Artificial Intelligence in Agriculture最新文献

Energy lost due to shadow in the absorber plate of solar collectors can decrease the solar energy gain. In some studies, mathematical modeling was applied for calculating the energy gain reduction due to shadow in flat-plate solar collectors. In this study, ANN method was developed for modeling the energy gain reduction. Multilayer Perceptron (MLP) with one hidden layer and a range of neurons (5–30) by two training algorithms (LM and BR) and tangent sigmoid activation function (TanSig) were used by help of K-fold cross validation method. In the first section, six set of solar collector dimensions were used (1×1; 1×1.5; 1×2; 1.5×1.5; 1.5×2 and 2×2). In the second section all the range of dimensions were used as the inputs. The results of the first section showed that MLP with BR training algorithm can predict the energy gain reduction with minimum MAPE and RMSE in all the categories. The best results related to (1.5×1.5) dimension that achieved a MAPE of 0.15 ± 0.09% and RMASE of 4.42 ± 2.43 KJm⁻² year⁻¹, respectively. The results of the second section indicated that BR is a better training algorithm than LM. The MAPE and R² factors for the best topology (5-27-1) were 0.0610 ± 0.0051% and 0.9999 ± 0.0001, respectively. The results of the sensitivity analysis showed that height has the biggest impact on total energy gain reduction due to shadow in flat-plate solar collectors. Finally, the results of this study indicated that by using ANN and decrease the energy lost, the efficiency of solar collectors can be increased in all applications such as industry and agriculture.

Food traceability is an important aspect of the food safety supply chain to ensure efficient tracking of produce to check contamination and other foodborne diseases. The health and nutrition response after the Covid-19 pandemic requires a robust and diverse food supply chain in which traceability could play a major role. Since it is an emerging field of study with growing interest in the technological front, it is important to study the scientific trend and research activities. This study provides an important insight into the food safety value chain response towards modern food safety management systems through scientometric analysis. Scopus database was used to retrieve the documents from the year 1992–2021. The research papers and conference papers were only chosen. Vosviewer software was used to carry out the scientometric analysis. The distribution and growth trend of documents, country-level distribution of publications, the relationship between authors and co-authors, etc., were analyzed. The intensity of publications from different countries and the collaborations was analyzed using bibliometrix R-package. The year-wise research publication showed a rapid increase in the researchers conducted on traceability systems to enhance food safety from 2014 onwards, mainly from the USA and China. However, the research appeared to be in the developing phase compared to other technology implementation and automation advancements.

Searching, recognizing and retrieving a video of interest from a large collection of a video data is an instantaneous requirement. This requirement has been recognized as an active area of research in computer vision, machine learning and pattern recognition. Flower video recognition and retrieval is vital in the field of floriculture and horticulture. In this paper we propose a model for the retrieval of videos of flowers. Initially, videos are represented with keyframes and flowers in keyframes are segmented from their background. Then, the model is analysed by features extracted from flower regions of the keyframe. A Linear Discriminant Analysis (LDA) is adapted for the extraction of discriminating features. Multiclass Support Vector Machine (MSVM) classifier is applied to identify the class of the query video. Experiments have been conducted on relatively large dataset of our own, consisting of 7788 videos of 30 different species of flowers captured from three different devices. Generally, retrieval of flower videos is addressed by the use of a query video consisting of a flower of a single species. In this work we made an attempt to develop a system consisting of retrieval of similar videos for a query video consisting of flowers of different species.

The measurement of aromaticity in biochars is generally conducted using solid state ¹³C nuclear magnetic resonance spectroscopy, which is expensive, time-consuming, and only accessible in a small number of research-intensive universities. Mathematical modelling could be a viable alternative to predict biochar aromaticity from other much easier accessible parameters (e.g. elemental composition). In this research, Genetic Programming (GP), an advanced machine learning method, is used to develop new prediction models. In order to identify and evaluate the performance of prediction models, an experimental data set with 98 biochar samples collected from the literature was utilized. Due to the benefits of the intelligence iteration and learning of GP algorithm, a kind of underlying exponential relationship between the elemental compositions and the aromaticity of biochars is disclosed clearly. The exponential relationship is clearer and simpler than the polynomial mapping relationships implicated by Maroto-Valer, Mazumdar, and Mazumdar-Wang models. In this case, a novel exponential model is proposed for the prediction of biochar aromaticity. The proposed exponential model appears better prediction accuracy and generalization ability than existing polynomial models during the statistical parameter evaluation.