A Review on the Application of Natural Computing in Environmental Informatics

Natural computing offers new opportunities to understand, model and analyze the complexity of the physical and human-created environment. This paper examines the application of natural computing in environmental informatics, by investigating related work in this research field. Various nature-inspired techniques are presented, which have been employed to solve different relevant problems. Advantages and disadvantages of these techniques are discussed, together with analysis of how natural computing is generally used in environmental research.Comment: Proc. of EnviroInfo 201

Crop Yield Forecasting by Adaptive Neuro Fuzzy Inference System

Meteorological uncertainties affect crop yield portentously during different stages of crop growing seasons, therefore several studies have been carried out to forecast crop yield using climatic parameters with empirical statistical regression equations relating regional yield with predictor variables. In this study an attempt has been made to develop Crop Yield Forecasting models to map relation between climatic data and crop yield. Present study was undertaken for forecasting rice yield by adaptive neuro fuzzy inference system (ANFIS) technique based on time series data of 27 years, yield and weather data (w.e.f. 1981-82 to 2007-08) obtained from G. B. Pant University of Agriculture and Technology, Pantnagar, District Udham Singh Nagar, Uttarakhand, India. Keywords: climate, crop yield, ANFIS, forecastin

Design and Implementation of Deep Learning Based Model Predictive Controller to Automatically Adjust Nutrient of Solution for Hydroponic Crop

Smart farming is the future of agriculture sector and brings a new era in agriculture; it enables farmers to increase the production and quality of crops with minimal use of resources. In current scenario land availability decreases enormously, hence soilless hydroponic cultivation is considered as the fastest growing sector of agriculture. However, in hydroponic system it is a very challenging task to manage nutrient for crop. To solve these issues this study was conducted which could control robustly EC and pH of hydroponic solution with the help of deep learning model long short-term memory (LSTM). A model predictive controller (MPC) using LSTM was designed and simulated to control EC and pH in hydroponic farm. The predicted outcome of LSTM was operating time of pH buffer solution pump (Ton_pH) and nutrient solution pump (Ton_EC).  The proposed MPC adjust these operating times to control EC and pH with an RMSE of 0.24 and 0.27s, respectively. Furthermore, proposed system improves the predicting accuracy of Ton_pH and Ton_EC of 77% and 61%, respectively, as compared to fuzzy logic controller. This study provides a smart and efficient way to predict and estimate the optimum value for robustly manage the nutrient as per crop requirements

WHEAT YIELD PREDICTION USING NEURAL NETWORK AND INTEGRATED SVM-NN WITH REGRESSION

The production of wheat plays an important role in Pakistan’s economy. Wheat yield forecasting is significant farming problem as it’s the most important crop of Pakistan. Prediction of the wheat yield has been determined by data mining techniques with different environmental factors. Data mining techniques have been developed for analysing and implementation on wheat yield dataset to predict the yield which is very helpful to produce wheat. In this study, Neural Network and a Novel Integrated approach of Neural Network, Support Vector Machine and Regression are used to analyze and estimates the wheat yield production. We have used these predictive techniques with area, yield, production, soil pH, temperature, air pressure, rainfall, water availability, humidity, pesticides and fertilizer parameter for wheat yield prediction

Application and Scope of Data Mining in Agriculture

Making agriculture sustainable and resilient to the ongoing change in climate and social structure is a major challenge for the scientists and researchers across the globe. Agricultural system demands transition and a multidisciplinary approach. Intelligent and precision agricultural approaches were given due importance for increasing production and productivity from the very same limited resources. The approach needs information from various sources and efficient use of them in relevant field. This need lead to growing interest in knowledge discovery from vast piles of data generated out of various research and survey works. The emergence of Data Mining techniques revolutionized the field of information generation and pattern recognition. Though Data Mining is an emerging science, it finds a wide application in agriculture and allied sectors, and has a wide future prospect

Insecticidal and repellant activities of Southeast Asia plants towards insect pests: a review

Crops are being damaged by several plant pests. Several strategies have been developed to restrict the damage of cultivated plants by using synthetic pesticides and repellants. However, the use to control these insects is highly discouraged because of their risks on humans. Therefore, several alternatives have been developed from plant extracts to protect crops from plant pests. Accordingly, this review focuses on outlining the insecticidal and repellant activities of Southeast Asia plants towards insect pests. Several extracts of plants from Southeast Asia were investigated to explore their insecticidal and repellant activities. Azadiracha indica (neem) and Piper species were highly considered for their insecticidal and repellant activities compared to other plants. This review also addressed the investigation on extracts of other plant species that were reported to exert insecticidal and repellant activities. Most of the conducted studies have been still in the primarily stage of investigation, lacking a focus on the insecticidal and repellant spectrum and the identification of the active constituents which are responsible for the insecticidal and repellant activity

Assessing accuracy of barley yield forecasting with integration of climate variables and support vector regression

Investigations of the relation between crop yield and climate variables are crucial for agricultural studies and decision making related to crop monitoring. Multiple linear regression (MLR) and support vector regression (SVR) are used to identify and model the impact of climate variables on barley yield. The climate variables of 36 years (1982–2017) are gathered from three provinces of Iran with different climate: Yazd (arid), Zanjan (semi-arid), Gilan (very humid). Air temperature by high correlation coefficient with barley yield was introduced as the dominant climate variable. According to evaluation criteria, SVR provided accurate estimation of crop yield in comparison with MLR. The diversity of climate impressed the estimated yield in which UI, decreasing from Gilan to Yazd provinces, was 47.77%. Support vector machine (SVM) with capturing the nonlinearity of time series, could improve barley yield estimation, with the minimum UI for Yazd province. Also, the minimum correlation coefficient between the observed and simulated yield was found in Gilan province. Based on GMER calculations, SVM forecasts were underestimated in three provinces. All findings show that SVM is able to have high efficiency to model the climate effect on crop yield