Data mining in agriculture
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Data mining in agriculture, is the use of data science techniques to analyze large volumes of agricultural data. Recent advancements in technology, such as sensors, drones, and satellite imagery, have enabled the collection of large amounts of data on soil health, weather patterns, crop growth, and pest activity. Data is analyzed to help improve agricultural efficiency, identify patterns and trends, potentially spot issues early on and minimize potential losses. It can also be used to better understand environmental effects.[1]
Applications
[edit]Fruit defect detection
[edit]Data mining can be used in agriculture to enhance fruit defect detection algorithms, which are important for the post-harvest related decisions. Such as, but not limited to the identification of potential markets and enhanced reporting for export insurance companies. The development of data collection methods in development is used to generate actionable data to classify fruit according to surface defects.[2] To exemplify, data mining has been applied to defect detection in fruits due to chemical spraying, since spraying can cause various defects in different types of fruit. This data is particularly useful to extensively comply with legislation that requires documentation such as dates of applications and chemical information. Legislation requirements are justified since fungicidal sprays, for example, are often used to prevent rot from developing on fruits, such as russeting on apples.[3] Currently, much of this knowledge is based on anecdotal evidence rather than qualitative and quantitative data collection methods, which is why efforts are being made to apply data mining practices to horticulture research.[4]
Wine fermentation diagnosis
[edit]The metabolic transformations of 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 k-means algorithm,[5] and classification techniques based on the concept of blustering[6] have been used to study these metabolic processes, successfully predicting fermentation outcomes even after three days of fermentation. These methods classify wine according to the metabolite profile of the fermentations and is different from traditional wine classifications systems. See the wiki page Classification of Wine for more details. Based on experimental data, scientists propose this is as valuable tool to diagnose unwanted fermentation outcomes and hence plan for intervention at the early stages of the fermentation.[7]
Predicting metabolizable energy of poultry feed using group method of data handling-type neural network
[edit]A Group Method of Data Handling (GMDH)-type network combined with an evolutionary method of genetic algorithm, was used to predict the metabolizable energy of feather meal and poultry offal meal based on their protein, fat, and ash content. Data samples from published literature were collected and used to train a GMDH-type network model. The novel approach of combining GMDH-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.[8] It is also reported that the GMDH-type network can accurately estimate the poultry performance from their dietary nutrients such as metabolizable energy, protein, and amino acids.[9]
Detection of diseases from animal sounds
[edit]The detection of diseases on farms 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 affected animal as soon as the symptoms appear. Sounds emitted by pigs, such as coughing, can be analyzed for disease detection. A computational system is currently being development to monitor pig sounds through microphones installed in the farm, and which is also able to differentiate between the various sounds that can be detected.[10]
Growth of sheep from genes polymorphism using artificial intelligence
[edit]Polymerase Chain Reaction-Single Strand Conformation Polymorphism (PCR-SSCP) method was used to determine the growth hormone (GH), leptin, calpain, and calpastatin polymorphism in Iranian Balochi male sheep. An artificial neural network (ANN) model was developed to predict average daily gain (ADG) in lambs using input parameters of GH, leptin, calpain, and calpastatin polymorphism, birth weight, and birth type. The results revealed that the ANN-model is an appropriate tool for identifying the patterns of data to predict lamb growth in terms of ADG given specific genes polymorphism, birth weight, and birth type. The platform of PCR-SSCP approach and ANN-based model analyses may be used in molecular marker-assisted selection and breeding programs to design a scheme in improving the efficacy of sheep production.[11]
Sorting apples by water cores
[edit]Before being sent to the market, apples 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 an invisible defect is a water core, an internal apple disorder that can affect the longevity of the fruit called a water core. Apples with slight or mild water core are sweeter, but apples with moderate to severe degree of water core cannot be stored for as long as apples Moreover, a few fruits with severe water core could spoil an entire batch of apples. Because of this, a computational system is under study, which takes X-ray photographs of the fruit while the apples run on conveyor belts. The system is also able to analyze (by data mining techniques) the pictures taken and estimate the probability of the fruit containing water cores.[12]
Optimizing pesticide use
[edit]Recent studies by agricultural researchers in Pakistan showed that attempts of cotton crop to 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. As a result, the excessive use (or abuse) of pesticides is causing the farmers adverse financial, environmental and social impacts. By data mining the cotton, pest scouting data along with the meteorological recordings show how pesticide use can be optimized (reduced). Clustering of data revealed interesting patterns in farming practices along with pesticide use dynamics, helping to identify the reasons for this pesticide abuse.[13]
Explaining pesticide abuse
[edit]To monitor cotton growth, different government departments and agencies in Pakistan have been recording pest scouting, agricultural 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 data recorded has never been digitized, integrated or standardized to give a complete picture, and therefore, 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 pesticides used for the right reasons and temporal relationship between pesticide usage and day of the week.[14]
Analyzing chicken performance data by neural network models
[edit]A platform of artificial neural network-based models combined with sensitivity analysis and optimization algorithms was successfully used to integrate published data on the responses of broiler chickens to threonine. Analyses of the artificial neural network models for weight gain and feed efficiency from a compiled dataset suggested that the dietary protein concentration was more important than the threonine concentration. The results revealed that a diet containing 18.69% protein, and 0.73% threonine may lead to producing optimal weight gain, while the optimal feed efficiency may be achieved with a diet containing 18.71% protein and 0.75% threonine.[15]
Literature
[edit]There are a precision agriculture journals, such as Springer's Precision Agriculture or Elsevier's Computers and Electronics in Agriculture. However, these journals are not exclusively devoted to data mining in agriculture.
References
[edit]- ^ Ait Issad, Hassina (October 2019). "A comprehensive review of Data Mining techniques in smart agriculture". Science Direct. 12 (4): 511–525. doi:10.1016/j.eaef.2019.11.003.
- ^ Firouz, Mahmoud Soltani (2022). "Defect Detection in Fruit and Vegetables by Using Machine Vision Systems and Image Processing". Springer Nature Link. 14 (3): 353–379. doi:10.1007/s12393-022-09307-1.
- ^ "Apple russeting". www.extension.umn.edu. Archived from the original on 2016-10-02. Retrieved 2016-10-04.
- ^ 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". Computers and Electronics in Agriculture. 108: 250–257. doi:10.1016/j.compag.2014.08.011.
- ^ Urtubia, A.; Perez-Correa, J.R.; Meurens, M.; Agosin, E. (2004). "Monitoring Large Scale Wine Fermentations with Infrared Spectroscopy". Talanta. 64 (3): 778–784. doi:10.1016/j.talanta.2004.04.005. PMID 18969672.
- ^ Mucherino, A.; Urtubia, A. (2010). "Consistent Biclustering and Applications to Agriculture". IbaI Conference Proceedings, Proceedings of the Industrial Conference on Data Mining (ICDM10), Workshop Data Mining in Agriculture (DMA10), Springer: 105–113.
- ^ 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.
- ^ 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". British Poultry Science. 49 (3): 315–320. doi:10.1080/00071660802136908. ISSN 0007-1668. PMID 18568756. S2CID 205399055.
- ^ 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.
- ^ Abdullah, Ahsan; Hussain, Amir (2006). "Data Mining a New Pilot Agriculture Extension Data Warehouse" (PDF). Journal of Research and Practice in Information Technology. 38 (3): 229–249. Archived from the original (PDF) on 2010-09-23.
- ^ 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.