Identifying General Stress in Commercial Tomatoes Based on Machine Learning Applied to Plant Electrophysiology

Detecting stress caused by nitrogen deficit using deep learning techniques applied on plant electrophysiological data

Plant electrophysiology carries a strong potential for assessing the health of a plant. Current literature for the classification of plant electrophysiology generally comprises classical methods based on signal features that portray a simplification of the raw data and introduce a high computational cost. The Deep Learning (DL) techniques automatically learn the classification targets from the input data, overcoming the need for precalculated features. However, they are scarcely explored for identifying plant stress on electrophysiological recordings. This study applies DL techniques to the raw electrophysiological data from 16 tomato plants growing in typical production conditions to detect the presence of stress caused by a nitrogen deficiency. The proposed approach predicts the stressed state with an accuracy of around 88%, which could be increased to over 96% using a combination of the obtained prediction confidences. It outperforms the current state-of-the-art with over 8% higher accuracy and a potential for a direct application in production conditions. Moreover, the proposed approach demonstrates the ability to detect the presence of stress at its early stage. Overall, the presented findings suggest new means to automatize and improve agricultural practices with the aim of sustainability.

Leaf Intracellular Water Transport Rate Based on Physiological Impedance: A Possible Role of Leaf Internal Retained Water in Photosynthesis and Growth of Tomatoes

Water consumed by photosynthesis and growth rather than transpiration accounts for only 1-3% of the water absorbed by roots. Leaf intracellular water transport rate (LIWTR) based on physiological impedance (Z) provides information on the transport traits of the leaf internal retained water, which helps determine the intracellular water status. Solanum lycopersicum plants were subjected to five different levels of relative soil water content (SWC R ) (e.g., 100, 90, 80, 70, and 60%) for 3 months. The leaf water potential (ΨL), Z, photosynthesis, growth, and water-use efficiency (WUE) were determined. A coupling model between gripping force and physiological impedance was established according to the Nernst equation, and the inherent LIWTR (LIWTR i ) was determined. The results showed that LIWTR i together with Ψ L altered the intracellular water status as water supply changed. When SWC R was 100, 90, and 80%, stomatal closure reduced the transpiration and decreased the water transport within leaves. Net photosynthetic rate (P N) was inhibited by the decreased stomatal conductance (g s ) or Ψ L , but constant transport of the intracellular water was conducive to plant growth or dry matter accumulation. Remarkably, increased LIWTR i helped to improve the delivery and WUE of the retained leaf internal water, which maintained P N and improved the WUE at 70% but could not keep the plant growth and yields at 70 and 60% due to the further decrease of water supply and Ψ L . The increased transport rate of leaf intracellular water helped plants efficiently use intracellular water and maintain growth or photosynthesis, therefore, adapting to the decreasing water supply. The results demonstrate that the importance of transport of the leaf intracellular water in plant responses to water deficit by using electrophysiological parameters. However, the LIWTR in this research is not directly linked to the regulation of photosynthesis and growth, and the establishment of the direct relationship between leaf internal retained water and photosynthesis and growth needs further research.