Frontiers in the Solicitation of Machine Learning Approaches in Vegetable Science Research

Along with essential nutrients and trace elements, vegetables provide raw materials for the food processing industry. Despite this, plant diseases and unfavorable weather patterns continue to threaten the delicate balance between vegetable production and consumption. It is critical to utilize machine learning (ML) in this setting because it provides context for decision-making related to breeding goals. Cutting-edge technologies for crop genome sequencing and phenotyping, combined with advances in computer science, are currently fueling a revolution in vegetable science and technology. Additionally, various ML techniques such as prediction, classification, and clustering are frequently used to forecast vegetable crop production in the field. In the vegetable seed industry, machine learning algorithms are used to assess seed quality before germination and have the potential to improve vegetable production with desired features significantly; whereas, in plant disease detection and management, the ML approaches can improve decision-support systems that assist in converting massive amounts of data into valuable recommendations. On similar lines, in vegetable breeding, ML approaches are helpful in predicting treatment results, such as what will happen if a gene is silenced. Furthermore, ML approaches can be a saviour to insufficient coverage and noisy data generated using various omics platforms. This article examines ML models in the field of vegetable sciences, which encompasses breeding, biotechnology, and genome sequencing

Development of chemometric multivariate calibration models for spectroscopic quality analysis of biodiesel blends

Thesis (Master)--İzmir Institute of Technology, Chemistry, İzmir, 2011Includes bibliographical references (leaves: 128-132)Text in English; Abstract: Turkish and Englishxiii, 132 leavesThe fact that the biodiesel is produced from renewable resources and environmentally friendly when compared to the fossil-based petroleum diesel, biodiesel has gained an increasing interest. It is mainly produced from a variety of different animal fat and vegetable oil combined with an alcohol in the presence of a homogeneous catalyst and the determination of the quality of the produced biodiesel is as important as its production. Industrial scale biodiesel production plants have been adopted the chromatographic analysis protocols some of which are standard reference methods proposed by official bodies of the governments and international organizations. However, analysis of multi component mixtures by chromatographic procedures can become time consuming and may require a lot of chemical consumption. For this reason, as an alternative, spectroscopic methods combined with chemometrics offer several advantages over classical chromatographic procedures in terms of time and chemical consumption. With the immense development of computer technology and reliable fast spectrometers, new chemometric methods have been developed and opened up a new era for processing of complex spectral data. In this study, laboratory scale produced biodiesel was mixed with methanol, commercial diesel and several different vegetable oils that are used to prepare biodiesels and then several different ternary mixture systems such as diesel-vegetable oil-biodiesel and methanol-vegetable oil-biodiesel were prepared and gas chromatographic analysis of these samples were performed. Then, near infrared (NIR) and mid infrared (FTIR) spectra of the same samples were collected and multivariate calibration models were constructed for each component for all the infrared spectroscopic techniques. Chemometric multivariate calibration models were proposed as genetic inverse least square (GILS) and artificial neural networks (ANN). The results indicate that determination of biodiesel blends quality with respect to chemometric modeling gives reasonable consequences when combined with infrared spectroscopic techniques

Implementation of Particle Swarm Optimization (PSO) to Improve Neural Network Performance in Univariate Time Series Prediction

One of the oldest known predictive analytics techniques is time series prediction. The target in time series prediction is use historical data about a specific quantity to predicts value of the same quantity in the future. Multivariate time series (MTS) data has been widely used in time series prediction research because it is considered better than univariate time series (UTS) data. However, in reality MTS data sets contain various types of information which makes it difficult to extract information to predict the situation. Therefore, UTS data still has a chance to be developed because it is actually simpler than MTS data. UTS prediction treats forecasts as a single variable problem, whereas MTS may employ a large number of time-concurred series to make predictions. Neural Network (NN) model could be built to predict the target variable given the other (predictor) variables. In this study, we used Particle Swarm Optimization (PSO) algorithm to optimize performance of NN on a UTS dataset. Our proposed model is validated using x-validation and and use RMSE to measure its performance. The experimental results show that NN performance after optimization using PSO produces good results compared to classical NN performance. This is evidenced by the value of RMSE = 0.410 which is the smallest RMSE value produced. The smaller the RMSE value, the better the model performance. It can be concluded that the proposed method can improve NN performance on UTS data

Research on the Design of Financial Management Model Based on SOM-PNN Driven by Digital Economy

This study proposes a novel financial risk prediction methodology by harnessing the power of self-organizing mapping (SOM) neural network and probabilistic neural network (PNN). The amalgamation of SOM and PNN\u27s advantageous characteristics is seamlessly integrated into the algorithm posited within this paper. In order to collate and prognosticate data, the SOM network employs a two-dimensional topological framework comprising of two layers of neurons. Subsequently, the PNN model expeditiously furnishes the final classification outcomes by processing the output results obtained from the SOM model. The technique developed atop this composite model offers accelerated computation, effectively mitigates the impact of noisy samples, and significantly augments model accuracy. Finally, the effectiveness of the proposed method was demonstrated through a comprehensive financial risk analysis of listed companies from 2016 to 2020. The experimental results show that the SOM-PNN method has achieved high accuracy in predicting the financial difficulties experienced by traditional companies in the selected company samples, exceeding 85%. Especially when the sample data is insufficient, its accuracy reaches 80%, surpassing other algorithms. Statement: In the modern era, financial institutions use big data to perform background analysis and review, continuously optimize, and adjust, in order to introduce quantitative analysis methods into every link of risk management as far as possible. This allows financial institutions to quickly achieve balance in the game process of risk and income, and achieve Profit maximization in local or even more space

Prediksi Harga Komoditas Pertanian Menggunakan Hybrid Algoritma Jaringan Syaraf Tiruan Arsitektur Radial Basis Function (RBF) dengan Algoritma Genetika

Harga komoditas pertanian seperti bawang merah dan cabai merah biasa sangat fluktuatif sehingga membuat masyarakat Indonesia menjadi sensitif akan hal itu. Banyak masalah yang dihadapi di Indonesia, contohnya harga sayuran yang tergolong tidak stabil, salah satu faktornya yaitu cuaca. Hal tersebut berdampak besar bagi masyarakat, memprediksi harga sayuran merupakan salah satu solusi untuk masalah ini. Oleh karena itu, pada tugas akhir ini dilakukan suatu metode untuk memprediksi harga bawang merah dan cabai merah biasa agar didapatkan gambaran kejadian yang akan datang. Penelitian ini akan menghasilkan sebuah sistem yang dapat digunakan untuk memprediksi komoditas pertanian yaitu bawang merah dan cabai merah biasa yang disertai curah hujan dan tanpa curah hujan untuk 10 minggu kedepan berdasarkan data harga mingguan komoditas tersebut dan data mingguan curah hujan di Bandung. Prediksi ini menggunakan Algoritma Neural Network (NN) atau dalam bahasa Indonesia yaitu Jaringan Syaraf Tiruan (JST) untuk memprediksi harga komoditas pertanian bawang merah dan cabai merah biasa. Akan tetapi, RBFNN memiliki kelemahan dalam menentukan nilai center yang optimal. Untuk mendapatkan hasil terbaik, maka Algoritma Genetika akan digunakan untuk mengoptimasi RBFNN. Algoritma Genetika membangkitkan sejumlah individu random dengan representasi integer yang berarti posisi dari data input, kemudian individu tersebut akan dikodekan sehingga mendapatkan nilai center dari data. Setiap individu akan dievaluasi menggunakan algoritma RBFNN untuk mencari individu terbaik berdasarkan fitnessnya, setelah itu dilakukan operator GA lainnya seperti seleksi orang tua, rekombinasi dan mutasi sehingga didapatkan individu yang berisi nilai center di RBFNN yang optimal. Penelitian hybrid GA dan RBFNN dengan kasus memprediksi harga komoditas pertanian yaitu sayuran jamur dengan nama latin Lentionus edodes pernah dilakukan di China oleh Changshou Luo, Qingfeng Wei, Liying Zhou, Junfeng Zhang dan Sufen Sun dengan judul “Prediction of Vegetable Price Based on Neural Network and Genetic Algorithm” dengan MAE yang didapatkan 0.144. Untuk sistem prediksi harga bawang merah tanpa disertai curah hujan didapatkan nilai center yang optimal dengan inputan 22, ukuran populasi 50, maksimal generasi 500, probabilitas crossover (Pc) 0.8, probabilitas mutasi (Pm) 0.1 dengan MAPE yg didapatkan 16.166, sedangkan untuk prediksi bawang merah yang disertai curah hujan yang optimal dengan inputan 4, ukuran populasi 50, maksimal generasi 500, Pc 0.6, Pm 0.1 dengan MAPE yg didapatkan 19.212, sedangkan untuk sistem prediksi cabai merah biasa tanpa disertai curah hujan yang optimal dengan inputan 26, ukuran populasi 50, maksimal generasi 500, Pc 0.6, Pm 0.1 dengan MAPE 24.116 dan untuk cabai merah disertai curah hujan dengan inputan 52, ukuran populasi 50, maksimal generasi 500, Pc 0.6 dan Pm 0.1 didapatkan MAPE 18.723. Kata kunci : Prediksi, harga komoditas pertanian, curah hujan, Neural Networks, Genetic Algorithm, Radial Basis Function

Trends in application of NIR and hyperspectral imaging for food authentication

Food fraud can cause damage to consumer health and affect their confidence, destroy brands and generate large economic losses in the industry. Food authenticity allows to identify if food composition, geographical origin, genetic variety and farming system corresponds to what has been declared on the label. Although there are currently standardized methods to identify certain adulterants, the complexity of the food, the complexity of the supply chain and the appearance of new adulterants require the continuous development of analytical techniques to detect food fraud. NIR and Hyperspectral imaging (HSI) in tandem with chemometrics are non-destructive, non-invasive and accurate techniques for food authentication. This review focuses on NIR and HIS approaches to food authentication, including adulteration by substitution, geographical origin and farming system. In this context, the advances in NIR and HSI approaches reported since 2014 are discussed regarding their potential use in food authentication. Both techniques have shown to have efficiency, precision and selectivity to detect adulterants and identify geographic origin, genetic variety and farming system. Portability and remote access are shown as the next step for the industrialization of NIR and HSI devices

Modeling Bankruptcy Prediction for Non-Financial Firms: The Case of Pakistan

This paper aims to identify the financial ratios that are most significant in bankruptcy prediction for the non-financial sector of Pakistan based on a sample of companies which became bankrupt over the 1996-2006 period. Twenty four financial ratios covering four important financial attributes namely profitability, liquidity, leverage, and turnover ratios) were examined for a five-year period prior bankruptcy. The discriminant analysis produced a parsimonious model of three variables viz. sales to total assets, EBIT to current liabilities, and cash flow ratio. Our estimates provide evidence that the firms having Z value below zero fall into the “bankrupt” whereas the firms with Z value above zero fall into the “non-bankrupt” category. The model achieved 76.9% prediction accuracy when it is applied to forecast bankruptcies on the underlying sample.Bankruptcy; Z-Score; Non-Financial Firms; Financial Ratios; Pakistan

Energy Consumption and Modeling of output energy with Multilayer Feed-Forward Neural Network for Corn Silage in Iran

In this study, various Artificial Neural Networks (ANNs) were developed to estimate the output energy for corn silage production in Esfahan province, Iran. For this purpose, the data on 65 corn silage production farms in the Esfahan province, were collected and analyzed. The results indicated that total energy input for corn silage production was about 83126 MJ ha–1; machinery (with 38.8 %) and chemical fertilizer (with 24.5 %) were amongst the highest energy inputs for corn silage production. The developed ANN was a multilayer perceptron (MLP) with eight neurons in the input layer (human power, machinery, diesel fuel, chemical fertilizer, water for irrigation, seed, farm manure and pesticides ), one, two, three, four and five hidden layer(s) of various numbers of neurons and one neuron (output energy) in the output layer. The results of ANNs analyze showed that the (8-5-5-1)-MLP, namely, a network having five neurons in the first and second hidden layer was the best-suited model estimating the corn silage output energy. For this topology, MAB, MAE, RMSE and R2 were 0.109, 0.001, 0.0464 and 98%, respectively. The sensitivity analysis of input parameters on output showed that diesel fuel and seeds had the highest and lowest sensitivity on output energy with 0.0984 and 0.0386, respectively. The ANN approach appears to be a suitable method for modeling output energy, fuel consumption, CO2 emission, yield, and energy consumption based on social and technical parameters. This method would open new doors to advances in agriculture and modeling

Optimización de la gestión de redes de riego a presión a diferentes escalas mediante Inteligencia Artificial

Factors such as climate change, world population growth or the competition for the water resources make freshwater availability become an increasingly large and complex global challenge. Under this scenario of reduced water availability, increasing droughts frequency and uncertainties associated with a changing climate, the irrigated agriculture sector, particularly in the Mediterranean region, will need to be even more efficient in the use of the water resources. In Spain, many irrigation districts have been modernized in recent years, replacing the obsolete open channels by pressurized water distribution networks towards improvements in water use efficiency. Thanks to this, water use has reduced but the energy demand and the water costs have dramatically increased. Thus, strategies to reduce simultaneously water and energy uses in irrigation districts are required. This thesis consists of nine chapters, which include several models to optimize the management of the irrigation districts and increase the efficiency of water and energy use.Factores tales como el cambio climático, el crecimiento de la población mundial o la competencia por los recursos hídricos hacen que la disponibilidad de agua se esté convirtiendo en un desafío global cada vez más grande y complejo. En este escenario de reducción de la disponibilidad de agua, aumento de la frecuencia de las sequías y de las incertidumbres asociadas a un cambio climático, el sector de la agricultura de regadío, en particular en la región mediterránea, tendrá que ser aún más eficiente en el uso de los recursos hídricos. En España, muchas comunidades de regantes se han modernizado en los últimos años, sustituyendo los obsoletos canales abiertos por redes de distribución de agua a presión con el objetivo de mejorar la eficiencia en el uso del agua. Gracias a esto, el uso del agua se ha reducido, pero la demanda de energía y los costos del agua se han incrementado drásticamente. Por lo tanto, se requieren estrategias para reducir simultáneamente el uso de agua y energía en las comunidades de regantes. Esta tesis consta de nueve capítulos que incluyen varios modelos para optimizar la gestión de las comunidades de regantes y aumentar la eficiencia en el uso del agua y la energía