Publications

@article{sharma2023deep,

title = {A Deep Neural Network with Electronic Nose for Water Stress Prediction in Khasi Mandarin Orange Plants},

author = {Chayanika Sharma and Nairit Barkataki and Utpal Sarma},

url = {https://nairit.in/wp-content/uploads/2023_MST_Sharma_Deep.pdf},

doi = {10.1088/1361-6501/acf8e3},

issn = {1361-6501},

year  = {2023},

date = {2023-09-21},

urldate = {2023-09-01},

journal = {Measurement Science and Technology},

volume = {34},

number = {12},

pages = {125152},

publisher = {IOP Publishing},

abstract = {Water stress is a significant environmental factor that hampers plant productivity and leads to various physiological and biological changes in plants. These include modifications in stomatal conductance and distribution, alteration of leaf water potential & turgor loss, altered chlorophyll content, and reduced cell expansion and growth. Additionally, water stress induces changes in the emission of volatile organic compounds across different parts of the plants. This study presents the development of an electronic nose (E-nose) system integrated with a deep neural network (DNN) to detect the presence and levels of water stress induced in Khasi Mandarin Orange plants. The proposed approach offers an alternative to conventional analytical methods that demand expensive and complex laboratory facilities. The investigation employs the leaf relative water content (RWC) estimation, a conventional technique, to evaluate water stress induction in the leaves of 20 plants collected over a span of 9 days after stopping irrigation. Supervised pattern recognition algorithms are trained using the results of RWC measurement, categorising leaves into non-stressed or one of four stress levels based on their water content. The dataset used for training and optimising the DNN model consists of 27 940 samples. The performance of the DNN model is compared to traditional machine learning methods, including linear and radial basis function support vector machines, k-nearest neighbours, decision tree, and random forest. From the results, it is seen that the optimised DNN model achieves the highest accuracy of 97.59% in comparison to other methods. Furthermore, the model is validated on an unseen dataset, exhibiting an accuracy of 97.32%. The proposed model holds the potential to enhance agricultural practices by enabling the detection and classification of water stress in crops, thereby aiding in water management improvements and increased productivity.},

keywords = {artificial intelligence, Artificial neural network (ANN), classification, water stress},

pubstate = {published},

tppubtype = {article}

}