• Andalam Srihari
• V.N.S Manaswini
• U.Swetha
• V.Satheesh

- This main aim of this project is to apply Deep Learning to Establish a Predictive Model for Plant Growth and Output in Greenhouse Settings. In the realm of agriculture and greenhouse cultivation, it is imperative to be able to prognosticate the growth and yield of plants. This capability affords cultivators the advantage of making the necessary adjustments to the growing environment for increased production, balancing supply and demand, and minimizing expenses. The integration of Machine Learning (ML) and Deep Learning (DL) technology presents an innovative approach to this challenge. The focus of this investigation is to use ML and DL techniques to anticipate the yield and stem growth variability of two selected crops, namely tomatoes and Ficus benjamina, in environments specifically controlled for greenhouse agriculture. In this investigation, we advance a cutting-edge deep recurrent neural network (RNN) that utilizes the Long Short-Term Memory (LSTM) cell model for the prediction of growth. The network considers both the past records of yield, growth, and stem diameter, as well as the microclimate conditions to predict the target growth parameters. The efficiency of this approach is evaluated through comparison with other machine learning (ML) techniques such as Support Vector Regression and Random Forest Regression, using the Mean Square Error criterion. The findings of this study are obtained from data gathered from two greenhouses situated in Belgium and the United Kingdom as part of the EU Interreg SMARTGREEN project, which was conducted between 2017 and 2021, and present encouraging outcomes.

Forecasting Plant Development and Production in Greenhouse Settings with Deep Learning

"Forecasting Plant Development and Production in Greenhouse Settings with Deep Learning", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.10, Issue 3, page no.e485-e490, March-2023, Available :http://www.jetir.org/papers/JETIR2303460.pdf

"Forecasting Plant Development and Production in Greenhouse Settings with Deep Learning", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.10, Issue 3, page no. ppe485-e490, March-2023, Available at : http://www.jetir.org/papers/JETIR2303460.pdf