Agricultural Informatics. Группа авторов. Читать онлайн. Newlib. NEWLIB.NET

Автор: Группа авторов
Издательство: John Wiley & Sons Limited
Серия:
Жанр произведения: Программы
Год издания: 0
isbn: 9781119769217
Скачать книгу
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.

Schematic illustration of the layers and connection of a feed-forward back propagation ANN.

       1.1.1.2 Information Fuzzy Network

      Research experiments analyzed parameters for predicting crop yield. The study used aggressive neural network for prediction. Remote sensing was one among the used parameters for the work [7, 8]. The experiments implemented the flexible Neuro-fuzzy Inference system (ANFIS). The inputs to the ANFIS were moisture content available in soil, biomass information of the ground and repository organ. Yield was the output node for result. Limited data was used for designing the network to predict values for future. This is the challenge in prediction. Rearrangement of data was done by eliminating one year and using the remaining data. The estimation deviation was calculated and compared to the yield of the year that is left out. The procedure was applied recursively to all the years and averaged efficiency for prediction was obtained.

       1.1.1.3 Decision Trees

      Decision trees are the supervised learning techniques used in machine learning. The Decision Tree model is comprised of nodes, branches, leaf, terminal value, payoff distribution, rollback and certain equivalent and method. Decision trees have three variants of nodes and two variants of branches. Square is used to represent one of the decision points. The decision node is a point of choice for the tree. The decision nodes extend decision branches. Each node toward the end of the tree is called terminal node. There is an associated value associated with terminal node commonly referred to as payoff, outcome value. The terminal value is the measure of sequences of decisions or the resultant of the scenario in the tree. The construction of Decision tree algorithm is a two-step process that includes growth of tree and pruning. In the growth step the large decision tree is created, reduced and overfitting is removed. The second step does the tree pruning to reach a decision. The classification tree used for decision making is the obtained pruned tree [13].

      Prediction is influenced by various factors in agricultural explorations [14]. Variables associated with agronomics, application of nitrogen and weed control were used for machine learning and decision tree for yield forecasting and development of yield mapping. Both decision trees and ANN were implemented and results showed that greater accuracies were obtained from ANN results.

      The vast climatic diversity of India impacts the agricultural production in several parts of the country. Convenient decisions can be made by the farmers and policy inventors if the production can be forecasted in advance.

Schematic illustration of a decision tree structure for crop details prediction.

      Crop Advisor is one of the advancement in this area. It is a user friendly webpage that identified the impact of weather parameters on the yield of crops. Crop Advisor implements C4.5 algorithm. The most effective parameter of the climate on the yields of specified crops in selected region of Madhya Pradesh was ascertained using C4.5. There is boom in cloud based decision and support system for agriculture these days. There exists a Decision support and Automation system (DSAS) to assist farmers and growers. Users of the application have controls for different features in web portal. There are different stages in DSAS. DSAS provides farmer with real time data via interconnection of several devices. The farmer had right to monitor the real time data and control the machine through software. Few another systems like spray controller will spray defined amount of pesticide in fields, irrigation controller manage irrigation and fertilizer controller takes care of fertilizer. The data to DSAS is given by different sensors of climate and soil [17].

       1.1.1.4 Regression Analysis