Many studies have applied machine learning to crop yield prediction with a focus on specific case studies. The data and methods they used may not be transferable other crops locations. On the hand, operational large-scale systems, such as European Commission's MARS Crop Yield Forecasting System (MCYFS), do use learning. Machine is promising method especially when large amounts of are being coll...

Artificial neural networks (ANNs) are flexible computing frameworks and universal approximators that can be applied to a wide range of time series forecasting problems with a high degree of accuracy. However, despite all advantages cited for artificial neural networks, their performance for some real time series is not satisfactory. Improving forecasting especially time series forecasting accur...

Neural networks have gained a great deal of importance in the area of soft computing and are widely used in making predictions. The work presented in this paper is about the development of Artificial Neural Network (ANN) based models for the prediction of sugarcane yield in India. The ANN models have been experimented using different partitions of training patterns and different combinations of...

In order to make decisions regarding when to harvest cultured shrimp, short-term forecasts of future growth must be compared with expected future costs and changes in the value of the shrimp harvested. As with most agricultural products, researchers and business professionals collect and maintain data on the various factors that influence growth. Though many general fixed relationships between,...

Accurate and timely information on rice crop growth and yield helps governments and other stakeholders adapting their economic policies, enables relief organizations to better anticipate and coordinate relief efforts in the wake of a natural catastrophe, and provides technical backbone of an insurance solution where risks of yield losses from the rice smallholders are transferred to the insuran...

Using a Dynamic Semiparametric Factor Model (DSFM) we investigate the term structure of interest rates. The proposed methodology is applied to monthly interest rates for four southern European countries: Greece, Italy, Portugal and Spain from the introduction of the Euro to the recent European sovereign-debt crisis. Analyzing this extraordinary period, we compare our approach with the standard ...

This study aimed to develop a remote sensing-based method for forecasting rice yield by considering vegetation greenness conditions during initial and peak greenness stages of the crop; and implemented for “boro” rice in Bangladeshi context. In this research, we used Moderate Resolution Imaging Spectroradiometer (MODIS)-derived two 16-day composite of normalized difference vegetation index (NDV...

In our present study, GARCH family models are used for modeling and forecasting the rice yield of four provinces of Pakistan during the period of 1947-48 to 2008-09. Also Auto regressive, moving average and Autoregressive moving average models are described. Thus, the selected GARCH models for all provinces are also presented for forecasting purpose on the basis of two criteria AIC (Akaike info...

Data mining applications in agriculture is a relatively new approach for forecasting / predicting of agricultural crop/animal management. In the present study an attempt has been made to study the influence of climatic parameters on soybean productivity using decision tree induction technique. The findings of Decision tree were framed into different rules for better understanding by the end use...