Combined application of Artificial Neural Networks and life cycle assessment in lentil farming in Iran

Elhami, Behzad; Khanali, Majid; Akram, Asadollah

doi:10.1016/j.inpa.2016.10.004

Cited by 31 publications

(17 citation statements)

References 39 publications

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“…In addition, we only used studies that expressed results with at least a mass-based functional unit and global warming potential in a mass-based CO 2 equivalent. The majority of references were from Organisation for Economic Co-operation and Development member countries, although data availability necessitated the inclusion of some non-members (e.g., Thailand for grasshoppers and Iran for chickpeas, lentils and peanuts) [37][38][39][40]. Finally, where multiple LCA data sources for individual foods were available that met our requirements, we calculated mean and standard deviation for the food, which are shown in Figure 3.…”

Section: Methodsmentioning

confidence: 99%

Integrating Protein Quality and Quantity with Environmental Impacts in Life Cycle Assessment

et al. 2019

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Life cycle assessment (LCA) evaluates environmental impacts of a product from material extraction through disposal. Applications of LCA in evaluating diets and foods indicate that plant-based foods have lower environmental impacts than animal-based foods, whether on the basis of total weight or weight of the protein content. However, LCA comparisons do not differentiate the true biological value of protein bioavailability. This paper presents a methodology to incorporate protein quality and quantity using the digestible indispensable amino acid score (DIAAS) when making comparisons using LCA data. The methodology also incorporates the Food and Drug Administration’s (FDA) reference amounts customarily consumed (RACCs) to best represent actual consumption patterns. Integration of these measures into LCA provides a mechanism to identify foods that offer balance between the true value of their protein and environmental impacts. To demonstrate, this approach is applied to LCA data regarding common protein foods’ global warming potential (GWP). The end result is a ratio-based score representing the biological value of protein on a GWP basis. Principal findings show that protein powders provide the best efficiency while cheeses, grains, and beef are the least efficient. This study demonstrates a new way to evaluate foods in terms of nutrition and sustainability.

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Section: Methodsmentioning

confidence: 99%

Integrating Protein Quality and Quantity with Environmental Impacts in Life Cycle Assessment

et al. 2019

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“…We’re going to process video data more efficiently. At present, computer vision technology has been widely used in agricultural research [ 30 , 31 , 32 , 33 ], such as crop pest detection [ 34 , 35 , 36 ] or pest activity detection [ 37 ], crop disease detection [ 38 ], identification of crop growth [ 39 , 40 ], crop yield prediction [ 41 ], and animal behavior detection [ 26 , 27 , 42 ]. The first four kinds of applications can get good results by processing and analyzing only a few clear images.…”

Section: Discussionmentioning

confidence: 99%

Application of Spatio-Temporal Context and Convolution Neural Network (CNN) in Grooming Behavior of Bactrocera minax (Diptera: Trypetidae) Detection and Statistics

Zhang

Zhan

et al. 2020

Insects

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Statistical analysis and research on insect grooming behavior can find more effective methods for pest control. Traditional manual insect grooming behavior statistical methods are time-consuming, labor-intensive, and error-prone. Based on computer vision technology, this paper uses spatio-temporal context to extract video features, uses self-built Convolution Neural Network (CNN) to train the detection model, and proposes a simple and effective Bactrocera minax grooming behavior detection method, which automatically detects the grooming behaviors of the flies and analysis results by a computer program. Applying the method training detection model proposed in this paper, the videos of 22 adult flies with a total of 1320 min of grooming behavior were detected and analyzed, and the total detection accuracy was over 95%, the standard error of the accuracy of the behavior detection of each adult flies was less than 3%, and the difference was less than 15% when compared with the results of manual observation. The experimental results show that the method in this paper greatly reduces the time of manual observation and at the same time ensures the accuracy of insect behavior detection and analysis, which proposes a new informatization analysis method for the behavior statistics of Bactrocera minax and also provides a new idea for related insect behavior identification research.

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“…In this study, the selected principal components were employed as inputs for developing the ANN models; while, energy flows for tea production was the output of the model. Then, the relationship between PCs (input variables) and energy output was analyzed in order to ensure the suitability of this selection . Data were split into three datasets of 80%, 10%, and 10% for training, testing and validation phases, respectively.…”

Section: Methodsmentioning

confidence: 99%

Integration of principal component analysis and artificial neural networks to more effectively predict agricultural energy flows

Nikkhah

Rohani

Rosentrater

et al. 2019

Env Prog and Sustain Energy

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There are some studies regarding the prediction of agricultural energy flows using artificial neural networks (ANNs). These models are quite sensitive to correlations amongst inputs, and, there are often strong correlations amongst energy inputs for agricultural systems. One potential method to remediate this problem is to use principal component analysis (PCA). Therefore, the purpose of this research was to predict energy flows for a specific agricultural system (Iranian tea production) via a novel methodology based on ANNs, and using principal components as model inputs, not raw data. PCA results showed that the first and second components could account for more than 99% of variation in the data, thus the dimensions of the data set could be decreased from six to two for the prediction of energy flows for Iranian tea production. Using these principal components as inputs, an ANN model with 2–15–1 structure was determined to be optimal for energy flow modeling of this system. To conclude, the results of this study highlighted that the use of PC as ANN inputs improved ANN model prediction through reducing its complexity and eliminating data colinearity. Many agricultural systems could benefit from using this methodology for energy modeling. © 2019 American Institute of Chemical Engineers Environ Prog, 38:e13130, 2019

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Combined application of Artificial Neural Networks and life cycle assessment in lentil farming in Iran

Cited by 31 publications

References 39 publications

Integrating Protein Quality and Quantity with Environmental Impacts in Life Cycle Assessment

Integrating Protein Quality and Quantity with Environmental Impacts in Life Cycle Assessment

Application of Spatio-Temporal Context and Convolution Neural Network (CNN) in Grooming Behavior of Bactrocera minax (Diptera: Trypetidae) Detection and Statistics

Integration of principal component analysis and artificial neural networks to more effectively predict agricultural energy flows

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