it & agricultre

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IT Project: Artificial Intelligence In Agriculture

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IT for MANAGERS

PGDM FINANCESec-D(2010-12)

IT PROJECT Artificial Intelligence in Agriculture Term 1

ITM Group Members:

NISHANT JAIN SAYAN MAJUMDER ROHAN MITRA PUNIT FRANCIS PURNENDU

AcknowledgementWe would like to take the opportunity to thank everyone concerned whose

support was the basic reason we were able to complete the project successfully. The assignment gave a true exposure to the actual functioning of the AI in Agriculture. This is the exact time to get acquainted with the facts of the AI and its uses in human life mainly focusing on agriculture .

This assignment has definitely helped in creating a very clear picture of the working of the AI in agriculture, its styles and trends .

At the beginning of our report we want to thank a few important people without whom we would not have been able to complete my training.

To begin with we would like to thank my Mr. Rupesh Kumar Sinha (Project Coordinator) who helped me to do our project successfully.

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Project Contents Page No.

1. Artificial Intelligence 01

2. Applications of Artificial Intelligence

3. Need of Artificial Intelligence in Our life

4. Artificial Intelligence in Agriculture

5. Sectors in Agriculture

6. Conclusions

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Artificial Intelligence (AI) is the area of computer science focusing on creating machines that can engage on behaviours that humans consider intelligent. The ability to create intelligent machines has intrigued humans since ancient times and today with the advent of the computer and 50 years of research into AI programming techniques, the dream of smart machines is becoming a reality. Researchers are creating systems which can mimic human thought, understand speech, beat the best human chess player, and countless other feats never before possible. Find out how the military is applying AI logic to its hi-tech systems, and how in the near future Artificial Intelligence may impact our lives.

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CONTENT 1

Intelligence and Artificial Intelligence What do we mean by Intelligence or intelligent person? Generally we say in education, the ability to learn or understand or to deal with new or challenging situations. In psychology, the term may more specifically denote the ability to apply knowledge to manipulate one’s environment or to think abstractly as measured by objective criteria (such as the IQ test). Intelligence is usually thought of as deriving from a combination of inherited characteristics and environmental (developmental and social) factors.

So Now a days when the world understand only machine language then they have thought some different to work with their thinking capability also. The biological meaning of Intelligence has change into mechanical. We termed this mechanical language as Artificial Intelligence.

Now what is Artificial Intelligence

Artificial Intelligence (AI) is the area of computer science focusing on creating machines that can engage on behaviours that humans consider intelligent. The ability to create intelligent machines has intrigued humans since ancient times and today with the advent of the computer and 50 years of research into AI programming techniques, the dream of smart machines is becoming a reality. Researchers are creating systems which can imitate human thought, understand speech, beat the best human chess player, and countless other feats never before possible. Find out how the military is applying AI logic to its hi-tech systems, and how in the near future Artificial Intelligence may impact our lives.

Mechanical or "formal" reasoning has been Developed by philosophers and mathematicians since antiquity. The study of logic led directly to The invention of the programmable digital Electronic computer, based on the work of

Mathematician Alan Turing and others. Turing's theory of computation suggested that a machine, by shuffling symbols as simple as "0" and "1", could simulate any conceivable act of mathematical deduction.[23] This, along with recent discoveries in neurology, information theory and cybernetics, inspired a small group of researchers to begin to seriously consider the possibility of building an electronic brain.

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The field of AI research was founded at a conference on the campus of Dartmouth College in the summer of 1956. The attendees, including John McCarthy, Marvin Minsky, Allen Newell and Herbert Simon, became the leaders of AI research for many decades. They and their students wrote programs that were, to most people, simply astonishing: computers were solving word problems in algebra, proving logical theorems and speaking English. By the middle of the 1960s, research in the U.S. was heavily funded by the Department of Defence and laboratories had been established around the world. AI's founders were profoundly optimistic about the future of the new field: Herbert Simon predicted that "machines will be capable, within twenty years, of doing any work a man can do “and Marvin Minsky agreed, writing that "within a generation ... the problem of creating 'artificial intelligence' will substantially be solved".

They had failed to recognize the difficulty of some of the problems they faced. In 1974, in response to the criticism of England's Sir James Lighthill and ongoing pressure from Congress to fund more productive projects, the U.S. and British governments cut off all undirected, exploratory research in AI. The next few years, when funding for projects was hard to find, would later be called an "AI winter".

In the early 1980s, AI research was revived by the commercial success of expert systems, a form of AI program that simulated the knowledge and analytical skills of one or more human experts. By 1985 the market for AI had reached over a billion dollars. At the same time, Japan's fifth generation computer project inspired the U.S and British governments to restore funding for academic research in the field.[36] However, beginning with the collapse of the Lisp Machine market in 1987, AI once again fell into disrepute, and a second, longer lasting AI winter began.

In the 1990s and early 21st century, AI achieved its greatest successes, albeit somewhat behind the scenes. Artificial intelligence is used for logistics, data mining, medical diagnosis and many other areas throughout the technology industry.[9] The success was due to several factors: the incredible power of computers today (see Moore's law), a greater emphasis on solving specific sub problems, the creation of new ties between AI and other fields working on similar problems, and above all a new commitment by researchers to solid mathematical methods and rigorous scientific standards.[38]

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Content 2:- Application of Artificial Intelligence

Finance

Banks use artificial intelligence systems to organize operations, invest in stocks, and manage properties. In August 2001, robots beat humans in simulated financial trading competition financial institutions have long used artificial neural network systems to detect charges or claims outside of the norm, flagging these for human investigation.

Medicine

In medical industry also AI Can is used effectively. A medical clinic can use this technique to organise their medical stock, admission, staffs absenteeism etc.... Artificial neural networks are used for medical diagnosis (such as in Concept Processing technology in EMR software), functioning as machine differential diagnosis. EMR Experts is the leading broker of the most highly regarded Electronic Medical Record (EMR) & Medical Billing Software.

Heavy industry & Robotics

Robots have become common in many industries. Robots have evolved over the last 50 years from teleported machines – mechanical devices operated remotely by humans. Robots have proven effective in jobs that are very repetitive which may lead to mistakes or accidents due to a lapse in concentration and other jobs which humans may find degrading. General Motors Corporation uses around 16,000 robots for tasks such as painting, welding, and assembly. Japan is the leader in using and producing robots in the world. In 1995, 700,000 robots were in use worldwide; over 500,000 of which were from Japan. For more information, see survey about artificial intelligence in business.

Transportation

In transport sector also Ai plays an important role. A fuzzy

Control system is a control system based on fuzzy logic—a

Mathematical system that analyzes analogy input values in

Terms of logical variables Fuzzy logic controllers have been

Developed for automatic gearboxes in automobiles. It

Generally uses in machine control. Audi TT, VW Toureg and VW Caravell feature the DSP transmission which utilizes Fuzzy logic.

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Telecommunications

Many telecommunication companies using this artificial intelligence for their management work force. For example BT Group has deployed heuristic in a scheduling application that provides the work schedules of 20000 engineers.

Agriculture

In the domain of agriculture, the utilization of already developed models in a broad area is often hindered. One frequent factor which impedes transportation is model inaccuracy. For example, when models that perform well in one region, are transported to be used in a different region, they often do not give accurate output (such as, recommendations, results, and/or indicators) in their new environment (i.e., when they are run in a new region). The general component created by this combinational methodology will here be called an ’Agricultural Model-GA’ or an AGMOD-GA. The theory of this adaptation methodology is that by utilizing historical data from a particular region, a model’s parameter settings can be adapted so that the new parameters allow the model to work well in the particular region.

Toys and games

The 1990s saw some of the first attempts to mass-produce domestically aimed types of basic Artificial Intelligence for education. This prospered greatly with the Digital Revolution, this idea invented digital toys or electronic toys which can entertain more children, to a life of dealing with various types of AI, specifically in the form of Tamagotchis and Giga Pets, the Internet (example: basic search engine interfaces are one simple form), and the first widely released robot, Furby. A mere year later an improved type of domestic robot was released in the form of Aibo, a robotic dog with intelligent features and autonomy. AI has also been applied to video games.

Music

The evolution of music has always been affected by technology. With AI, scientists are trying to make the computer emulate the activities of the skilful musician. Composition, performance, music theory, sound processing are some of the major areas on which research in Music and Artificial Intelligence are focusing.

Aviation

The Air Operations Division AOD, uses AI for the rule based expert systems. The use of artificial intelligence in simulators is proving to be very useful for the AOD. Airplane simulators are using artificial intelligence in order to process the data taken from simulated flights. Other than simulated flying, there is also simulated aircraft warfare. The computers are able to come up with the best success scenarios in these situations. The computers can also create strategies based on the placement, size, speed, and strength of the forces and counter forces. The artificial intelligent programs can sort the information and provide the pilot with the best possible manoeuvres, not to

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mention getting rid of certain manoeuvres that would be impossible for a sentient being to perform. The computer simulated pilots are also used to train future air traffic controllers.

In2003 NASA made an F-15 .made of newly developed soft wares emerging with Artificial Intelligent which was a Triumph in AI. The Intelligent Flight Control System was tested on an F-15 [3], which was heavily modified by NASA. The software compensates for all the damaged components by relying on the undamaged components. The neural network used in the software proved to be effective and marked a triumph for artificial intelligence.

Content 3:-

Needs of Artificial IntelligenceToday artificial intelligence is already a major part of our lives. For example there are several separate AI based systems just in Microsoft Word. The little paper clip that advises us on how to use office tools is built on a Bayesian belief network and the red and green squiggles that tell us when we've misspelled a word or poorly phrased a sentence grew out of research into natural language. However, you could argue that this hasn't made a positive difference to our lives; such tools have just replaced good spelling and grammar with a labour saving device that results in the same outcome. For example I compulsively spell the word 'successfully' and a number of other word with multiple double letters wrong every time I type them, this doesn't matter of course because the software I use automatically corrects my work for me thus taking the pressure off me to improve. The end result is that these tools have damaged rather than improved my written English skills. Speech recognition is another product that has emerged from natural language research that has had a much more dramatic effect on people's lives. The progress made in the accuracy of speech recognition software has allowed a friend of mine with an incredible mind who two years ago lost her sight and limbs to septicaemia to go to Cambridge University. Speech recognition had a very poor start, as the success rate when using it was too poor to be useful unless you have perfect and predictable spoken English, but now it’s progressed to the point where it’s possible to do on the fly language translation. The system in development now is a telephone system with real time English to Japanese translation. These AI systems are successful because they don't try to emulate the entire human mind the way a system that might undergo the Turing test does. They instead emulate very specific parts of our intelligence. Microsoft Words grammar systems emulate the part of our intelligence that judges the grammatical correctness of a sentence. It doesn't know the meaning of the words, as this is not necessary to make a judgement. The voice recognition system emulates another distinct subset of our intelligence, the ability to deduce the symbolic meaning of speech. And the 'on the fly translator' extends voice recognitions systems with voice synthesis. This shows that by being more accurate with the function of an artificially intelligent system it can be more accurate in its operation.

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Content 4:- Artificial intelligence in agriculture

The Automated Land Evaluation System, or ALES, is a land information system which allows countries to determine the crops which are physically and economically best suited to their respective land units. ALES allow land evaluators to build expert systems to evaluate land according to the method presented in the Food and Agriculture Organization "Framework for Land Evaluation" (FAO 1976). It is intended for use in project or regional scale land evaluation. The entities evaluated by ALES are map units, which may be defined either broadly (as e.g. in reconnaissance surveys and general feasibility studies) or narrowly (as e.g. in detailed resource surveys and farm-scale planning).

ALES' function is basically to match the land attributes to crop requirements and to determine the most suitable options for land use, including in the analysis socio-economic variables such as cost. ALES is being piloted in three Eastern Caribbean countries

Evaluators build their own expert systems with ALES, taking into account local conditions and objectives. ALES are not by itself an expert system, and does not include by itself any knowledge about land and land use. ALES are a framework within which evaluators can express their own, local, knowledge.

ALES have seven components:-

1. A framework for a knowledge base describing proposed land uses, in both physical and economic terms; 2. A framework for a database describing the land areas to be evaluated; 3. An inference mechanism to relate these two, thereby computing the physical and economic suitability of a set of map units for a set of proposed land uses; 4. An explanation facility that allows model builders to understand and fine-tune their models; 5. A consultation mode that allows a casual user to query the system about one land use at a time; 6. A report generator (on-screen, to a printer, or to disk files); and 7. An import/export module that allows data to be exchanged with external databases, geographic information systems, and spreadsheets. This includes the ALIDRIS interface to the IDRISI geographic information system as well as an interface to xBase (dBase III+) - format database files, including Attribute Tables in PC-Arc/Info

ALES are not a GIS and do not display maps. It can, however, analyze geographic land characteristics if map units are appropriate defined, and it can directly reclassify IDRISI maps or Arc/Info Attribute Tables with the same mapping unit legend as the ALES database.

Given its characteristics, ALES is a good candidate for being used as a decision support system for sustainable land-use purposes. Due to its flexibility, environmental and socioeconomic attributes that are considered to be important for sustainability could be used to define desirable land

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qualities. The definition of the appropriate data for estimating sustainability to be collected and input into the system would be the main challenge left to overcome.

Application of artificial neural networks in mage recognition and classification of crop and weeds.

The objective of this study was to develop a back propagation artificial neural network (ANN) model that could distinguish young corn plants from weeds. Although only the colour indices associated with image pixels were used as inputs, it was assumed that the ANN model could develop the ability to use other information, such as shapes, implicit in these data. The 756x504 pixel images were taken in the field and were then cropped to 100x100-pixel images depicting only one plant, either a corn plant or weeds. There were 40 images of corn and 40 of weeds. The ability of the ANNs to discriminate weeds from corn was then tested on 20 other images. A total of 80 images of corn plants and weeds were used for training purposes. For some ANNs, the success rate for classifying corn plants was as high as 100%, whereas the highest success rate for weed recognition was 80%. This is considered satisfactory, given the limited amount of training data and the computer hardware limitations.Therefore, it is concluded that an ANN-based weed recognition system can potentially be used in the precision spraying of herbicides in agricultural fields.Significant progress in the development of machine vision and image processing technology has been made in the past few years in conjunction with improvements in computer technology (Baxes 1994). Equipment for machine vision and image processing has been reduced in cost, size, and weight, can beinstalled in most vehicles (e.g., tractors), and is accessible for civilian use. Machine vision and image processing are usedincreasingly in biology, materials science, photography, and other fields (Baxes 1994). Many experiments have suggested that machine vision can be used to recognize and localize weeds in agricultural fields (Anonymous 1994a, 1994b; Blackmer and Schepers 1996; Meyer et al. 1997; Schmoldt et al. 1997; Staff and Benlloch 1997). It might therefore be used to control sitespecific spraying herbicide application, thus reducing bothenvironmental pollution from the overuse of agrochemicals, as well as the cost of weed control.It is presently quite difficult to use machine vision to distinguish weeds from the main crop in real time, due to the substantial computational resources and the complicatedalgorithms required. Artificial neural networks (ANNs) can overcome some of these difficulties by interpreting images quickly and effectively. ANNs are composed of numerous processing elements (PEs) arranged in various layers, with interconnections between pairs of PEs (Haykin 1994; Kartalopoulos 1996; Kasabov 1996). They are designed to emulate the structure of natural neural networks such as those of a human brain. For most ANNs, PEs in each layer are fullyconnected with PEs in the adjacent layer or layers, but are not connected to other PEs in the same layer. The PEs simulate the function of the neurons in natural neural networks, while the interconnections between them mimic the functions of dendrites and axons. There have been many applications of ANNs reported for the interpretation of images in the agri-food industry. Studies have shown that for the interpretation of images ANNs can be as accurate as procedural models (Deck et al. 1995; Timmermans and Hulzebosch 1996). For example, the accuracy of classification of potted plants can be greater than 99% (Timmermans and Hulzebosch 1996), apples can be graded by colour with an accuracy of 95% (Nakano 1997), the classification of logs for defects using computed tomography imagery can be 95% accurate (Schmoldt et al. 1997), and theaccuracy for the classification of wheat kernels by colour can be 98% or more (Wang et al. 1999). Generally, ANNs can efficiently model various input/output relationships with the advantage of requiring less execution

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time than a procedural model (Yang et al. 1997a, 1997b). These features make theANN approach very appealing for real-time image processing.

Simulation: Biomass growth model

Agricultural models and decision support systems are becoming increasingly available for a wide audience of users. The Great Plains Framework for Agricultural Resource Management (GPFARM) DSS is a strategic planning tool for farmers, ranchers, and agricultural consultants that incorporates a science simulation model with an economic analysis package and multi-criteriadecision aid for evaluating individual fields or aggregating to the entire enterprise. The GPFARM DSS is currently being expanded to include 1) better strategic planning by simulating a greater range of crops over a wider geographic range and management systems, 2) incorporating a tactical planning component, and 3) adding a production, environmental, and economic risk component. The plant growth component within the science simulation model is subdivided into separate submodels for crops and rangeland forage. User requirements have determined that the DSS must be easy to use in terms of setup, and therefore little parameterization or calibration for a specific site can be required. Based on evaluation of both the crop and rangeland forage growth module of GPFARM, improvements are needed to more accuratelysimulate plant responses to varying levels of water availability. This paper presents our approach and some preliminary results for improving the plant growth models.Our approach is based on using the stand-alone plant growth model derived from the Wind Erosion Prediction System (WEPS), which is based on the EPIC plant growth model. Steps that should improve the plant growth models include 1) incorporate modifications from ork done to other models that are based on the EPIC plant growth model (e.g., GPFARM; Water ErosionPrediction Project, WEPP; ALMANAC; and Soil and Water Assessment Tool, SWAT), and 2) Then there is an AI adaptation methodology designed to assist in transporting agricultural models between regions is presented. Models frequently need adaptation when transported because models developed in one region often do not produce valid results when used in a different region. The methodology prescribes the linkage of a genetic algorithm to a model. This makes the model more robust because it is able to adapt to the region in which it is being used. This methodology has been implemented within a DSS, and preliminary testing indicates this methodology has the ability to allow agricultural models developed in one In the domain of agriculture, the utilization of already developed models in a broad area is often hindered by one or more factors. One frequent factor which impedes transportation is model inaccuracy. For example, when models that perform well in one region, are transported to be used in a different region, they often do not give accurate output (such as, recommendations, results, and/or indicators) in their new environment (i.e., when they are run in a new region). This is one of the major difficulties of model technology transfer. To address this difficulty, an artificial intelligence (AI) methodology is proposed. At the heart of this methodology is a genetic algorithm (GA) (an AI search technique) which is linked to the agricultural model engine (e.g., a risk assessment, provisional, or crop growth model). The general component created by this combinational methodology will here be called an ’Agricultural Model-GA’ or an AGMOD-GA. The following sections will describe the overall structure and elements of this methodology, the generic component created by following this methodology, and discuss the application of this methodology.

DESCRIPTION OF THE AI METHODOLOGY FOR ADAPTING AGRICULTURAL MODELS

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The theory of this adaptation methodology is that by utilizing historical data from a particular region, a model’s parameter settings can be adapted so that the new parameters allow the model to work well in the particular region. This adaptation is done by trying to match the model parameter settings to the particular region. To find matching model parameter settings, intelligent search are performed which utilizes historical data as part of the objective function.Overall, by following this methodology, a component will be created which can search for good model parameter settings such that when the given model is applied and run at the location in question, the output values given will be consistent with the historical outcome data; moreover it is hoped that this will also allow the model to be generally used in this region, producing accurate output values on data which it has not seen. The component that performs this search/adaptation can be called an expert system component since it intelligently modifies and adjusts a model to work in a new location in the same way an expert would modify and adjust a model.Additionally, it should be emphasized that this methodology is particularly appealing because it is not a strictly empirical or analytical, but both. That is, this methodology does not perform a search to fit the historical data from a particular location into an empirical algorithm; rather it performs the search in a larger context, fitting the model parameter settings to a particular location. Therefore, the resulting instantiation of the adapted/localized agricultural model (with the new parameter settings inside) is as good (or as bad) as the original model; consequently, if the model is biologically significant (e.g., if it simulates biological events) then this is not lost by this adaptation methodology since the model is used in the same form (i.e., the structure of the model is left intact), only the model parameter settings are changed.

3. ELEMENTS OF THIS AI METHODOLOGYIn general, this methodology prescribes the utilization of:(i) historical situation data,(ii) historical outcome data,(iii) the agricultural model, and(iv) an intelligent search method (in this case, a genetic algorithm, also called a GA, which is an artificial intelligence search technique).Historical situation data is the basic data required by the model in question. In the domain of agricultural models, this often includes meteorological data since this is frequently an important input to the model. In this methodology, the more historical situation data that is available, the better. The presence of historical outcome data plays a large part in how accurately a model will be adapted using this methodology. This is due to the basic fact that models accepts situations and computes outcomes. The historical outcome data will be used to fit the model parameter settings to the new region in question. Therefore, when constructing a component using the methodology described here, it must be possible to match model outputs to some combination of outcomes and/or events in the real-world (and there must be one-to-one correspondence). For risk assessment models, historical outcome data regards the occurrence of fungus or pest problems in past years (epidemiological data); or for crop growth models, historical outcome data regards crop yield in past years. In this methodology, the agricultural model (i.e., the engine or core of this model) is fundamental because it will be used to obtain evaluations of how well particular model parameter settings work in the given region (i.e., with the given data). In particular, the intelligent search method will repeatedly call upon this model engine as it constructs new model parameter settings that need to have their worth evaluated. This methodology has the capability to address many types of agricultural models: risk assessment models, damage prediction models, crop growth simulation models, etc. The intelligent search method is an important part of this component because, in this particular domain of agricultural models, knowledge of the domain is often hard to codify (i.e., ’rules of thumb’ are vague and difficult to construct), and the selection of an intelligent search method can help to alleviate this difficulty. This is due to the fact that intelligent search methods do not rely on

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’rules of thumb’, rather, rules are not required and these methods can actually facilitate the user in identifying ’rules of thumb’. The selection of the actual intelligent search method to be employed was made among the following possible methods: hill-climbing, simulated annealing, and genetic algorithms (GAs). In the end, GAs were selected as the most desirable method based on the arguments given in Goldberg (1989), Grefenstette (1985, 1987), and Schaffer (1989).4. AGRICULTURAL MODEL-GA (AGMOD-GA) To allow a GA to search the space of an agricultural model’s parameters, the agricultural model is linked to a GA, and the GA uses the model as the evaluation function. Furthermore, the model uses the historical situation data (as this is necessary to run the model in the given historical years), and the GA additionally uses the historical outcome data (discussed earlier) in combination with the output of the model. Whenever the GA wants to evaluate one instance of model parameter settings, the agricultural model is called, and the final outcome is returned through an objective function to the GA so that a fitness can be computed. The resulting general component created by employing this combinational methodology can be called an ’Agricultural Model-GA’ or an AGMOD-GA. Figure 1 illustrates an agricultural model and a GA linked to form an AGMOD-GA.Figure 1. Structure of an AGMOD-GAThe function of the AGMOD-GA is to find near-optimal model parameter settings for the given desired behaviour (i.e., matching the given historical outcome data). There are three main steps involved in the execution of a typical AGMOD-GA. First, the agricultural model and the GA are initialized. With the simple GA implemented in this case, an entirely random initial set (i.e., population) of parameter settings is generated. This has the effect of starting the search in a number of different random points in the space. A collection of random starting points does not have a negative effect on GA performance because a GA searches from many different point at the same time, not just from one point. The second step in a typical AGMOD-GA is the fitness computation (i.e., valuation of each population member’s worth). This involves taking each GA population member and executing one or more model executions using the model parameter settings represented by this member. These model executions utilize the user-provided historical situation data, with one execution initiated for each one of these sets of data. The outcomes from thesemodel executions are compared against the user-provided historical outcome data (which is the target). The further the model outputs are from the historical outcome data, the lower the fitness, and inversely, the closer the model outputs are, the higher the fitness. This fitness evaluation step is executed many times because new population members are continually being generated by the GA. Fitness evaluation is usually continued until the GA has converged on a suitable optimal or quasi-optimal solution. The last step is the evolution of the GA population. This involves applying operations to the population members. The three operators used in a typical GAs are reproduction, crossover, and mutation. They act by treating the GA bit strings (which represent model parameters) in a way analogous to the evolution of chromosomes in genetics (Goldberg, 1989).

5. APPLICATION OF THE METHODOLOGYAn example of applying the above described methodology to create a real AGMOD-GA component isgiven. To put this example in context, the project under which this methodology was developed (Project SYBIL) and the DSS which utilizes this methodology (one of the SYBIL DSSes which focuses on grapes and apples) are outlined.

5.1. Description of Project SYBILEC Project SYBIL (consisting of five partners from four countries) involves the implementation ofcomputerized decision support systems (DSSes) to assist farmers in intelligently governing their crops such that environmental impact is reduced and economic returns are increased. Existing agro-meteorological computer models from multiple sources are integrated into the portable, user-friendly DSSes designed to assess the risk of a crop to pest and fungus damage. By evaluating this

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risk, the farmer has the option to apply pesticides and fungicides only when needed and avoid using these, often environmentally damaging, chemicals blindly on a regular basis or when the risk of pest and fungus damage is small. This evaluation has the potential to save the farmer both time and money because expensive chemicals will not be applied when they do not benefit the crop.

5.2. DSS DescriptionThe SYBIL DSS described here (which includes the AI methodology discussed) is targeted to grape and apple growers. Figure 2 displays the first screen of this DSS. The model focused on for illustrating the adaptation methodology described here will be the P.R.O. model for grapes.Figure 2. The System’s Main Screen5.3. Description and Origin of the P.R.O. ModelThe P.R.O. (Plasmopara Risikoprognose Oppenheim or Plasmopara Risk Oppenheim) model for grapes was the first model selected for the application of the adaptation/localization methodology discussed above.This analytical model is a biological life cycle model that simulates the infection and growth of downy mildew (viz. Plasmopara viticola, also called peronospora) on grape vines based on meteorological conditions. The model was developed in Rheinhessen, Germany by Dr. Georg K. Hill (Hill 93). It was designed to help growers determine when it is necessary to spray grape vines against peronospora. The model had been used by multiple Rheinhessen region farmers with good results; that is, the information provided to the farmer has assisted in the making of intelligent decisions about when to perform the first spray of the season against peronospora. The goal is to overcome the habit (which is not based on temporal information) of performing the first spray early in the season (possibly around May), which is often before it is necessary. This goal is approached by using the P.R.O. model to produce interpreted-operational temporal information (i.e., useful up-to-date information about the status of the peronospora growth), then examining this information, and deciding if it necessary to spray at the current moment, or if spraying can be delayed (possibly many weeks beyond when growers would traditionally perform the first spray) because the grape vines are not currently at risk to being damaged by peronospora. In the common caseswhere spraying can in fact be delayed beyond when an agriculturalist would normally spray, the overall number of interventions and amounts of chemicals sprayed on the crop are reduced. Agriculturalist usingthis model in the region around Rheinhessen have been able to save between one and four sprayapplications per year, with an average saving of two (Hill 93).5.4. Results of Transporting the P.R.O. ModelAfter deciding that the P.R.O. model was a good choice for inclusion into the DSS (and therefore a good choice for trying to transfer this model between countries), an instantiation of the model (with only small changes so that the model would accept other types of meteorological data) was programmed into the DSS, and test runs were made with various data from other regions (e.g., Würzburg, Germany and Trentino, Italy). Upon running these tests, it was found that the output values (which in the case of the P.R.O. model are: the primary infection date, the end of the incubation period, a list of special night occurrences, and a recommended spray date), were inaccurate in the new regions. That is, the P.R.O. model outputs were rejected by agricultural experts based on their historical epidemiological data (more generally, their historical outcome data) and general knowledge of when epidemiological events occur in their regions.For example, Table 1 displays the results from running the P.R.O. model with data from an area inside the Trentino region of Italy. As this table shows, the dates produced by the original P.R.O. model using original model parameter settings (i.e., model parameter settings selected by Dr. Hill for the Rheinhessen area) (these dates shown in the column titled "Case 1") for data coming from Trentino, only approached the dates known to be correct from observations done by agricultural experts in Trentino (these dates shown in the column titled "Actual Dates") for the primary infection dates (rows titled "Prim Inf 19xx"). For the recommended spray dates (rows titled "Rec Spray 19xx"),

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the model could not even produce estimates of this date (with this data from Trentino) for two out of the three years in which actual dates were available for comparison. Therefore, the model in this state is of little use to agriculturalists in Trentino since it is generally not able to produce an accurate estimate of the recommended spray date. Additionally, the difficulties observed in this case also held true for data taken from other regions, so overall the P.R.O. model was problematic because it did not give accurate output when run in regions external to where it was developed.

Multi-Crop Plant Growth Modeling for AgriculturalModels and Decision Support SystemsEXTENDED ABSTRACTAgricultural models and decision support systems are becoming increasingly available for a wide audience of users. The Great Plains Frameworkfor Agricultural Resource Management (GPFARM) DSS is a strategic planning tool for farmers, ranchers, and agricultural consultants that incorporates a science simulation model with an economic analysis package and multi-criteria decision aid for evaluating individual fields or aggregating to the entire enterprise. The GPFARM DSS is currently being expanded to include:- 1) better strategic planning by simulating a greater range of crops over a wider geographic range and management systems, 2) incorporating a tactical planning component, and3) adding a production, environmental, and economic risk component.

The plant growth component within the science simulation model is subdivided into separate submodels for crops and rangeland forage. User requirements have determined that the DSS must be easy to use in terms of setup, and therefore little parameterization or calibration for a specific site can be required. Based on evaluation of both the crop and rangeland forage growth module of GPFARM, improvements are needed to more accurately simulate plant responses to varying levels of water availability. This paper presents our approach and some preliminary results for improving the plant growth models. Our approach is based on using the stand-aloneplant growth model derived from the Wind Erosion Prediction System (WEPS), which is based on the EPIC plant growth model. Steps that should improve the plant growth models include 1) incorporate modifications from work done to other models that are based on the EPIC plant growth model (e.g., GPFARM; Water Erosion Prediction Project, WEPP; ALMANAC; and Soil and Water Assessment Tool, SWAT), and 2) thoroughly evaluate how the plant processes are represented in these models. Deficiencies in adequately simulating plant growth responses to water availability can fall under two general categories: inadequate quantification of the process or omission of a needed process in the model. High priority needs identified to dateinclude:- 1) seedling emergence, 2) phenology, 3)biomass generation, 4) biomass partitioning, 5) root growth, and 6) plant stress factors.

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Initial work has created stand-alone submodels for predicting seedling emergence (as a function of soil water and thermal time) and phenology (by predicting specific growth stages and responses to different levels of soil water availability). Evaluation of alternative approaches for generating biomass (e.g., radiation use efficiency, transpiration use efficiency, plant growth analysis), biomass partitioning (e.g., modifications to generating LAIand partitioning coefficients partly based on better phenology prediction), and stress factors (e.g.,single-most limiting, additive, multiplicative) is underway. We envision that these modificationsand enhancements should improve model responses to varying levels of soil water availability.

GREENHOUSE ENVIRONMENTAL CONTROL SYSTEM WITH A CROP MODEL AND AN EXPERT SYSTEMAbstract: Most greenhouse controllers are for automating greenhouse operation, and few crop models and expert systems have been utilized in greenhouse control programs to maximize the production of the crop grown in the greenhouse. This paper shows a way of controlling the greenhouse environment using a built-in crop model and an expert system.

The new control system produced reasonable temperature set values. Although the absolute values from the system needs to be verified by sequential, seasonal experiments, it showed the potential for automation in the optimization of greenhouse production.

GREENHOUSES DETECTION USING AN ARTIFICIAL NEURAL NETWORK WITH A VERY HIGH RESOLUTION SATELLITE IMAGE

ABSTRACT:

Detecting and locating greenhouses in south-east of Spain is very important for politicians and other persons who may take decisions about management of natural resources and who must design agricultural development plans. Agriculture is one of more important economic activities in this zone, and till now, development and disposition of new greenhouses was uncontrolled. In this paper, we present a methodology to detect greenhouses from 1.5 m pixel size QuickBird image, based in Artificial Neural Network algorithm. Thanks to the information introduced as training sites, we “teach” to the mathematical model to classify the image considering its radiometric and wavelet texture properties. This assessment is known as training, and the algorithm to obtain it, back-propagation. Classification accuracy was evaluated using multi-source data, comparing results including and no-including wavelet texture analysis. We conclude that some texture analysis can not improve classification accuracy but if one choose correctly parameters and texture model, it can become better. Actually we are working on automatic detection and actualization of greenhouses distributions.

1. INTRODUCTIONClassification is the process by which we develop thematic maps from remote sensors images. Traditionally this process was reached applying visual interpretation and drawing its boundaries manually. Nevertheless with computer apparition, scientists focused in computed aided interpretation. Information obtained from classified image can help us to designing of development

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planning or to take decision about natural resources anagement. In this work, located in Campo de Níjar region, in south-east of Spain, classified image will help to plan agricultural development or to know greenhouses distribution changes due in last years. Greenhouses arestructures with the aim of protecting horticultural products of environments agents, mainly the win. Its plastic roofs have a characteristic radiometric behaviour in which classification algorithms base to separate this class of other different classes. Nevertheless, angle vision, plastic chemical composition, age, and photosynthetic activity under plastic can change dramatically it spectral signature. Besides, greenhouse’s owner paint plastic roof with white colour to decrease sun radiation inside in summer time. Due maintenance operations between different crop cycles, plastic roof may temporally disappear in greenhouses. So, all this motives can deteriorate accuracy ingreenhouse classification. There are two traditional approaches in classification of images:supervised and unsupervised. The first one classification of pixel based on characteristics of classes. To characterize a class, operator must to delineate pixel groups corresponding to each interesting class, called training sites. So, a pixel will belong to the class more statistically similar to own properties. In the second one, classification algorithm produces more frequent land cover by the clustering method. Artificial Neural Network is a supervised classification method, recently applied to land use/cover change detection (Xiou et al., 2002). This is a non-linear mathematical model that imitates the way in which human brain interprets graphical information.This particular parcel of Artificial Intelligence doesn’t require hypothesis a priori about distribution functions or another statistical assumptions. Besides, thanks to the previous learning approach, a neural network is able to classify unknown data. The aim of this work was to design an appropriate methodology to obtaining classified images from very high resolution satellite. The resultant classification is focused in greenhouses class in south-east of Spain and so, we studied the best texture analysis to improving accuracy. Actually we claim to get automatic change greenhouses cover detection to adapt it to actualization cartographic and thematic information.2. AREA OF STUDYCampo de Níjar is a region located in province of Almería (Spain) with an approximated surface about 20.000 ha(Figure 1). Figure 1. Area of study Here, there is an intensive agricultural production system based on greenhouses structures. This fact characterizes the landscape, besides coppice natural vegetation, disperse urban cover, a soft.

Content 5:- sectors in agriculture

Agricultural Research Agro-Meteorology Agricultural Marketing

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Agricultural Engineering & Food Processing Agricultural Extension and Transfer of Technology Credit and Cooperation Crop production & protection Environment & Forest Fertilizers and Manure Fisheries Irrigation and Drainage System Livestock, Dairy Development & Animal Husbandry Rural Development & Planning Soil and Water Management Watershed Development Wasteland Development

Why is watershed management planning important?When water flows across the land during rainfall events, it carries fertilizers, loose soil, litter and other pollutants into streams and other surrounding water bodies. As a result, everything we do on the landaffects the quality and quantity of our water resources and the natural systems that surround us. Therefore, the natural resources and the quality of life in our communities are directly affected by the way we plan for and manage land use activities. Watershed management planning provides opportunities to address water quality and habitat issues within the physical boundaries of a watershed rather than political boundaries. It is an inclusive approach to support environmental protection, quality of life issues, and economic development—using the watershed as a holistic planning framework.

Content 6:-

ConclusionThe artificial intelligence (AI) methodology discussed addresses model technology transfer (i.e., the moving of functional and useful agricultural models that are developed in one location to a new location so they can be used in this new location). In particular, it addresses one of the major difficulties within thisarea, namely, model accuracy; that is, it addresses the instance when a useful model is transported from a region where it is functioning accurately (viz. producing accurate recommendations, results, and/or indicators) to a new region where it subsequently does not function accurately.

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The methodology employs four main elements, with a genetic algorithm (GA) at the center. By employing this AI component in conjunction with the engine of an agricultural model and historical data, model parameter settings can be adapted to new locations, allowing the model to give accurate results when run in the new location. Specifically, the module created by this methodology can be applied to localize models by deriving new model parameter settings that can be employed in the particular location to give good suggestions/decision support.With the assumption that model technology transfer is an advantageous action (refer to (Jacucci, et al., 1994) for an elaboration of advantages and disadvantages in transporting models between regions), this AI methodology has been found to efficiently addresses this issue and improves the current state-of-the-art in model technology transfer. This has been shown through an example which describes the utilization of this methodology within one of the decision support systems (DSSes) developed under EC Project SYBIL. In particular, this DSS was designed to provide temporal information to assist grape and apple agriculturalists in the management of crops with respect to controlling fungus and pests. Specifically, this methodology has been applied to an instantiation of the P.R.O. model that is programmed into one of the SYBIL DSSes. This model addresses the infection and growth of Plasmopara viticola on grape vines, and has the capability to provideinformation to a farmer so that decisions regarding when to apply fungicides are made more intelligently. Due to difficulties in transporting this model to run in regions outside where it was developed, our methodology to adapt model parameter settings was employed. The testing of new model parameter settings produced by this adaptation showed that this methodology has great potential to localize model parameter settings, and this should assist in achieving the goal of making sound models more widely available.

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