Regression Analysis to Estimate Morphology Parameters of Pepper Plant in a Controlled Greenhouse System

Basak, Jayanta Kumar; Qasim, Waqas; Okyere, Frank Gyan; Khan, Fawad; Lee, Yong Jin; Park, Jihoon; Kim, Hyeon Tae

doi:10.1007/s42853-019-00014-0

Cited by 32 publications

(15 citation statements)

References 34 publications

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“…Likewise FFBP, the efficiency of regression-based model also depends on the existence of linear relationships between input and output variables (Basak et al, 2019). By Assessment of the Influence of Environmental Variables on Pig's Body Temperature using ANN and MLR Models taking the same inputs and output data which were used for the FFBP, the efficiency of the regression model was evaluated to estimate the most influential factors in affecting PBT.…”

Section: Ffbp and Mlr Model Performancementioning

confidence: 99%

Assessment of the Influence of Environmental Variables on Pig’s Body Temperature using ANN and MLR Models

Basak

Arulmozhi

Khan

et al. 2020

IJAR

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An experiment was conducted to find out the most influential factors affecting pig’s body temperature (PBT). For this purpose, eight environmental parameters and three growth related factors were considered as variables. Among these factors, seven environmental parameters, including temperature, CO2, temperature-humidity index inside and outside the pig’s barn and relative humidity inside the barn were taken as input variables for artificial neural networks (ANN) and multiple linear regression (MLR) models due to their good correlation (r ³ 0.5) with PBT. The results showed that ANN and MLR models had the lowest R2 values (0.81 and 0.69, respectively) and the highest RMSE (1.17 and 1.48, respectively) when they were run without temperature-humidity index; however, the maximum R2 (0.90 and 0.75, respectively) and minimum RMSE (0.92 and 1.40, respectively) were found without relative humidity. Based on the results, the temperature-humidity index could represent an important indicator in registering early warning signs of PBT status alternations.

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Section: Ffbp and Mlr Model Performancementioning

confidence: 99%

Assessment of the Influence of Environmental Variables on Pig’s Body Temperature using ANN and MLR Models

Basak

Arulmozhi

Khan

et al. 2020

IJAR

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“…Tractor ATs can be estimated using relatively inexpensive sensors and multiple linear regression (MLR)-based models [ 11 ]. MLR-based models have been used to estimate dependent variables in various research fields, where the models are developed based on statistical analysis methods by adopting explanatory variables that are closely related to the prediction targets [ 12 ]. In general, it is important to select variables that have a great influence on the AT of a tractor, in order to develop MLR-based estimation models [ 13 ].…”

Section: Introductionmentioning

confidence: 99%

Estimation of Axle Torque for an Agricultural Tractor Using an Artificial Neural Network

Kim

Lee

Kim

et al. 2021

Sensors

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The objective of this study was to develop a model to estimate the axle torque (AT) of a tractor using an artificial neural network (ANN) based on a relatively low-cost sensor. ANN has proven to be useful in the case of nonlinear analysis, and it can be applied to consider nonlinear variables such as soil characteristics, unlike studies that only consider tractor major parameters, thus model performance and its implementation can be extended to a wider range. In this study, ANN-based models were compared with multiple linear regression (MLR)-based models for performance verification. The main input data were tractor engine parameters, major tractor parameters, and soil physical properties. Data of soil physical properties (i.e., soil moisture content and cone index) and major tractor parameters (i.e., engine torque, engine speed, specific fuel consumption, travel speed, tillage depth, and slip ratio) were collected during a tractor field experiment in four Korean paddy fields. The collected soil physical properties and major tractor parameter data were used to estimate the AT of the tractor by the MLR- and ANN-based models: 250 data points were used for developing and training the model were used, the 50 remaining data points were used to test the model estimation. The AT estimated with the developed MLR- and ANN-based models showed agreement with actual measured AT, with the R2 value ranging from 0.825 to 0.851 and from 0.857 to 0.904, respectively. These results suggest that the developed models are reliable in estimating tractor AT, while the ANN-based model showed better performance than the MLR-based model. This study can provide useful results as a simple method using ANNs based on relatively inexpensive sensors that can replace the existing complex tractor AT measurement method is emphasized.

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Section: Methodsmentioning

confidence: 99%

“…All the vegetative and reproductive growth parameters were measured after 8 weeks and 16 weeks after the cultivation period and yield attributing parameters like the number of fruit set and the number of fruits per plant were measured beginning and end of the reproductive stage (8 weeks and 16 weeks of planting). Plant height was measured in 25 plants of each treatment in two different stages using a metric ruler as reported by Basak et al (2019). Generally, fresh weight and dry weight of 6 root samples from each treatment in two different stages were measured using a digital balance (Model-FX-300iWP, A&D Company Limited, Tokyo, Japan) and drying oven (Shelves for 5E-DHG6310: 2 layers, Changsha Kaiyuan Instruments Co., Ltd, Changsha 410100, PR China).…”

Section: Experimental Designmentioning

confidence: 99%

Influence of different growing media on the growth and development of strawberry plants

Madhavi

Khan

Bhujel

et al. 2021

Heliyon

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The optimal production of strawberries requires the essential nutrients and favourable media for vegetative and reproductive growth. The present study sought to determine the effectiveness of growth parameters and fruit yield of strawberries in different media growing under a greenhouse. To analyze the significant effect for the growth and fruit yield among the growing media, four treatments such as control soil (CS), bio plus compost (T 1 ), the combination of bio plus compost, and synthetic nutrient applied media/integrated media (T 2 ) and synthetic nutrient applied soil media (T 3 ) were assayed. Morphology parameters like plant height, canopy area, fresh weight, dry weight of roots were measured in each stage after eight weeks and sixteen weeks and yield attributing parameter as the number of fruits set per plant and number of fruits per plant were measured at the beginning and end of the reproductive stage eight and sixteen weeks respectively. The effects of growing media for the strawberry plant growth and productivity were analyzed using completely randomized block designs through analyzing the variance with a significance level of p < 0.05. The canopy area of the strawberry plants was calculated using the image processing technique applied in HSV colour space. Correspondingly, the vegetative stage and reproductive stage of T 2 plants attained the maximum plant height of 16.93 AE 0.31 cm and 19.34 AE 0.21 cm, canopy area with 23.02 AE 1.94 cm 2 and 28.78 AE 0.93 cm 2 , fresh weight of 18.00 AE 3.06 g, and 20.15 AE 3.49 g, dry weight of 5.15 AE 1.26 g and 6.66 AE 2.34 g and the number of fruits set per plant 18.83 AE 2.64 and number of fruits per plant 24.17 AE 2.14 followed by T 1 , T 3, and CS respectively. A comparison of the relative growth and fruit yield at the vegetative and reproductive phases of plants T 2 implied better performance. This study demonstrated that bio plus compost with synthetic nutrients act as a better source for the growth and production of strawberries under the greenhouse.

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Regression Analysis to Estimate Morphology Parameters of Pepper Plant in a Controlled Greenhouse System

Cited by 32 publications

References 34 publications

Assessment of the Influence of Environmental Variables on Pig’s Body Temperature using ANN and MLR Models

Assessment of the Influence of Environmental Variables on Pig’s Body Temperature using ANN and MLR Models

Estimation of Axle Torque for an Agricultural Tractor Using an Artificial Neural Network

Influence of different growing media on the growth and development of strawberry plants

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