Greenhouse Humidity Prediction and Control Models by Using Fuzzy Inference Systems | Chapter 06 | Research Developments in Science and Technology Vol. 6

 Greenhouse horticulture is a popular method for growing high-value crops with a large profit margin. To enable excellent management of environmental elements, fuzzy inference systems have been successfully used in prediction and control models. The goal of this research is to discover the many relationships in fuzzy inference systems now used for greenhouse modelling, prediction, and humidity management, as well as their change through time, in order to design more robust and understandable models. The major goal is to apply optimization techniques to find distinct linkages inside fuzzy inference systems, their configurations, and models, which are currently used for greenhouse forecast, control, and humidity modelling. The procedure is based on the PRISMA working guide. Four academic databases were combed through for a total of 93 questionnaires. Its bibliometric features have been retrieved and analysed, which helps the survey achieve its goal. Finally, it was discovered that combining Mamdani's fuzzy inference systems with optimization and fuzzy clustering methodologies, as well as tactics like model-based predictive control, assures great accuracy and interpretability.

Author(S) Details

Sebastian-Camilo Vanegas-Ayala
Faculty of Engineering, Universidad Distrital Francisco José de Caldas, Bogotá D.C. 11021-110231588, Colombia.

Julio Barón-Velandia
Faculty of Engineering, Universidad Distrital Francisco José de Caldas, Bogotá D.C. 11021-110231588, Colombia.

Daniel-David Leal-Lara
Faculty of Engineering, Universidad Distrital Francisco José de Caldas, Bogotá D.C. 11021-110231588, Colombia.

View Book:- https://stm.bookpi.org/RDST-V6/article/view/7012

Determination of Environmental Humidity and Temperature Prediction in Agriculture Using Mamdani Inference Systems| Chapter 1 | Innovations in Science and Technology Vol. 6

The findings of a humidity and temperature prediction model in the environment for agriculture, which utilised diffuse sets and optimised their parameters using heuristic approaches such as genetic algorithms and exact methods such as Quasi-Newton, are presented in this chapter. It has been discovered that non-specialized users may have difficulty comprehending system dynamics and varied behaviour over time. The purpose of this research is to create models for predicting temperature and humidity values in the environment that are both interpretable and accurate. Because fuzzy logic has a high interpretability rating, using it to present a solution has various advantages. Non-specialized users can have a better grasp of the system dynamics by categorising the obtained values for environment humidity and temperature as high, medium, or low. The humidity and temperature are forecasted using two optimization algorithms used to two separate diffuse sets. Mamdani fuzzy inference systems have been effectively employed for crop prediction, such as crop recommendation based on temperature, humidity, and rain or weather level, using microcontroller-based devices. A Mamdani fuzzy inference system optimised with the Quasi-Newton algorithm, which uses a set of initial values generated during a previous optimization phase using a genetic algorithm, is the best implementation.

Author(s) Details:

Julio Baron Velandia,
Faculty of Engineering, Universidad Distrital “Francisco José de Cáldas”, Colombia.


Jonathan Steven Capera Quintana,
Faculty of Engineering, Universidad Distrital “Francisco José de Cáldas”, Colombia.


Sebastian Camilo Vanegas Ayala,
Faculty of Engineering, Universidad Distrital “Francisco José de Cáldas”, Colombia.

Please see the link here: https://stm.bookpi.org/IST-V6/article/view/6030

Measurement and Estimation of Crop Coefficient of Plastic Greenhouse for Marigold (Tagetes erecta (L.)) under Drip Irrigation| Chapter 11 | New Perspectives in Agriculture and Crop Science Vol. 3

 Depending on the type of crop, stage of production, soil and climatic conditions, crop water consumption (ETc) varies from region to region. The water demand of the cultivated plants should be determined accurately and irrigation water should be applied according to plant requirements in order to obtain optimum yield and avoid unnecessary water usage. In this analysis, the growth stage specific crop coefficient (Kc) and pan coefficient (Kp) for greenhouse-grown marigold (Tagetes erecta (L.)) were calculated in field No.C3 of the Central farm at the Agricultural Engineering College and Research Institute, Kumulur, Trichy district. Since a Class A pan occupied a wide area, a reduced evaporative pan size (20 and 60 cm compared to a Class A pan) was used and a pan coefficient of 0.93 and 0.96 respectively was calculated. For the transformation of pan evaporation (Epan) to grass reference evapotranspiration (ETo), a pan coefficient (Kp) was used. The depleted moisture content was taken to estimate the crop coefficient for different growth phases based on the tensiometer readings. The results showed that the marigold crop coefficient (Kc) was 0.37 during the initial stage (Kcin), 0.8 during the mid-stage (Kcmid) and 0.47 (Kcfin) during the final stage. Such findings will be helpful in needing crop water and scheduling irrigation for similar conditions.

Author(s) Details

E. Sujitha
Institute of Agriculture, Tamil Nadu Agricultural University, Kulmulur, Trichy, India.

K. Shanmugasundaram

Department of Basic Engineering & Applied Sciences, Agriculture Engineering College and Research Institute, Tamil Nadu Agricultural University, Kumulur, Trichy, India.

View Book :- https://bp.bookpi.org/index.php/bpi/catalog/book/293