◽  
Digital Twins

The article presents breakthrough technologies: agro robotics, artificial intelligence, digital twins, BigData, Internet of Things, blockchain, GIS, etc. The review of breakthrough technologies determines the need for active study and implementation of digital technologies in agricultural practice within the framework of agricultural education and production.

scholarly journals Plant Factory: A New Playground of Industrial Communication and Computing

Sensors ◽  
2021 ◽  
Vol 22 (1) ◽  
pp. 147
Author(s):  
Yu Liu ◽  
Sepehr Mousavi ◽  
Zhibo Pang ◽  
Zhongjun Ni ◽  
Magnus Karlsson ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Crop Production ◽  
Physical Systems ◽  
Vertical Farming ◽  
Plant Factory ◽  
Diverse Application ◽  
Speed Up ◽  
Solution Design ◽  
Application Requirements ◽  
Farming Industry

Plant Factory is a newly emerging industry aiming at transforming crop production to an unprecedented model by leveraging industrial automation and informatics. However, today’s plant factory and vertical farming industry are still in a primitive phase, and existing industrial cyber-physical systems are not optimal for a plant factory due to diverse application requirements on communication, computing and artificial intelligence. In this paper, we review use cases and requirements for future plant factories, and then dedicate an architecture that incorporates the communication and computing domains to plant factories with a preliminary proof-of-concept, which has been validated by both academic and industrial practices. We also call for a holistic co-design methodology that crosses the boundaries of communication, computing and artificial intelligence disciplines to guarantee the completeness of solution design and to speed up engineering implementation of plant factories and other industries sharing the same demands.

scholarly journals Precision crop production and artificial intelligence – the future of sustainable agriculture

2018 ◽  
pp. 47-58
Author(s):  
Miklós Neményi
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Sustainable Agriculture ◽  
Crop Production ◽  
Alternative Solution ◽  
Basic Question ◽  
Decision Making Process ◽  
The Future

According to Kay et al. (2004, in Shockley et al., 2017), there are seven steps to the decision-making process: 1) Identify the problem or opportunity, 2) Identify the alternative solution, 3) Collect all data and information, 4) Analyse the alternatives and make a decision, 5) Implement the decision, 6) Monitor the results of the decision, 7) Accept responsibility for the decision. The basic question is what kind of tasks we can perform in the decision-making process and what to leave for Artificial Intelligence (AI).

scholarly journals Information technology as a basis of creating “smart” agricultural machinery

2019 ◽  
pp. 171-174
Author(s):  
Mironenko, V.
Keyword(s):  
Artificial Intelligence ◽  
Information Technology ◽  
Agricultural Production ◽  
Crop Production ◽  
Information Technologies ◽  
Intelligent Machines ◽  
Core Technology ◽  
Phase Condition ◽  
Technological Level ◽  
Working Bodies

Purpose. Formalize requirements to information systems “smart” machines to increase efficiency of agro industrial production. Methods. Analysis of the possibilities of improving the efficiency of agricultural production by building a hardware based control systems with elements of artificial intelligence. Synthesis of the systems of automatic control by technological processes of crop production on the basis of modern information technologies. Results. Components of the information “intelligent” machines in the plant. The core technology of information mining. Conclusions. Further development of the maintenance of agricultural production should be based on creating technology 5th technological level, which involves the saturation technique by means of information, computing and electronics for operational changes in modes of working bodies in order to achieve the optimum phase condition of the object being processed. Keywords: operations systems of adaptive management, artificial intelligence, information technology.

Food Crisis-How Artificial Intelligence and Machine Learning Are Solving Humanity’s Greatest Challenge—A Review

2020 ◽  
Vol 17 (9) ◽  
pp. 3839-3843
Author(s):  
Anirudh Kiran ◽  
G. Narayana Raj ◽  
Manjunath B. Talawar
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Crop Production ◽  
Crop Cultivation ◽  
Sustainable Solutions ◽  
Crop Fields ◽  
Pest Infestation ◽  
Guardian Angel ◽  
Growing Conditions ◽  
The Guardian

Humanity is heading towards a crisis. The mammoth task of providing food for 2 billion more people by 2050 is being deliberated by governments, scientists and agriculturists alike. The consequence of climate change has led to erratic and non-uniform crop growth. Floods are inundating agriculture land, drought is making crop cultivation impossible and pests and insects are wiping out entire crop fields. Just as the situation seems to be going out of hand, technology is proving itself to be the guardian angel yet again. With the power of Machine Learning and Artificial Intelligence, scientists are able to understand and predict intolerable growing conditions, identify various weather and pest infestation patterns and provide sustainable solutions, which helps accomplish our ultimate goal—increasing crop production by two-folds in the next thirty years. This paper gives an insight about ways in which artificial intelligence and machine learning are helping humanity overcome one of its biggest challenge.

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