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 UF/IFAS Standardized Nutrient Recommendations for Vegetable Crop Production in Florida

EDIS ◽  
2022 ◽  
Vol 2021 (6) ◽  
Author(s):  
Rao Mylavarapu ◽  
George Hochmuth ◽  
Guodong Liu
Keyword(s):  
Environmental Management ◽  
Crop Production ◽  
Soil Testing ◽  
Soil Test ◽  
Vegetable Crops ◽  
Basic Information ◽  
Vegetable Crop ◽  
Testing Laboratory ◽  
State And Local ◽  
Local Agencies

This publication presents the fertilization recommendations for vegetable crops based on soil tests performed by the UF/IFAS Extension Soil Testing Laboratory (ESTL). It contains the basic information from which ESTL soil test reports and fertilization recommendations are generated. The audiences for this information include commercial and small farmers, crop advisers and consultants, state and local agencies, fertilizer industry, and any interested individuals interested in sustainable nutrient and environmental management. Major revision by Rao Mylavarapu, George Hochmuth, and Guodong Liu; 12 pp. https://edis.ifas.ufl.edu/cv002

IFAS Standardized Nutrient Recommendations for Vegetable Crop Production in Florida

EDIS ◽  
2017 ◽  
Vol 2017 (6) ◽  
Author(s):  
Rao S. Mylavarapu ◽  
George J. Hochmuth ◽  
Guodong Liu
Keyword(s):  
Crop Production ◽  
Soil Testing ◽  
Soil Test ◽  
Vegetable Production ◽  
Vegetable Crops ◽  
Basic Information ◽  
Vegetable Crop ◽  
Testing Laboratory ◽  
Additional Information ◽  
Agronomic Crops

This publication presents the fertilization recommendations for vegetable crops based on soil tests performed by the IFAS Extension Soil Testing Laboratory (ESTL). It contains the basic information from which ESTL soil-test reports and fertilization recommendations are generated. Additional information on nutrient recommendations is presented in the Vegetable Production Handbook of Florida, 2017-2018. Similarly, IFAS Standardized Nutrient Recommendations for Agronomic Crops can be found in SL129 (Mylavarapu, 2015).  

scholarly journals Conversions of parts per million on soil test reports to pounds per acre

EDIS ◽  
2013 ◽  
Vol 2013 (10) ◽  
Author(s):  
Guodong Liu ◽  
Yuncong Li ◽  
Aparna Gazula
Keyword(s):  
Water Management ◽  
Crop Production ◽  
Nutrient Management ◽  
Soil Testing ◽  
Soil Test ◽  
Vegetable Production ◽  
Fact Sheet ◽  
Conversion Method ◽  
Simple Conversion

December 18th, 2013 Soil testing and the resulting fertilization recommendations are critical for appropriate nutrient management in commercial vegetable production, but growers and soil experts sometimes speak different languages. This 8-page fact sheet provides a simple conversion method for crop consultants, crop advisors, growers, students, and researchers who are interested in nutrient and water management of crop production. Written by Guodong Liu, Yuncong Li, and Aparna Gazula, and published by the UF Department of Horticultural Sciences, August 2013. http://edis.ifas.ufl.edu/hs1229

scholarly journals LSI with Support Vector Machine for Text Categorization – a practical example with Python

2021 ◽  
Vol 6 (3) ◽  
Author(s):  
József Dr. Menyhárt ◽  
Joao Henrique Gomes Da Costa Cavalcanti
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Support Vector Machine ◽  
Programming Language ◽  
Text Categorization ◽  
Latent Semantic Indexing ◽  
Supervised Machine Learning ◽  
Support Vector ◽  
Machine Model ◽  
New Era

Artificial intelligence is becoming a powerful tool of modernity science, there is even a science consensus about how our society is turning to a data-driven society. Machine learning is a branch of Artificial intelligence that has the ability to learn from data and understand its behavers. Python programming language aiming the challenges of this new era is becoming one of the most popular languages for general programming and scientific computing. Keeping all this new era circumstances in mind, this article has as a goal to show one example of how to use one supervised machine learning method, Support Vector Machine, and to predict movie’s genre according to its description using the programming language of the moment, python. Firstly, Omdb official API was used to gather data about movies, then tuned Support Vector Machine model for Latent semantic indexing capable of predicting movies genres according to its plot was coded. The performance of the model occurred to be satisfactory considering the small dataset used and the occurrence of movies with hybrid genres. Testing the model with larger dataset and using multi-label classification models were purposed to improve the model.

Application of Remote Sensing in Agriculture

2014 ◽  
Vol 13 (1) ◽  
Author(s):  
Jan Piekarczyk
Keyword(s):  
Remote Sensing ◽  
Precision Agriculture ◽  
Crop Production ◽  
Satellite Images ◽  
Crop Yields ◽  
Spatial Scales ◽  
Strong Relationship ◽  
Physical Parameters ◽  
Agricultural Crop ◽  
Remote Sensing Methods

AbstractWith increasing intensity of agricultural crop production increases the need to obtain information about environmental conditions in which this production takes place. Remote sensing methods, including satellite images, airborne photographs and ground-based spectral measurements can greatly simplify the monitoring of crop development and decision-making to optimize inputs on agricultural production and reduce its harmful effects on the environment. One of the earliest uses of remote sensing in agriculture is crop identification and their acreage estimation. Satellite data acquired for this purpose are necessary to ensure food security and the proper functioning of agricultural markets at national and global scales. Due to strong relationship between plant bio-physical parameters and the amount of electromagnetic radiation reflected (in certain ranges of the spectrum) from plants and then registered by sensors it is possible to predict crop yields. Other applications of remote sensing are intensively developed in the framework of so-called precision agriculture, in small spatial scales including individual fields. Data from ground-based measurements as well as from airborne or satellite images are used to develop yield and soil maps which can be used to determine the doses of irrigation and fertilization and to take decisions on the use of pesticides.

Effect of Nanobionics in crop production- A Review

2020 ◽  
Vol 10 ◽  
Author(s):  
Vinayak Fasake ◽  
Nita Patil ◽  
Zoya Javed ◽  
Mansi Mishra ◽  
Gyan Tripathi ◽  
...  
Keyword(s):  
Crop Production ◽  
Seed Quality ◽  
Negative Impact ◽  
Potential Solution ◽  
New Era ◽  
Mineral Toxicity ◽  
The Impact ◽  
Favourable Situation ◽  
Unique Chemical ◽  
Macro Nutrients

: Nanobionics involves the improvement of plant or plant productivity using nanomaterials. Growth of a plant from a seed encompasses various factors which are directly or indirectly dependent upon the imbibition of micro and macro nutrients and vital elements from the soil. Since most of the nutrition is physiologically unavailable to the plants, it leads to mineral deficiencies in plant and mineral toxicity in soil. Either ways, it is not a favourable situation for the microcosom. The new era of nanotechnology offers a potential solution to the availability of the nutrients to the plants due to its unique chemical and physical properties of nanoparticles. Positive and negative impact of these nanoparticles on seed quality and plant growth varies according to the specific properties of nanoparticles. The present review is an attempt to summarize the impact of nanobionics in agriculture.

scholarly journals How Do Machines Learn? Artificial Intelligence as a New Era in Medicine

2021 ◽  
Vol 11 (1) ◽  
pp. 32
Author(s):  
Oliwia Koteluk ◽  
Adrian Wartecki ◽  
Sylwia Mazurek ◽  
Iga Kołodziejczak ◽  
Andrzej Mackiewicz
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Health Care ◽  
Medical Data ◽  
General Process ◽  
New Era ◽  
Automated Evaluation ◽  
Machine Learning Applications ◽  
And Training ◽  
Current Standards

With an increased number of medical data generated every day, there is a strong need for reliable, automated evaluation tools. With high hopes and expectations, machine learning has the potential to revolutionize many fields of medicine, helping to make faster and more correct decisions and improving current standards of treatment. Today, machines can analyze, learn, communicate, and understand processed data and are used in health care increasingly. This review explains different models and the general process of machine learning and training the algorithms. Furthermore, it summarizes the most useful machine learning applications and tools in different branches of medicine and health care (radiology, pathology, pharmacology, infectious diseases, personalized decision making, and many others). The review also addresses the futuristic prospects and threats of applying artificial intelligence as an advanced, automated medicine tool.

scholarly journals Economics of autonomous equipment for arable farms

2021 ◽  
Author(s):  
James Lowenberg-DeBoer ◽  
Kit Franklin ◽  
Karl Behrendt ◽  
Richard Godwin
Keyword(s):  
Precision Agriculture ◽  
Environmental Performance ◽  
Crop Production ◽  
Practical Experience ◽  
Production Costs ◽  
Farm Size ◽  
Medium Size ◽  
Economic Implications ◽  
The United Kingdom ◽  
Crop Agriculture

AbstractBy collecting more data at a higher resolution and by creating the capacity to implement detailed crop management, autonomous crop equipment has the potential to revolutionise precision agriculture (PA), but unless farmers find autonomous equipment profitable it is unlikely to be widely adopted. The objective of this study was to identify the potential economic implications of autonomous crop equipment for arable agriculture using a grain-oilseed farm in the United Kingdom as an example. The study is possible because the Hands Free Hectare (HFH) demonstration project at Harper Adams University has produced grain with autonomous equipment since 2017. That practical experience showed the technical feasibility of autonomous grain production and provides parameters for farm-level linear programming (LP) to estimate farm management opportunities when autonomous equipment is available. The study shows that arable crop production with autonomous equipment is technically and economically feasible, allowing medium size farms to approach minimum per unit production cost levels. The ability to achieve minimum production costs at relatively modest farm size means that the pressure to “get big or get out” will diminish. Costs of production that are internationally competitive will mean reduced need for government subsidies and greater independence for farmers. The ability of autonomous equipment to achieve minimum production costs even on small, irregularly shaped fields will improve environmental performance of crop agriculture by reducing pressure to remove hedges, fell infield trees and enlarge fields.

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