Machine vision in estimation of fruit crop yield

2014 ◽  
pp. 329-374 ◽  
Author(s):  
A Payne ◽  
K Walsh
Keyword(s):  
Machine Vision ◽  
Crop Yield ◽  
Fruit Crop

scholarly journals Mathematical modeling of climatological data to estimate passion fruit crop yield (Passiflora edulis L. f. Flavicarpa y purpurea)

2021 ◽  
Vol 43 (3) ◽  
Author(s):  
Leila Nayibe Ramírez Castañeda ◽  
Gina Paola González Angarita ◽  
José-Alejandro Cleves-Leguizamo
Keyword(s):  
Regression Model ◽  
Multiple Regression ◽  
Crop Yield ◽  
Arima Model ◽  
Multiple Regression Model ◽  
Passion Fruit ◽  
Water Yield ◽  
Yield Model ◽  
Fruit Crop ◽  
Dependence Relationship

Abstract Passion fruit crop yield depends on the behavior of climatic variables, and modeling the dependence relationship of these variables regarding crop yield offers information aimed at facilitating agribusiness decision making. As main aim, passion fruit crop yield was estimated using mathematical models. A multivariate and univariate statistical analysis of meteorological variables was carried out during the observation period between 2007 and 2014 of selected weather stations, identified and located in the Colombian middle tropics (County of Huila). The relationship between yield with the following agroclimatic variables were analyzed: temperature, sunlight, relative humidity, rainfall and ENSO at monthly resolution with empirical and mechanistic models, recommended in scientific literature. Results showed that the multiple regression model requires the highest yield peaks; the adjustment of the multiple regression model is low, while univariate models such as the ARIMA model showed better adjustment in the time series analyzed. The Stewart’s water-yield model has better performance to estimate yield as a function of evapotranspiration in the different phenological phases.

Machine Vision

2009 ◽  
Author(s):  
Wesley E. Snyder ◽  
Hairong Qi
Keyword(s):  
Machine Vision

K-MEANS CLUSTERING ALGORITHM BASED CLASSIFICATION OF SOIL FERTILITY IN NORTH WEST NIGERIA

2020 ◽  
Vol 4 (2) ◽  
pp. 780-787
Author(s):  
Ibrahim Hassan Hayatu ◽  
Abdullahi Mohammed ◽  
Barroon Ahmad Isma’eel ◽  
Sahabi Yusuf Ali
Keyword(s):  
Soil Fertility ◽  
Crop Yield ◽  
Clustering Algorithm ◽  
Soil Samples ◽  
North West ◽  
R Programming ◽  
Available Information ◽  
Northwest Region ◽  
The Relationship

Soil fertility determines a plant's development process that guarantees food sufficiency and the security of lives and properties through bumper harvests. The fertility of soil varies according to regions, thereby determining the type of crops to be planted. However, there is no repository or any source of information about the fertility of the soil in any region in Nigeria especially the Northwest of the country. The only available information is soil samples with their attributes which gives little or no information to the average farmer. This has affected crop yield in all the regions, more particularly the Northwest region, thus resulting in lower food production.  Therefore, this study is aimed at classifying soil data based on their fertility in the Northwest region of Nigeria using R programming. Data were obtained from the department of soil science from Ahmadu Bello University, Zaria. The data contain 400 soil samples containing 13 attributes. The relationship between soil attributes was observed based on the data. K-means clustering algorithm was employed in analyzing soil fertility clusters. Four clusters were identified with cluster 1 having the highest fertility, followed by 2 and the fertility decreases with an increasing number of clusters. The identification of the most fertile clusters will guide farmers on where best to concentrate on when planting their crops in order to improve productivity and crop yield.

Classroom Lighting Energy-Saving Control System Based on Machine Vision Technology

2018 ◽  
pp. 143-149 ◽  
Author(s):  
Ruijie CHENG
Keyword(s):  
Control System ◽  
Machine Vision ◽  
Energy Saving ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Lighting Control ◽  
Lighting Energy ◽  
Neural Network Algorithm ◽  
Energy Saving Control ◽  
Model Algorithm

In order to further improve the energy efficiency of classroom lighting, a classroom lighting energy saving control system based on machine vision technology is proposed. Firstly, according to the characteristics of machine vision design technology, a quantum image storage model algorithm is proposed, and the Back Propagation neural network algorithm is used to analyze the technology, and a multi­feedback model for energy­saving control of classroom lighting is constructed. Finally, the algorithm and lighting model are simulated. The test results show that the design of this paper can achieve the optimization of the classroom lighting control system, different number of signals can comprehensively control the light and dark degree of the classroom lights, reduce the waste of resources of classroom lighting, and achieve the purpose of energy saving and emission reduction. Technology is worth further popularizing in practice.

Machine Vision, Let's Go into the Street

1997 ◽  
Vol 117 (10) ◽  
pp. 1339-1344
Author(s):  
Katsuhiko Sakaue ◽  
Hiroyasu Koshimizu
Keyword(s):  
Machine Vision

Machine Vision Technologies to Connect with Humans

2005 ◽  
Vol 125 (11) ◽  
pp. 692-695
Author(s):  
Kazunori UMEDA ◽  
Yoshimitsu AOKI
Keyword(s):  
Machine Vision

Evaluation of nitrogen dynamics and crop yield in a long-term tillage experiment

2007 ◽  
Vol 35 (2) ◽  
pp. 769-772 ◽  
Author(s):  
Attila Megyes ◽  
Tamás Rátonyi ◽  
Dénes Sulyok
Keyword(s):  
Crop Yield ◽  
Nitrogen Dynamics
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