Читать книгу Agricultural Informatics - Группа авторов - Страница 15

The artificial neural network is a collaboration of artificial neurons based on human brains biological architecture. They replicate the behavior of the human brain for processing the data. Artificial neural network belongs to the category of supervised learning where a part of data is used for model training and the remaining is tested on the trained model. Once the neural net is trained, the similar patterns can be generated for obtaining efficient and solutions to problems and predictive analysis. The trained neural network can produce solutions even if the input data is incomplete or incorrect. Adding more layers and data increases the accuracy of the ANN. The ANN is capable of adopting their complexity without the need to know the underlying principles. The relationship among input and output for any process can be derived using ANN. Authors used [1] ANN to predict potato yield in Iran. Figure 1.1 shows the basic architecture of artificial neural network.

Input energy was taken as the input parameter. The work intended to design output energy and greenhouse gas emission for production forecast. The data collection was done from 260 farmers by taking inputs from them. Multiple ANNs were designed and utilized to forecast. The forecast efficiency was assessed from quality aspect. The prediction results. Electricity, chemical fertilizer and seed were identified as most important factors affecting production rate. Literature [3] quotes ANN systems are the results of inspiration of the human brain. Each node in neural net represents neurons and each link is the representation of interaction among two associated neurons. Execution of simple tasks is the responsibility of single neuron while the network performs more complex tasks that are aggregations of all the neuron groups in network. There exists an interconnected set of input and output that has weighted connections. The testing phase of network enables them to earn to predict the input sampled by performing weight tuning. Flood forecast uses neural networks to model rainfall and runoff relationships for predicting flood situations. Neural networks have better performance over conventional computing methods. ANN finds suitability for the time consuming problem solutions such as pest prediction. Research [4] found that validation of the symptoms of tomato crop can be done using a web-based expert system that utilized applied artificial learning and machine learning algorithms particularly for the identification task. In crop expert system applicant is advised with crop related information. The farmer gets insight for crop varieties, pest affecting the crop production and diseases symptoms on the crop, cultural practice for good yield, mosaic of tomato fruits and plant. Client is also facilitated for communicating with the system online. The query put forward by the client is responded by the expert system that advises and informs client regarding all the hazards and control measures. The knowledge expert system provides data about disease recognition, pest and varieties of tomato crop.

Figure 1.1 Layers and connection of a feed-forward back propagation ANN [2].

Machine learning algorithms are reliable for decisions and can be integrated with statistics for implementation in applied machine learning [5]. Machine Learning is an emerging subject to expedite the release of new genotypes. There exist several uses of Machine Learning in maize breeding. Few of them can be enlisted as loci mapping based on quantitative traits, heterotic group assignment and selections based on genomes. Authors [6] implemented ANN to predict crop production. The parameters used for the task were related to soil properties such as pH, nitrogen, phosphate, potassium, organic carbon, calcium, magnesium, and sulfur, manganese, copper, iron, depth and climate parameters such as temperature, rainfall, humidity. Cotton, Sugarcane, Jawar, Bajara, Soybean, Corn, Wheat, Rice and Groundnut were the crops taken for experiment.