Determination of a Tomato Growth in a Plant Chamber using Neural Network

This paper presents the use of artificial neural network in determining the tomato growth in a plant chamber. The input parameter that was used, gathered, and analyzed were temperature, carbon dioxide, relative humidity in a period of 9 weeks. The data parameters were used in the development of the ANN model to determine the tomato plant growth. The prediction of plant growth will help in producing quality crops by identifying the desirable input parameters. A total of 2736 data sets were used 70% for training and 30% were split for validation and testing. Results shows that a forward feed neural network with 10 layers and hidden neuron gives the best result. The researchers were able to develop an ANN model that predict the tomato growth leafing stage in a plant chamber.