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least record the date of spray and the product name. It is known that spraying can have different fruit defects for different fruit. Fungicidal sprays are often used to prevent rots from being expressed on fruit. It is also known that some sprays can cause russeting on apples. Currently much of this knowledge comes anecdotally, however some efforts have been in regards to the use of data mining in horticulture.
66:
402:
data recorded has never been digitized, integrated or standardized to give a complete picture, and hence cannot support decision making, thus requiring an
Agriculture Data Warehouse. Creating a novel Pilot Agriculture Extension Data Warehouse followed by analysis through querying and data mining some interesting discoveries were made, such as pesticides sprayed at the wrong time, wrong
123:
25:
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environmental and social impacts. By data mining the cotton Pest
Scouting data along with the meteorological recordings it was shown that how pesticide use can be optimized (reduced). Clustering of data revealed interesting patterns of farmer practices along with pesticide use dynamics and hence help identify the reasons for this pesticide abuse.
401:
To monitor cotton growth, different government departments and agencies in
Pakistan have been recording pest scouting, agriculture and metrological data for decades. Coarse estimates of just the cotton pest scouting data recorded stands at around 1.5 million records, and growing. The primary agro-met
392:
crop yield maximization through pro-pesticide state policies have led to a dangerously high pesticide use. These studies have reported a negative correlation between pesticide use and crop yield in
Pakistan. Hence excessive use (or abuse) of pesticides is harming the farmers with adverse financial,
213:
Fruit defects are often recorded (for a multitude of reasons, sometimes for insurance reasons when exporting fruit overseas). It may be done manually or through computer vision (detecting surface defects when grading fruit). Spray diaries are a legal requirement in many countries and at the very
371:
are checked and the ones showing some defects are removed. However, there are also invisible defects that can spoil the apple flavor and look. An example of invisible defect is an internal apple disorder that can affect the longevity of the fruit called a watercore. Apples with slight or mild
289:, such as coughs, can be analyzed for the detection of diseases. A computational system is under development which is able to monitor pig sounds by microphones installed in the farm, and which is also able to discriminate among the different sounds that can be detected.
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watercourse are sweeter, but apples with moderate to severe degree of watercore cannot be stored for any length of time. Moreover, a few fruits with severe watercore could spoil a whole batch of apples. For this reason, a computational system is under study which takes
284:
can positively impact the productivity of the farm by reducing contamination to other animals. Moreover, the early detection of the diseases can allow the farmer to treat and isolate the animal as soon as the disease appears. Sounds issued by
230:
have been used to study the process of fermentation in order to predict problematic wine fermentations. These methods differ from techniques where a classification of different kinds of wine is performed. See the wiki page
442:
concentration. The results revealed that a diet containing 18.69% protein and 0.73% threonine may lead to producing optimal weight gain, whereas the optimal feed efficiency may be achieved with a diet containing 18.71%
658:
Ahmadi, Dr H.; Mottaghitalab, M.; Nariman-Zadeh, N.; Golian, A. (2008-05-01). "Predicting performance of broiler chickens from dietary nutrients using group method of data handling-type neural networks".
251:
was used to predict the metabolizable energy of feather meal and poultry offal meal based on their protein, fat, and ash content. Published data samples were collected from literature and used to train a
260:-type network with an evolutionary method of genetic algorithm can be used to predict the metabolizable energy of poultry feed samples based on their chemical content. It is also reported that the
532:
Hill, M. G.; Connolly, P. G.; Reutemann, P.; Fletcher, D. (2014-10-01). "The use of data mining to assist crop protection decisions on kiwifruit in New
Zealand".
710:
Chedad, A.; Moshou, D.; Aerts, J.M.; Van Hirtum, A.; Ramon, H.; Berckmans, D. (2001). "Recognition System for Pig Cough based on
Probabilistic Neural Networks".
139:
Please remove or replace such wording and instead of making proclamations about a subject's importance, use facts and attribution to demonstrate that importance.
222:
The fermentation process of wine impacts the productivity of wine-related industries as well as the quality of the wine. Data science techniques, such as the
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200:. Recent technologies are able to provide extensive data on agricultural-related activities, which can then be analyzed in order to find information.
596:
IbaI Conference
Proceedings, Proceedings of the Industrial Conference on Data Mining (ICDM10), Workshop Data Mining in Agriculture (DMA10), Springer
380:, and which is also able to analyse (by data mining techniques) the taken pictures and estimate the probability that the fruit contains watercores.
611:"Prediction Model for True Metabolizable Energy of Feather Meal and Poultry Offal Meal Using Group Method of Data Handling-Type Neural Network"
264:-type network may be used to accurately estimate the poultry performance from their dietary nutrients such as dietary metabolizable energy,
339:-model is an appropriate tool to recognize the patterns of data to predict lamb growth in terms of ADG given specific genes polymorphism,
559:
Urtubia, A.; Perez-Correa, J.R.; Meurens, M.; Agosin, E. (2004). "Monitoring Large Scale Wine
Fermentations with Infrared Spectroscopy".
764:
Schatzki, T.F.; Haff, R.P.; Young, R.; Can, I.; Le, L-C.; Toyofuku, N. (1997). "Defect
Detection in Apples by Means of X-ray Imaging".
331:(ANN) model was developed to describe average daily gain (ADG) in lambs from input parameters of GH, leptin, calpain, and calpastatin
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Predicting metabolizable energy of poultry feed using group method of data handling-type neural network
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from a compiled data set suggested that the dietary protein concentration was more important than the
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and optimization algorithms was used successfully to integrate published data on the responses of
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used for the right reasons and temporal relationship between pesticide usage and day of the week.
800:. Australasian Workshop on Data Mining and Web Intelligence, Dunedin, New Zealand. Archived from
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Mucherino, A.; Urtubia, A. (2010). "Consistent
Biclustering and Applications to Agriculture".
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860:"The integration of broiler chicken threonine responses data into neural network models"
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It may require cleanup to comply with Knowledge (XXG)'s content policies, particularly
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Abdullah, Ahsan; Brobst, Stephen; Pervaiz, Ijaz; Umar, Muhammad; Nisar, Azhar (2004).
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Recent studies by agriculture researchers in Pakistan showed that attempts of
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Ahmadi, H.; Golian, A.; Mottaghitalab, M.; Nariman-Zadeh, N. (2008-09-01).
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471:, but those are not exclusively devoted to data mining in agriculture.
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Growth of sheep from genes polymorphism using artificial intelligence
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827:"Data Mining a New Pilot Agriculture Extension Data Warehouse"
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Transactions of the American Society of Agricultural Engineers
335:, birth weight, and birth type. The results revealed that the
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Analyzing chicken performance data by neural network models
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Journal of Research and Practice in Information Technology
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programs to design a scheme in enhancing the efficacy of
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Learning Dynamics of Pesticide Abuse through Data Mining
343:, and birth type. The platform of PCR-SSCP approach and
226:, and classification techniques based on the concept of
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Mucherino, A.; Papajorgji, P.J.; Pardalos, P. (2009).
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A major contributor to this article appears to have a
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A group method of data handling-type neural network (
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is a research topic consisting of the application of
16:
Application of data mining techniques to agriculture
737:Mojtaba, Tahmoorespur; Hamed, Ahmadi (2012-01-01).
276:
Detection of diseases from sounds issued by animals
347:-based model analyses may be used in molecular
247:-type network) with an evolutionary method of
209:Relationship between sprays and fruit defects
8:
712:Journal of Agricultural Engineering Research
256:-type network model. The novel modeling of
218:Prediction of problematic wine fermentations
376:photographs of the fruit while they run on
133:promotes the subject in a subjective manner
53:Learn how and when to remove these messages
300:-single strand conformation polymorphism (
875:
626:
397:Explaining pesticide abuse by data mining
173:Learn how and when to remove this message
155:Learn how and when to remove this message
105:Learn how and when to remove this message
534:Computers and Electronics in Agriculture
469:Computers and Electronics in Agriculture
304:-SSCP) method was used to determine the
825:Abdullah, Ahsan; Hussain, Amir (2006).
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384:Optimizing pesticide use by data mining
858:Ahmadi, H.; Golian, A. (2010-11-01).
7:
490:Data Mining in Agriculture, Springer
130:This article contains wording that
135:without imparting real information
14:
34:This article has multiple issues.
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85:. Please discuss further on the
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42:or discuss these issues on the
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573:10.1016/j.talanta.2004.04.005
463:journals, such as Springer's
363:Sorting apples by watercourse
280:The detection of diseases in
840:(3): 229–249. Archived from
546:10.1016/j.compag.2014.08.011
434:models for weight gain and
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186:Data mining in agriculture
673:10.1080/00071660802136908
432:artificial neural network
416:artificial neural network
329:artificial neural network
298:Polymerase chain reaction
367:Before going to market,
323:in Iranian Balochi male
661:British Poultry Science
724:10.1006/jaer.2001.0719
233:Classification of wine
919:Agricultural research
877:10.3382/ps.2010-00884
628:10.3382/ps.2007-00507
508:www.extension.umn.edu
465:Precision Agriculture
461:precision agriculture
83:neutral point of view
420:sensitivity analysis
914:Applied data mining
778:10.13031/2013.21367
418:-based models with
430:. Analyses of the
235:for more details.
870:(11): 2535–2541.
743:Livestock Science
504:"Apple russeting"
249:genetic algorithm
224:k-means algorithm
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359:production.
341:birth weight
333:polymorphism
321:polymorphism
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228:biclustering
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204:Applications
194:data science
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95:October 2016
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36:Please help
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540:: 250–257.
318:calpastatin
270:amino acids
198:agriculture
190:data mining
908:Categories
811:2010-07-20
598:: 105–113.
518:2016-10-04
475:References
455:Literature
447:and 0.75%
404:pesticides
145:April 2017
39:improve it
886:0032-5791
751:1871-1413
697:205399055
681:0007-1668
637:0032-5791
449:threonine
440:threonine
428:threonine
87:talk page
45:talk page
894:20952719
689:18568756
645:18753461
581:18969672
353:breeding
561:Talanta
445:protein
314:calpain
266:protein
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390:cotton
369:apples
316:, and
310:leptin
308:(GH),
845:(PDF)
830:(PDF)
805:(PDF)
798:(PDF)
693:S2CID
374:X-ray
357:sheep
327:. An
325:sheep
282:farms
890:PMID
882:ISSN
747:ISSN
685:PMID
677:ISSN
641:PMID
633:ISSN
577:PMID
287:pigs
268:and
262:GMDH
258:GMDH
254:GMDH
245:GMDH
192:and
872:doi
774:doi
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426:to
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337:ANN
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