相关参考文献
注意：仅列出部分参考文献，下载原文获取全部文献信息。Prediction of greenhouse tomato yield using artificial neural networks combined with sensitivity analysis
Khaled Belouz et al.
SCIENTIA HORTICULTURAE (2022)
Simultaneous extraction of polysaccharides and polyphenols from blackcurrant fruits: Comparison between response surface methodology and artificial neural networks
Xueying Bu et al.
INDUSTRIAL CROPS AND PRODUCTS (2021)
Prediction of loquat soluble solids and titratable acid content using fruit mineral elements by artificial neural network and multiple linear regression
Xiao Huang et al.
SCIENTIA HORTICULTURAE (2021)
Modelling of corn kernel pre-treatment, drying and processing for ethanol production using artificial neural networks
Neven Voca et al.
INDUSTRIAL CROPS AND PRODUCTS (2021)
Application of artificial neural network (ANN) model for prediction and optimization of coronarin D content in Hedychium coronarium
Asit Ray et al.
INDUSTRIAL CROPS AND PRODUCTS (2020)
A Comprehensive Peach Fruit Quality Evaluation Method for Grading and Consumption
Guoxiang Zhang et al.
APPLIED SCIENCES-BASEL (2020)
Estimation of kiwifruit yield by leaf nutrients concentration and artificial neural network
Ali Mohammadi Torkashvand et al.
JOURNAL OF AGRICULTURAL SCIENCE (2020)
Evaluation of image processing technique as an expert system in mulberry fruit grading based on ripeness level using artificial neural networks (ANNs) and support vector machine (SVM)
Hossein Azarmdel et al.
POSTHARVEST BIOLOGY AND TECHNOLOGY (2020)
Assessment of bruchids density through bioacoustic detection and artificial neural network (ANN) in bulk stored chickpea and green gram
Km Sheetal Banga et al.
JOURNAL OF STORED PRODUCTS RESEARCH (2020)
Determination of Biochemical Composition in Peach (Prunus persica L. Batsch) Accessions Characterized by Different Flesh Color and Textural Typologies
Sara Serra et al.
FOODS (2020)
Artificial neural networks and multiple linear regression as potential methods for modeling seed yield of safflower (Carthamus tinctorius L.)
Moslem Abdipour et al.
INDUSTRIAL CROPS AND PRODUCTS (2019)
Geographical origin traceability of tea based on multi-element spatial distribution and the relationship with soil in district scale
Lei Li et al.
FOOD CONTROL (2018)
Using the artificial neural network to estimate leaf area
Ali Shabani et al.
SCIENTIA HORTICULTURAE (2017)
Modelling the solid-liquid adsorption processes using artificial neural networks trained by pseudo second order kinetics
K. Vasanth Kumar et al.
CHEMICAL ENGINEERING JOURNAL (2009)
Predicting average regional yield and production of wheat in the Argentine Pampas by an artificial neural network approach
R. Alvarez
EUROPEAN JOURNAL OF AGRONOMY (2009)
分享论文
