Data mining in agriculture

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Data mining in agriculture is a very recent research topic. It consists in the application of data mining techniques to agriculture. Recent technologies are nowadays able to provide a lot of information on agricultural-related activities, which can then be analyzed in order to find important information.[1] A related, but not equivalent term is precision agriculture.

Applications[edit]

Prediction of problematic wine fermentations[edit]

Wine is widely produced all around the world. The fermentation process of the wine is very important, because it can impact the productivity of wine-related industries and also the quality of wine. If we were able to predict how the fermentation is going to be at the early stages of the process, we could interfere with the process in order to guarantee a regular and smooth fermentation. Fermentations are nowadays studied by using different techniques, such as, for example, the k-means algorithm,[2] and a technique for classification based on the concept of biclustering.[3] Note that these works are different from the ones where a classification of different kinds of wine is performed. See the wiki page Classification of wine for more details.

Detection of diseases from sounds issued by animals[edit]

The detection of animal's diseases in farms can impact positively the productivity of the farm, because sick animals can cause contaminations. Moreover, the early detection of the diseases can allow the farmer to cure the animal as soon as the disease appears. Sounds issued by pigs can be analyzed for the detection of diseases. In particular, their coughs can be studied, because they indicate their sickness. 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.[4]

Sorting apples by watercores[edit]

Before going to 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 invisible defect is the watercore. This is an internal apple disorder that can affect the longevity of the fruit. Apples with slight or mild watercores are sweeter, but apples with moderate to sever 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 X-ray photographs of the fruit while they run on conveyor belts, and which is also able to analyse (by data mining techniques) the taken pictures and estimate the probability that the fruit contains watercores.[5]

Optimizing pesticide use by data mining[edit]

Recent studies by agriculture researchers in Pakistan (one of the top four cotton producers of the world) showed that attempts of cotton 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, 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.[6]

Explaining pesticide abuse by data mining[edit]

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 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 pesticides used for the right reasons and temporal relationship between pesticide usage and day of the week.[7]

Literature[edit]

Since this research topic is quite recent, there is only one reference book. Data Mining in Agriculture is published by Springer and it is co-authored by Antonio Mucherino, Petraq Papajorgji and Panos Pardalos. A quick survey of the book can be found at this address. There are a few precision agriculture journals, such as Springer's Precision Agriculture or Elsevier's Computers and Electronics in Agriculture, but those are not exclusively devoted to data mining in agriculture.

Conferences[edit]

There are many conferences organized every year on data mining techniques and applications, but rather few of them consider problems arising in the agricultural field. To date,[when?] there is only one example of a conference completely devoted to applications in agriculture of data mining. It is organized by Georg Ruß. This is the conference web page.

References[edit]

  1. ^ Mucherino, A.; Papajorgji, P.J.; Pardalos, P. (2009). Data Mining in Agriculture, Springer. 
  2. ^ Urtubia, A.; Perez-Correa, J.R.; Meurens, M.; Agosin, E. (2004). "Monitoring Large Scale Wine Fermentations with Infrared Spectroscopy". Talanta 64(3): 778–784. 
  3. ^ 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. 
  4. ^ 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. 
  5. ^ 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. 
  6. ^ Abdullah, Ahsan; Brobst, Stephen; Pervaiz, Ijaz; Umar, Muhammad; Nisar, Azhar (2004). "Learning Dynamics of Pesticide Abuse through Data Mining". Australasian Workshop on Data Mining and Web Intelligence, Dunedin, New Zealand. 
  7. ^ Abdullah, Ahsan; Hussain, Amir (2006). "Data Mining a New Pilot Agriculture Extension Data Warehouse". Journal of Research and Practice in Information Technology, Vol. 38, No. 3, August 2006: 229–249.