A recent study by Kumar, Saifi, and Soami Daya Krishnananda, published in 2023, developed a novel method for stress detection in plants. This ground-breaking approach relies on monitoring the changes in plant nutrient uptake, which tend to increase when the plants are under stress.
The research team conducted continuous electrical resistance measurements to estimate the rate of nutrient change in the growth medium, using agarose for chickpea seeds. This information was then utilized to determine the concentration of charge carriers in the growth medium using Drude’s model.
Two separate experiments were carried out to detect anomalies and predict stress in plants. First, the team looked for outliers in the electrical resistance data and relative changes in carrier concentration. The first anomaly was detected using the k-Nearest Neighbour, One Class Support Vector Machine, and Local Outlier Factor methods in unsupervised mode applied to the electrical resistance data.
In the second iteration of the experiment, the researchers applied a neural network-based Long Short Term Memory method to the relative change in the carrier concentration data. The findings suggest that the shift in the growth media’s resistance during plant stress can lead to a nutrient concentration change of approximately 35%, corroborating previous reports.
This cutting-edge approach offers significant potential for small-scale farmers, particularly those catering to local communities. These farmers are often most vulnerable to various local and global stress factors affecting plant health and yield. With this stress detection, they can predict plant stress more effectively, allowing for timely interventions to mitigate the adverse impacts on their crops.
The researchers’ work underlines the potential of technological advancements to address critical challenges in agriculture, ultimately helping safeguard crop yields, boost food security, and support the livelihoods of small-scale farmers. However, it’s crucial to note that broader field testing and validation are required to confirm the method’s practical applicability and effectiveness under real-world farming conditions.
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