Data mining in agriculture
Data mining in agriculture is a research topic regarding data mining and data science techniques within the agricultural sector. Recent advancements in technology have made it possible to collect large amounts of data related to agricultural activities. These are analyzed to inform better decision-making; for example, to optimize crop yields, predict the outcome of treatments, or as a diagnostic tool.[1]
Applications
Fruit defect detection
Data mining enhances fruit defect detection algorithms, crucial for post-harvest decisions like identifying potential markets and reporting for insurance. It helps classify fruit based on surface defects, such as those caused by chemical spraying. This data is vital for compliance with legislation regarding chemical applications and their documentation.[2]
Wine fermentation diagnosis
Data science techniques, including the k-means algorithm and biclustering, study the metabolic processes of wine fermentation. These methods predict fermentation outcomes and classify wine based on metabolite profiles, aiding in early diagnosis and intervention of unwanted fermentation outcomes.[3]
Predicting metabolizable energy of poultry feed
A Group Method of Data Handling (GMDH)-type network combined with genetic algorithms predicts the metabolizable energy of poultry feed based on protein, fat, and ash content. This method accurately estimates poultry performance from dietary nutrients.[4]
Detection of diseases from animal sounds
Analyzing sounds emitted by animals, such as pigs coughing, can detect diseases early, reducing contamination and allowing timely treatment and isolation of affected animals. Computational systems are being developed to monitor these sounds and differentiate between various detected sounds.[5]
Growth of sheep from genes polymorphism using artificial intelligence
An artificial neural network (ANN) model predicts the average daily gain (ADG) in lambs using gene polymorphism data, birth weight, and birth type. This platform can be used in molecular marker-assisted selection and breeding programs to improve sheep production.[6]
Sorting apples by watercore
A computational system under study uses X-ray imaging to detect invisible defects like watercore in apples, ensuring only high-quality fruits reach the market and preventing spoilage of entire batches.[7]
Optimizing pesticide use by data mining
Data mining of cotton pest scouting and meteorological data can optimize pesticide use, reducing adverse financial, environmental, and social impacts. Clustering of data reveals patterns in farming practices and pesticide use dynamics.[8]
Analyzing chicken performance data by neural network models
Neural network models combined with sensitivity analysis and optimization algorithms integrate data on broiler chickens' responses to threonine. The analysis suggests that dietary protein concentration is more important than threonine concentration for optimal weight gain and feed efficiency.[9]
References
- ↑
Mucherino, A.; Papajorgji, P.J.; Pardalos, P. (2009). Data Mining in Agriculture, Springer. Search this book on
- ↑ "Apple russeting". www.extension.umn.edu. Archived from the original on 2016-10-02. Retrieved 2016-10-04. Unknown parameter
|url-status=ignored (help) - ↑ Urtubia, Alejandra; Pérez-Correa, J. Ricardo; Soto, Alvaro; Pszczólkowski, Philippo (2007-12-01). "Using data mining techniques to predict industrial wine problem fermentations". Food Control. 18 (12): 1512–1517. doi:10.1016/j.foodcont.2006.09.010. ISSN 0956-7135.
- ↑ Ahmadi, H.; Golian, A.; Mottaghitalab, M.; Nariman-Zadeh, N. (2008-09-01). "Prediction Model for True Metabolizable Energy of Feather Meal and Poultry Offal Meal Using Group Method of Data Handling-Type Neural Network". Poultry Science. 87 (9): 1909–1912. doi:10.3382/ps.2007-00507. ISSN 0032-5791. PMID 18753461.
- ↑ 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". Journal of Agricultural Engineering Research. 79 (4): 449–457. doi:10.1006/jaer.2001.0719.
- ↑ Mojtaba, Tahmoorespur; Hamed, Ahmadi (2012-01-01). "neural network model to describe weight gain of sheep from genes polymorphism, birth weight and birth type". Livestock Science. ISSN 1871-1413.
- ↑ 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". Transactions of the American Society of Agricultural Engineers. 40 (5): 1407–1415. doi:10.13031/2013.21367.
- ↑ Abdullah, Ahsan; Brobst, Stephen; Pervaiz, Ijaz; Umar, Muhammad; Nisar, Azhar (2004). Learning Dynamics of Pesticide Abuse through Data Mining (PDF). Australasian Workshop on Data Mining and Web Intelligence, Dunedin, New Zealand. Archived from the original (PDF) on 2011-08-14. Retrieved 2010-07-20. Unknown parameter
|url-status=ignored (help) - ↑ Ahmadi, H.; Golian, A. (2010-11-01). "The integration of broiler chicken threonine responses data into neural network models". Poultry Science. 89 (11): 2535–2541. doi:10.3382/ps.2010-00884. ISSN 0032-5791. PMID 20952719.
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