Vegetable Commodities Research Articles

Price And Replenishment of Vegetables Based on NARX Neural Network

The research on the replenishment and pricing of vegetable commodities is helpful for supermarket to make more scientific purchase and sale decisions. Based on this, this paper mainly uses NARX neural network to study the replenishment and pricing of vegetable commodities. Firstly, it preprocesses the missing and abnormal values of the data, constructs a correlation analysis model to study the relationship between the sales of various categories of vegetables, then establishes a NARX neural network model to solve the daily replenishment total, and finally constructs a single objective programming model to get the pricing strategy, which provides a theoretical basis for supermarket to make scientific pricing and replenishment decisions.

Automatic Pricing and Replenishment Decision of Vegetable Commodities Based on ARIMA—Nonlinear Modeling

Automatic Pricing and Replenishment Decision of Vegetable Commodities Based on ARIMA—Nonlinear Modeling

Research on automatic pricing of vegetable commodities based on optimization model

Research on automatic pricing of vegetable commodities based on optimization model

Replenishment and Pricing Strategy Analysis of Vegetable Commodities Based on Time Series Model

Replenishment and Pricing Strategy Analysis of Vegetable Commodities Based on Time Series Model

Climate Change Impact Analysis on Food Availability in the Province of Kalimantan Utara, Indonesia

Climate change directly affects crop yields as a measure of food availability by, for example, causing harvest failure. El Niño and La Niña are two weather patterns affecting the climate of Indonesia, a country at low latitudes, and reportedly increasing its vulnerability to food insecurity. This research was designed to analyze climate change impact on food availability in the Province of Kalimantan Utara using secondary data(temperature, rainfall, and agricultural production) as the base data. It employed quantitative descriptive analysis to explain the study results expressed in maps, tables, and graphs and the regression technique to determine climate change impact on food availability. The regression analysis revealed that climate change significantly shaped the availability of rice (0.008) and vegetable commodity (0.000) but posed insignificanteffects on tubers, legumes, and fruits. Apart from climate change, food availability also depends on land management, land suitability, capital, technology, and cultivated plant variety

Response of root anatomy and vitamin C content of Brassica juncea L. on biofertilizer application in a saline environment

Indian mustard (Brassica juncea L.) is part of the family Brassicaceae which has an annual-herbaceous habitus and is counted as a popular vegetable commodity in Indonesia so it has a high-demand market. To fulfill the demand market can be done by hydroponic cultivation and biofertilizer application. Application of biofertilizer which contains inoculant microorganisms can support plant growth with increased availability of nutrients in planting medium and neutralized salinity stress effect. Salinity stress is indicated by the high concentration of Na+ and Cl- ions in plants resulting in toxicity and osmotic stress which will inhibit plant growth. This study aimed to determine the effect of various dosages of biofertilizer on plant growth and productivity with plant height, leaf number, cortex thickness, metaxylem diameter, and ascorbic acid levels as parameters. This research used cow urine base biofertilizer containing nitrogen (N), phosphate (P), kalium (K), and calcium (Ca). There are a few treatments: negative control, positive control, biofertilizer treatment 40 mL, 80 mL, and 120 mL dosage, and salinity treatment by applying NaCl 5000 ppm 10 mL/system. As the result showed on 120 mL biofertilizer dosage has the highest mean of plant height and leaves number, and 80 ml biofertilizer dosage treatment has the highest average of cortex thickness and metaxylem diameter, but the various dosage of biofertilizer treatment did not have a significant effect on ascorbic acid levels increase. Growth in plants given biofertilizer is higher than the plant growth that grew in the negative control treatment. It can be concluded that the application of biofertilizer can support the growth of B. juncea L. in the hydroponic system under salinity stress.

Growth and Yield of Broccoli Vegetables (Brassica oleracea var. botrytis L.) on Types and Dosages of Manure

Broccoli is a vegetable commodity that has high commercial value and prospects in Indonesia, because of its role in fulfilling food, feed, and industrial needs. In cultivating broccoli plants, they still face production problems due to nutrient availability problems. Farmers generally use chemical fertilizers continuously. Continuous use of chemical fertilizers will increase soil acidity, damage the physical condition of the soil, reduce organic matter, make plants vulnerable to pests and disease, and reduce soil microbial life. Many consumers are interested in organic broccoli. The use of organic fertilizer as an alternative to chemical (inorganic) fertilizer can reduce environmental pollution and create healthy broccoli (Brassica oleracea) vegetable products. One of the organic fertilizers is manure which is a waste product from domestic animals such as chickens, goats, and pigs which are generally found in Tomohon City. Manure can be used to add nutrients, and improve the physical and biological properties of soil. The quality of manure depends on the animal and greatly influences crop yields. The research objectives are: (1) Studying the effect of the interaction between the type and dose of manure on broccoli growth and yield, (2) Obtaining the type and dose of manure that provides the highest growth and yield of broccoli, and (3) Get semi-organic broccoli by using a 25% dose of compound nitrogen, phosphorus, potassium (NPK) fertilizer. To achieve the above objectives, this research used a Factorial Randomized Block Design (FRBD) with 2 treatment factors and 3 replications the first factor was the type of manure (A) which consisted of A1: Chicken manure, A2: Goat manure, A3: Pig manure. The second factor is the dose of manure (B) which consists of B0: 125 kg NPK/hectare, B1: 20 manure/hectare, B2: 30 kg/hectare. The observation variables include plant height, number of leaves, diameter of broccoli per plant, and wet weight of broccoli per plant. Data analysis uses Analysis of Variance (ANOVA). If the results of the analysis show that there is an effect of treatment, then continue with the BNT test at the 5% level. The results showed that the treatment with goat manure at a dose of 20 tons/hectare and without manure at half the recommended dose of nitrogen, phosphorus, and potassium gave the highest number of leaves (18.67 and 19.67 leaves). The best broccoli crop diameter was treated with goat manure at a dose of 20 and 30 tons/hectare. The weight of the best broccoli crop in goat and pig manure at a dose of 20 tons/hectare was 47.67 grams.

An ARIMA-based study of pricing and replenishment decisions for vegetable commodities

Fresh produce superstores face many challenges as an important part of the livelihood industry. The short freshness period and variable quality make their daily sales efficiency crucial. A simple, efficient and applicable decision model for supermarkets is beneficial to further promote the healthy and sustainable development of the fresh food market and the overall livelihood industry. In order to predict the daily replenishment and pricing strategy of each vegetable category in the coming week, the key lies in analyzing the supermarket's historical data. Since the sales volume shows strong cyclical fluctuations, the time series model ARIMA is chosen for this purpose to observe the model training and predict the pricing of different categories of products in the next 7 days. Due to the cost-plus pricing mechanism of the superstore, after processing the data, we selected simple linear regression model, multiple linear regression model and nonlinear regression model to fit the data, and opted for multiple linear regression to get the sales volume on the cost-plus pricing of the fitting formula, taking into account the discount rate of the different types of vegetables, we corrected the sales volume of the six types of vegetables, and ultimately obtained the superstore's daily replenishment and pricing strategy for the next seven days.

Replenishment Forecasting of Vegetable Commodities Based on Time Series Modeling

Replenishment Forecasting of Vegetable Commodities Based on Time Series Modeling

Innovative Research on Pricing and Replenishment Decision Making of Vegetable Commodities Based on ARIMA Prediction Modeling

Innovative Research on Pricing and Replenishment Decision Making of Vegetable Commodities Based on ARIMA Prediction Modeling

Research on Pricing and Replenishment Strategies of Supermarket Vegetables Based on LSTM Neural Network and Goal Planning

This paper focuses on the daily replenishment volume and pricing of vegetable commodities, especially the impact of freshness on market selling price. Through Pearson correlation analysis, this study first explored the correlation between different vegetable categories and their single products and evaluated the sales correlation between each category and the single products of the same category. Secondly, this paper considers the cost-plus pricing method to analyze the relationship between the total sales volume of vegetable products and the pricing. The LSTM neural network model is used to forecast the sales volume and wholesale price of vegetable commodities, with special attention to the forecast data from July 1 to 7. Finally, this paper establishes a goal planning model with the goal of maximizing supermarket returns. Considering the total quantity of replenishment, sales volume and cost pricing as constraint conditions, the depth-first search algorithm is adopted to solve the problem, and the daily replenishment volume and pricing strategy for vegetable products in the next week are provided.

Prediction and Replenishment Decision Making for Automatic Pricing of Vegetable Commodities Based on LSTM Models

In this paper, the problem of automatic pricing and replenishment decision-making of vegetable commodities is studied, and the LSTM model is used to predict and optimize it, and the replenishment volume and pricing strategy to maximize the revenue of supermarkets are proposed. Calculating the total annual sales volume of each category and each vegetable, the distribution law of most vegetables with strong seasonal periodicity and high sales volume from September 2022 to January 2023 can be obtained, and finally the correlation coefficient between different categories and different single products of vegetable commodities is obtained through the Pearson correlation coefficient algorithm, among which the highest correlation coefficient between different categories is mosaic and cauliflower. The coefficient was 0.75, and the highest correlation coefficient between different single items was 0.99 for green eggplant (1) and purple round eggplant. According to the cost-plus pricing algorithm and formula, the cost-plus pricing of each vegetable category is calculated, and then the relationship between the total sales of each vegetable category and the cost-plus pricing can be obtained through visual analysis. Then, the LSTM time series model was constructed to predict the total daily replenishment of each vegetable category in the coming week, and finally the linear programming model was used to maximize the revenue as the objective function to finalize the total daily replenishment and pricing strategy, and the maximum revenue was 75,624 yuan.

Automatic Pricing and Replenishment Decisions for Vegetables Based on Time Series Modeling

The market is highly competitive. Reasonable pricing and timely replenishment decisions can help enterprises to grasp the market demand and competitive situation, maximize profits. Through data-driven decision-making and technical support, supply chain efficiency and stability can be optimized, customer satisfaction can be improved, and personalized products and services can be provided. This paper uses statistics to analyze the distribution law of sales volume among categories and single products, and solves the problem of finding the correlation between different categories and different single products of vegetable commodities and the distribution law of sales volume. According to the distribution law and relationship of the sales volume of each category and single product of vegetables, the replenishment plan is made by taking the category as a unit, the relationship between the total sales volume of each vegetable category and the cost-plus pricing is analyzed, and the daily replenishment volume and pricing strategy of each vegetable category in the coming week are given, and finally the optimal daily replenishment and pricing strategy is determined, which provides a reference for supermarket operation and supply chain management.

A Study on Autonomous Pricing and Replenishment Decision of Vegetable Commodities Based on Gray Time Series Forecasting and Single-Objective Planning Models

Automated pricing and replenishment of vegetable items is one of the hot issues of the new era. In order to make replenishment decisions on the day's vegetable goods for superstores, this paper first constructs a nonlinear regression model to fit the function of vegetable category pricing and daily sales volume, and finds that the two have a negative correlation; Second, this paper predicts the cost of each category of vegetables for the coming week by building a gray time series prediction model; Finally, this paper takes the pricing of different categories on different dates as the decision variable, maximizes the revenue of the superstore as the objective function, establishes a single-objective planning model with the maximum capacity of the category and other constraints, and determines the optimal daily replenishment and pricing decisions based on the dynamic planning algorithm using the idea of iteration.

Research on replenishment decision of vegetable commodities based on genetic algorithm

Fresh produce superstores, are an important component of the modern economy, however, they face multiple challenges in vegetable merchandise management, including how to forecast accurate sales trends, develop reasonable pricing strategies, and effective replenishment programs. Therefore, this study aims to provide a comprehensive set of vegetable merchandising strategies for superstores to help them better solve their problems. Firstly, this paper constructs an integrated fitting model to fit a polynomial function to each single vegetable item; secondly, taking amaranth as an example, it constructs an image of the parameters of the amaranth fitting function; based on the ARIMA model of various categories of vegetable demand and wholesale price prediction, it solves the amaranth demand using the ARIMA model, and uses the hyper-parameter optimization to obtain the model with the smallest BIC value; it establishes the ARIMA (5,1,6) model and forecast the demand for the next seven periods, with the objective of maximizing the revenue of the superstore, establish a linear programming model (LP), constraints, and solve the model based on genetic algorithm.

Automatic Pricing and Replenishment Strategy for Vegetable Products Based on XGBoost and LSTM Prediction Models

Due to the short shelf life and imbalance between supply and demand of vegetable commodities, it is important to develop reasonable replenishment and pricing strategies. This paper takes the measured data of a superstore as an example, and develops a reasonable decision-making program by studying the relationship between the information of each commodity in the vegetable category, the breakdown of the flow, the wholesale price and the recent attrition rate. First, the distribution pattern and interrelationship of the sales volume of each category and single product of vegetables are studied, and the distribution, difference and correlation between them are analyzed. Then, XGBoost is used to predict the sales volume and then LSTM is used to predict the wholesale price, to get the relationship between the total sales volume of vegetable categories and the cost-plus pricing, and for the total daily replenishment and pricing strategy in the coming week, a planning model is set up to solve the problem by using genetic algorithm. Finally, in order to maximize the revenue of the superstore, a goal planning model is established to take the sales unit price and sales volume as variables, and mathematical calculation methods are used to determine the daily sales volume and sales unit price and the maximum revenue, so as to determine the replenishment volume of a single product and the optimal pricing strategy. The proposed model fits the actual needs, is practical, efficient in calculation, and can effectively solve the proposed problem.

Research on Automatic Pricing and Replenishment Strategy of Vegetable Commodities Based on Fourier Series Fitting and Linear Programming

Vegetable goods are a common category of goods sold in superstores. In order to determine the relationship between commodity price and sales quantity, and develop a reasonable replenishment and pricing strategy, this paper firstly uses the curve fitter linear regression in the MATLAB toolbox to analyse the relationship between total sales volume of different categories and cost plus pricing, and secondly, uses the curve fitter Fourier series in the MATLAB toolbox to fit the sales volume curve in June and calculates the degree of fit for validation to predict the sales volume of each vegetable category in the first week of July, calculate the daily replenishment volume, and determine the pricing of each category using the results of linear regression. Vegetable category sales, calculating daily replenishment, and using linear regression results to determine pricing for each category. Then, we consider the pricing and replenishment strategy when the total number of single items is restricted, and construct a linear programming decision optimisation model with single items as the main body, taking the replenishment quantity of single items as the decision variable, the restriction of the total number of single items, the minimum display quantity and the satisfaction of category demand as the constraints, and the maximum revenue of the superstore as the objective function, and then we construct the linear programming decision optimisation model, and find out the pricing and replenishment quantities for a total of 28 individual items as well as for each single item.

Research on Vegetable Commodity Pricing and Replenishment based on Planning Models and Genetic Algorithm

This paper focuses on an in-depth study of supermarket vegetable pricing and replenishment problems, utilizing a variety of methods such as statistics, prediction models, planning models and other methods of analysis. First, the data were preprocessed, and frequency distribution histograms were drawn, revealing the distribution pattern and correlation between each category and each single product of vegetables through descriptive statistical analysis. Secondly, for the relationship between sales and pricing, the sales unit price was averaged through the cost-plus pricing formula, and MATLAB was used to nonlinearly fit the relationship between the total sales volume of the categories and the sales price, and the fitting result was further optimized through neural network, and a nonlinear planning model was established, and a genetic algorithm was used to solve the daily replenishment volume of supermarkets and the pricing strategy in order to achieve the maximization of revenue. Finally, the top-rated individual products were screened out by entropy weighting method, and a linear programming model was established to predict the replenishment quantity and pricing strategy for the coming day, which further provided effective decision support for the sales management of the supermarket.

Development of an Intelligent System to Determine Land Suitability for Horticultural Crops on Vegetable Commodities

Global climate change has a significant impact on the agricultural sector, including horticulture, with climate fluctuations such as increased temperatures and changes in rainfall patterns potentially affecting crop productivity. Sustainable horticultural agriculture is important for safeguarding natural resources and reducing environmental impacts. However, challenges from climate change and variations in land conditions can affect horticultural crop production. Identifying crops that are suitable for the climate and land conditions is key to agricultural sustainability. An intelligent and adaptive approach is needed in selecting the right crops to grow in the face of climate change. This research develops an artificial intelligence application for the recommendation of horticultural crop types according to land conditions and climate change. The model built involves AHP and MFEP methods. The model takes into account various land parameters with weights determined through the AHP approach, allowing this AI application to provide accurate recommendations based on data and modeling. Based on the tests conducted, the system was able to produce analysis with an accuracy rate of 85%.

Morpho-physiology and metabolite content of Cosmos caudatus Kunth. and yellow and orange Cosmos sulphureus Cav.

Abstract. Saleh I, Aziz SA, Melati M, Andarwulan N. 2023. Morpho-physiology and metabolite content of yellow and orange Cosmos sulphureus Cav. and Cosmos caudatus Kunth. Biodiversitas 24: 5739-5746. Cosmos sp., known as kenikir in Indonesia, is an underutilized vegetable commodity. Cosmos sulphureus Cav. (C. sulphureus), with orange and yellow flowers, is commonly found as an ornamental plant. C. sulphureus can also be used as vegetable crops, although the Cosmos species widely used as a vegetable is Cosmos caudatus Kunth. (C. caudatus). This study investigated the growth, shoot production, primary and secondary metabolite content, and antioxidant activity of yellow and orange C. sulphureus and C. caudatus. This experiment was arranged using a Randomized Completely Block Design (RCBD) with one factor: Cosmos species (yellow and orange C. sulphureus and C. caudatus) with six replications. The result showed that plant height, leaf number, and branch number were not significantly different among the three Cosmos studied; however, the shoot weight per plant of orange C. sulphureus was significantly higher than the others. The antioxidant activity and phenol content of C. sulphureus were lower than that of C. caudatus. Meanwhile, crude fiber, flavonoid, anthocyanin, and carotenoid content were not significantly different among the three kinds of Cosmos. Orange C. sulphureus could be a functional vegetable, although its antioxidant activity is lower than C. caudatus.