Data mining in agriculture is a recent research topic consisting of the application of data mining and data science techniques to agriculture. Recent technologies are able to provide extensive data on agricultural-related activities, which can then be analyzed in order to find relevant information. A related, but not equivalent term is precision agriculture. Applications Relationship between sprays and fruit defects 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 least record the date of spray and the product name. It is known that spraying can have affect 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. Prediction of problematic wine fermentations 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, and classification techniques based on the concept of biclustering 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 Classification of wine for more details. Predicting metabolizable energy of poultry feed using group method of data handling-type neural network A group method of data handling-type neural network (GMDH-type network) 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. Published data samples were collected from literature and used to train a GMDH-type network model. The novel modeling of 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. It is also reported that the GMDH-type network may be used to accurately estimate the poultry performance from their dietary nutrients such as dietary metabolizable energy, protein and amino acids. Detection of diseases from sounds issued by animals The detection of diseases in