Decoding the physiological response of plants to stress using deep learning for forecasting crop loss due to abiotic, biotic, and climatic variables.

This paper presents a simple method for detecting both biotic and abiotic stress in plants. Stress levels are measured based on the increase in nutrient uptake by plants as a mechanism of self-defense when under stress. A continuous electrical resistance measurement was used to estimate the rate of change of nutrients in agarose as the growth medium for Cicer arietinum (Chickpea) seeds. To determine the concentration of charge carriers in the growth medium, Drude's model was used. For identifying anomalies and forecasting plant stress, two experiments were conducted and outliers were found in electrical resistance and relative changes in carrier concentration. Anomaly in the first iteration was detected by applying k-Nearest Neighbour, One Class Support Vector Machine and Local Outlier Factor in unsupervised mode on electrical resistance data. In the second iteration, the neural network-based Long Short Term Memory method was used on the relative change in the carrier concentration data. As a result of the change in resistance of growth media during stress, nutrient concentrations shifted by 35%, as previously reported. Farmers who cater to small communities around them and are most affected by local and global stress factors can use this method of forecasting.