Showing papers in "International Journal of Agricultural and Environmental Information Systems in 2019"
TL;DR: The authors found from model analysis that if the cost is low, the probability of firm to fulfill environmental responsibility is higher and the role of a government is also higher for the implementation of environmental responsibility and to ensure the punishment.
Abstract: The objective of this article is to design a game theory-based model to outline the role of the government, firm and civil society for environmental sustainability. The study used the dynamic game theory of complete information. Based upon the equilibrium analysis, the study highlights that when the punishment for non-compliance with environmental responsibility is smaller, the role of civil society would be higher for environmental sustainability. On the other hand, when the environmental responsibility cost is higher, then the role of a government is also higher for the implementation of environmental responsibility and to ensure the punishment. However, the authors found from model analysis that if the cost is low, the probability of firm to fulfill environmental responsibility is higher. In real life, the high cost of environmental responsibility is the main reason that the firm does not fulfill environmental responsibility. Under the high cost, the firm often has the phenomenon of bribery to the government and other means to avoid environmental responsibility. This article is a valuable policy guide for policy makers to cope with global environmental challenges.
19 citations
TL;DR: Quantitative evaluations conducted on a data set of 1,200 images collected by smart phones, demonstrates that the CNN achieves best precise performance in identifying diseased cherry leaves, with the testing accuracy of 99.6%.
Abstract: The cherry leaves infected by Podosphaera pannosa will suffer powdery mildew, which is a serious disease threatening the cherry production industry. In order to identify the diseased cherry leaves in early stage, the authors formulate the cherry leaf disease infected identification as a classification problem and propose a fully automatic identification method based on convolutional neural network (CNN). The GoogLeNet is used as backbone of the CNN. Then, transferred learning techniques are applied to fine-tune the CNN from pre-trained GoogLeNet on ImageNet dataset. This article compares the proposed method against three traditional machine learning methods i.e., support vector machine (SVM), k-nearest neighbor (KNN) and back propagation (BP) neural network. Quantitative evaluations conducted on a data set of 1,200 images collected by smart phones, demonstrates that the CNN achieves best precise performance in identifying diseased cherry leaves, with the testing accuracy of 99.6%. Thus, a CNN can be used effectively in identifying the diseased cherry leaves.
14 citations
TL;DR: In this paper, a systematic review was conducted, based on the systematic search and evaluation of related eligible articles, to investigate further the economic, agronomic, and environmental benefits from the adoption of precision agriculture technologies.
Abstract: Precision agriculture (PA) as an integrated information- and production-based farming system is designed to delivery high-end technology solutions to increase farm production efficiency and profitability while minimizing environmental impacts on the ecosystems and the environment. PA technologies are technology innovations that incorporate recent advances in modern agriculture providing evidence for lower production costs, increased farming efficiency and reduced impacts. However, the adoption of the precision agriculture technologies has encountered difficulties such as additional application or management costs and investment on new equipment and trained employees. Some of these PA technologies were proven efficient, providing tangible benefits with lower costs and as a result they quickly gained scientific interest. To investigate further the economic, agronomic, and environmental benefits from the adoption of PA technologies a systematic review was conducted, based on the systematic search and evaluation of related eligible articles.
14 citations
TL;DR: Five Feature Selection algorithms namely Sequential Forward FS, Sequential Backward Elimination FS, Correlation based FS, Random Forest Variable Importance and the Variance Inflation Factor algorithm for feature selection are discussed.
Abstract: In agriculture, crop yield prediction is critical. Crop yield depends on various features including geographic, climate and biological. This research article discusses five Feature Selection (FS) algorithms namely Sequential Forward FS, Sequential Backward Elimination FS, Correlation based FS, Random Forest Variable Importance and the Variance Inflation Factor algorithm for feature selection. Data used for the analysis was drawn from secondary sources of the Tamil Nadu state Agriculture Department for a period of 30 years. 75% of data was used for training and 25% data was used for testing. The performance of the feature selection algorithms are evaluated by Multiple Linear Regression. RMSE, MAE, R and RRMSE metrics are calculated for the feature selection algorithms. The adjusted R2 was used to find the optimum feature subset. Also, the time complexity of the algorithms was considered for the computation. The selected features are applied to Multilinear regression, Artificial Neural Network and M5Prime. MLR gives 85% of accuracy by using the features which are selected by SFFS algorithm.
13 citations
TL;DR: The noble intention behind this literature review and analogy is to figure out the gap between theoretical research and actual needs of farmers to design an effective and efficient decision support system for irrigation and fertilization can be designed for Indian farmers.
Abstract: Precision Agriculture (PA) is now becoming the base for rapid development of a nation. So many technologies are used in precision agriculture such as Global Positioning System (GPS), Artificial Intelligence (AI), Sensor Network and Geographical Information System (GIS). This manuscript per the authors will review all the factors that influence the precision agriculture. This article describes the major endeavors in the past of precision agriculture. The noble intention behind this literature review and analogy is to figure out the gap between theoretical research and actual needs of farmers. In order to find out the actual requirements manuscripts per the authors have conducted a questionnaire in Rajasthan State of India. This gap analysis would be helpful for researchers to design an effective and efficient decision support system for irrigation and fertilization can be designed for Indian farmers.
6 citations
TL;DR: This article proposes and validates the UML Profile and implementation for precision agriculture and smart farming through computer-aided and software engineering.
Abstract: Modelling WSN data behaviour is relevant since it would allow to evaluate the capacity of an application for supplying the user needs, moreover, it could enable a transparent integration with different data-centric information systems. Therefore, this article proposes a data-centric UML profile for the design of wireless sensor nodes from the user point-of-view capable of representing the gathered and delivered data of the node. This profile considers different characteristics and configurations of frequency, aggregation, persistence and quality at the level of the wireless sensor nodes. Furthermore, this article validates the UML profile through a computer-aided software engineering (CASE) tool implementation and one case study, centred on the data collected by a real WSN implementation for precision agriculture and smart farming.
5 citations
TL;DR: This article represents an effective use of machine vision technology for estimating plant morphological features to ascertain its growth and health conditions through a novel online plant vision system proposed and developed on the platform of virtual instrumentation.
Abstract: Motivated by the fact that human visionary intelligence plays a vital role in guiding many of the agriculture practices, this article represents an effective use of machine vision technology for estimating plant morphological features to ascertain its growth and health conditions. An alternative to traditional, manual and time-consuming testing methods of plant growth parameters, a novel online plant vision system is proposed and developed on the platform of virtual instrumentation. Deployed in real time, the system acquires plant images using digital camera and communicates the raw image to host PC on Wi-Fi network. The dedicated application software with plant user interface, effective image processing and analysis algorithms, loads the plant images, extracts and estimates certain morphological features of the plant such as plant height, leaf area, detection of flower onset and fall foliage. The system was tested and validated under real-time conditions using different plants and leaves. Further, the performance of the system was statistically analysed to show promising results.
5 citations
TL;DR: Findings showed a strong relation between IT implementation and impact of IT on TQM, and company size seemed to affect TQm implementation, and the majority of IT implementation constructs, while company performance was not significant in terms of net profit margin and value added per employee.
Abstract: The purpose of this study was to examine the implementation of Total Quality Management (TQM) as a business strategy in the Greek food and drink industry, along with the examination of the Information Technology (IT) adoption in the field. A research project was carried out in the sector companies based in Greece, using the questionnaire method. Findings showed a strong relation between IT implementation and impact of IT on TQM. Company size also seemed to affect TQM implementation, and the majority of IT implementation constructs, while company performance was not significant in terms of net profit margin and value added per employee.
4 citations
TL;DR: The present study is the first in the area and the results it will be utilized as a decision support tool for sustainability assessment of ecosystems with the help of the information systems.
Abstract: The Mediterranean basin is a global hotspot of biodiversity. Woody plants are key components of ecosystems. This article explores the environmental impacts on woody plant species richness and diversity in maquis and abandoned olive groves in an important ecological area of central Greece. The results showed that woody plant species richness and diversity had increasing values in maquis compared to abandoned olive groves. According to Principal Component Analysis, woody plant species richness and diversity (Shannon diversity index) were positively correlated with soil organic matter, plant litter, N, P, K, slope and precipitation in maquis. Also, positive correlations among woody plant species richness and diversity, and soil organic matter, and slope were detected in abandoned olive groves. Conclusively, the present study is the first in the area and the results it will be utilized as a decision support tool for sustainability assessment of ecosystems with the help of the information systems.
4 citations
TL;DR: To analyze the environmental pollution effects elicited by industrial agglomeration, a spatial econometric model is constructed based on the Green Solow model and it is demonstrated that there is significant correlation between industrial aggLomeration and industrial pollution discharge.
Abstract: To analyze the environmental pollution effects elicited by industrial agglomeration, a spatial econometric model is constructed based on the Green Solow model. Using data derived from 285 Chinese cities between 2003and 2014, the global Moran'I and local bivariate LISA agglomeration map demonstrates that there is significant correlation between industrial agglomeration and industrial pollution discharge. Then, the spatial Durbin model (SDM) is built and the empirical results are as follows. First, inter-city industrial pollution discharge has a demonstration effect. Cites in the same region should take measures to cooperate to lower industrial pollution discharge. Second, the relationship between the local cities' industrial agglomeration and the local cities' industrial pollution discharge fits the inverted “U” curve. While the neighboring cities' industrial agglomeration will decrease the local cities' industrial pollution discharge. So, measures should be taken to increase the industrial agglomeration degree in the long run.
4 citations
TL;DR: A new method for multivariate time series clustering, which can simultaneously segment and cluster the time series data, and provides a new way of thinking about market price prediction for agricultural products is provided.
Abstract: The high volatility of world soybean prices has caused uncertainty and vulnerability particularly in the developing countries. The clustering of time series is a serviceable tool for discovering soybean price patterns in temporal data. However, traditional clustering method cannot represent the continuity of price data very well, nor keep a watchful eye on the correlation between factors. In this work, the authors use the Toeplitz Inverse Covariance-Based Clustering of Multivariate Time Series Data (TICC) to soybean price pattern discovery. This is a new method for multivariate time series clustering, which can simultaneously segment and cluster the time series data. Each pattern in the TICC method is defined by a Markov random field (MRF), characterizing the interdependencies between different factors of that pattern. Based on this representation, the characteristics of each pattern and the importance of each factor can be portrayed. The work provides a new way of thinking about market price prediction for agricultural products.
TL;DR: The results showed that the Bayesian network has accurately represented the knowledge of the expert, and also could help the farmers to avoid the unnecessary applications.
Abstract: Asian rust is the main soybean disease in Brazil, causing up to 80% of yield reduction. The use of fungicides is the main form of control; however, due to farmer's concern with outbreaks many unnecessary applications are performed. The present study aims to verify the usefulness of a probability model to estimate the timing and the number of fungicides sprays required to control Asian soybean rust, using Bayesian networks and knowledge engineering. The model was developed through interviews with rust researchers and a literature review. The Bayesian network was constructed with the GeNIe 2.0 software. The validation process was performed by 42 farmers and 10 rust researchers, using 28 test cases. Among the 28 tested cases, generated by the system, the agreement with the model was 47.5% for the farmers and 89.3% for the rust researchers. In general, the farmers overestimate the number. The results showed that the Bayesian network has accurately represented the knowledge of the expert, and also could help the farmers to avoid the unnecessary applications.
TL;DR: Comparison of scatter plots indicates that the values of runoff predicted by the ANN model are more precise than those found by RBF or Fuzzy Logic model.
Abstract: The prediction of the runoff generated within a watershed is an important input in the design and management of water resources projects. Due to the tremendous spatial and temporal variability in precipitation, rainfall-runoff relationship becomes one of the most complex hydrologic phenomena. Under such circumstances, using soft computing approaches have proven to be an efficient tool in modeling of runoff. These models are capable of predicting river runoff values that can be used for hydrologic and hydraulic engineering design and water management purposes. It has been observed that the artificial neural networks (ANN) model performed well compared to other soft computing techniques such as fuzzy logic and radial basis function investigated in this study. In addition, comparison of scatter plots indicates that the values of runoff predicted by the ANN model are more precise than those found by RBF or Fuzzy Logic model.
TL;DR: This article illustrates how global footprint analysis can be incorporated into scenarios to enable local forest stakeholders in the EU to consider the impacts of their local decisions at national and global levels.
Abstract: This research serves to integrate the concept of an “ecological footprint” into future-oriented forest management scenarios. Scenarios are commonly used to explore stakeholder perceptions of possible forest futures, and are typically focused on the local impacts of different management choices. This article illustrates how global footprint analysis can be incorporated into scenarios to enable local forest stakeholders in the EU to consider the impacts of their local decisions at national and global levels. This illustration could be helpful to the construction of a forest decision support system that includes wood trade information and social processes (simulation of management decisions under changing political/economic conditions). It finds that different future forest management scenarios involving a potential increase or decrease of the harvested timber, or potential increase or decrease of subsidies for forest protection, combined with various possible changes in local consumption patterns, might have impact on both “internal” (local) and “external” (non-local) forest footprints.
TL;DR: A Two-step procedure designed on the methods of the least squares (LS) and instrumental variable (IV) techniques for simultaneous estimation of the three unknown parameters L∞, K and t0, which represent the individual growth of fish in the von Bertalanffy growth equation is presented.
Abstract: Advanced in the present article is a Two-step procedure designed on the methods of the least squares (LS) and instrumental variable (IV) techniques for simultaneous estimation of the three unknown parameters L∞, K and t0, which represent the individual growth of fish in the von Bertalanffy growth equation. For the purposes of the present analysis, specific MATLAB-based software has been developed through simulated data sets to test the operational workability of the proposed procedure and pinpoint areas of improvement. The resulting parameter estimates have been analyzed on the basis of consecutive comparison (the initial conditions being the same) between the results delivered by the two-step procedure for simultaneous estimation of L∞, K and t0 and the results obtained via the most commonly employed methods for estimating growth parameters; first, use has been made of the Gulland-and-Holt method for estimating the asymptotic length L∞and the curvature parameter K, followed by the von Bertalanffy method for estimation of t0.
SIDI1
TL;DR: A system of pollen identification based on the microscopic images is presented, in which deep convolutional neural network is used to extract features from the detected pollen grains and represent them in numerical vectors, therefore, these vectors can be used to classify them based on fully connected neural network, SVM or similarity calculation.
Abstract: Melissopalynology is a field that studies pollen grain origins to identify their species. It consists of studying either the chemical composition of each grain, or their shapes using microscopic images. This paper presents a system of pollen identification based on the microscopic images, it is divided into two parts, first part is the pollen detection using a thresholding method with simulated annealing algorithm. The second step is the pollen classification, in which we used deep convolutional neural network to extract features from the detected pollen grains and represent them in numerical vectors, therefore, we can use these vectors to classify them based on fully connected neural network, SVM or similarity calculation. The obtained results showed a high efficiency of the neural network in which it could recognize 98.07% of the pollen species compared not just to SVM and similarity methods, but also to works from literature.
TL;DR: Tapers are classified into three categories depending on whether they use measured diameters at relative or absolute heights in the tree trunk, and it emerged that in the first category all tapers approached the normal distribution.
Abstract: The aim of the present research is the study of the statistical behavior of ninety-three tapers. Tapers are classified into three categories depending on whether they use measured diameters at relative or absolute heights in the tree trunk. In each taper, measures of central tendency, measures of dispersion and a measure of skewness were examined. Each taper was examined if it fits normal distribution or not. It emerged that in the first category all tapers approached the normal distribution. In the second category, eight of the ten tapers are satisfactorily reaching the normal distribution, while in the third category thirty-seven out of seventy-eight are satisfactorily reaching the normal distribution. Data used in the research were collected in the Municipal Forest of Naoussa from 300 trees of Fagus sylvatica using random sampling.