# Data mining in agriculture

Data mining in agriculture are the processes of employing data science techniques to analyze large volumes of agricultural data. Recent technological advancements, such as via drones and satellite imagery, have facilitated the collection of extensive data on soil health, weather patterns, crop growth, and pest activity among other things. This large-data is analyzed to improve agricultural efficiency, identify patterns and trends, and minimize potential losses.

## Applications
### Detection of fruit and vegetable defects

Data mining is used in agriculture to improve algorithms that detect defects in harvested fruits and vegetables. Advanced visual data collection methods can be used to classify fruits and vegetables according to various surface defects. Additionally, this data can be analyzed to investigate potential causes of defects. Currently, much of this knowledge is based on anecdotal evidence rather than qualitative and quantitative data collection methods, which therefore efforts are being made to integrate data mining techniques into horticulture research.
Before being sent to market, apples are checked and those with defects are removed. However, invisible defects can spoil the apples' flavor and appearance. One instance of an invisible defect is water core, an internal disorder that can affect the fruit's longevity. Apples with a slight water core are sweeter, but those with a moderate to severe water core cannot be stored as long as regular apples. Additionally, a few fruits with severe water cores could spoil an entire batch. Because of this, a computational system is under study, which takes X-ray photographs of the apples as they run on conveyor belts. The system analyses the pictures using data mining techniques to estimate the probability of the fruit containing water cores.

### Wine fermentation diagnosis

The metabolic transformations during fermentation impact the quality of the wine produced and the productivity of the industries related to wine production. Data science techniques, such as the k-means algorithm, and classification techniques based on biclustering, have been used to study these metabolic processes, successfully predicting fermentation outcomes after as little as three days. These methods classify wine according to the metabolite profile of the fermentation, and differ from traditional wine classifications systems.

### Predicting metabolizable energy of poultry by-product meal

A Group Method of Data Handling (GMDH)-type network, combined with a genetic algorithm, was used to predict the metabolizable energy of feather meal and poultry offal meal based on protein, fat, and ash content. Data samples from published literature were collected and used to train a GMDH-type network model. This approach can predict the metabolizable energy of poultry feed samples based on their chemical content. The GMDH-type network can also accurately estimate poultry performance from dietary nutrients such as metabolizable energy, protein and amino acids.

### Detection of diseases from animal sounds

The early detection of animal diseases can benefit farm productivity by allowing farmers to treat and isolate affected animals as soon as symptoms appear, reducing the spread of disease. For instance, sounds pigs make, such as coughing, can be analyzed for disease detection. A computational system is being developed to monitor and differentiate pig sounds through microphones installed in the farm.

### Growth of sheep from genes polymorphism using artificial intelligence

PCR-Single Strand Conformation Polymorphism (PCR-SSCP) was used to determine 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 data patterns to predict lamb growth in terms of ADG given specific genes polymorphism, birth weight, and birth type. The PCR-SSCP approach and ANN-based model analyses may be used in molecular marker-assisted and breeding programs to improve the efficacy of sheep production.

### Optimizing pesticide use

Recent studies by agricultural researchers in Pakistan showed that pro-pesticide state policies have contributed to high pesticide use in cotton crops, and reported a negative correlation between pesticide use and crop yield. The excessive use of pesticides is causing a financial, environmental, and social impact on farmers. Data mining within the cotton industry, using pest data along with meteorological recordings, shows how pesticide use can be optimized.

### Analyzing chicken performance data with neural network models

A platform of artificial neural network (ANN)-based models combined with sensitivity analysis and optimization algorithms was used to integrate published data on the responses of broiler chickens to threonine. Analyses of the ANN models for weight gain and feed efficiency suggested that dietary protein concentration was more important than threonine concentration. The results revealed that a diet containing 18.69% protein and 0.73% threonine may lead to optimal weight gain, while optimal feed efficiency may be achieved with a diet containing 18.71% protein and 0.75% threonine.