Sensor Array Optimization to Design and Develop an Electronic Nose System for the Detection of Water Stress in Khasi Mandarin Orange

Drought stress is one of the most significant abiotic stresses, adversely affecting the economy by tumbling or even eliminating agricultural productivity, development, and yield. Adverse effects of water scarcity can be reduced if certain precautionary actions could be taken in advance. Therefore, monitoring and early detection of the drought can be helpful for preparing a well-developed response plan. In response to the stresses, an intricate response system of the plants is involved which emits a range of Volatile Organic Compounds (VOCs) from different parts, such as flowers, leaves, roots and stems. These VOCs can be used as fingerprints for categorizing stressed and nonstressed plants. This paper addresses the optimization of an array of gas sensors used in an in-situ stress diagnosis system, and the data obtained after optimization have been used for the detection of induced stresses in plants by recording the VOCs emitted by the plants. The flow characteristics of the gas chamber were modeled using the Finite Element method before fabrication. The temperature modulation of the gas sensors used in the designed electronic nose system was accomplished. The optimization of the sensor array was performed using the radar plot and Wilks’ Lambda technique. The optimum operating temperatures for each gas sensor were selected using a radar plot. Furthermore, the number of the sensors in the sensor array was reduced by choosing the sensors having higher discriminant ability using the Wilks’ Lambda optimization technique. Twelve healthy Khasi Mandarin Orange saplings were considered for the investigation. Three different levels of water stresses are induced in the plants artificially for the experiments. The overall response and the optimized response of the developed electronic nose system are compared using Linear Discriminant Analysis (LDA) and bootstrap ensemble K-Nearest Neighbors (KNN) classifier. The Leaf Relative Water Content (LRWC) of the leaves is also measured concurrently to confirm the stress induction in the plants.