Management of Antracnosis with Electrochemically Activated Salt Solutions (EASSs) on Bean Culture.

Common bean (Phaseolus vulgaris L.) is an important crop for food security and for national economics for several countries worldwide. One of the most important factors of risk in common bean production is the fungal disease anthracnose caused by Colletotrichum lindemuthianum, which, in some cases, causes complete yield losses; this kind of plant disease is usually managed through the application of chemical products such as fungicides that are commonly not accepted by society. This rejection is based on the relationship of pesticides with health damage and environmental contamination. In order to help in solving these drawbacks, the present work proposes the use of electrochemically activated salt solutions (EASSs) as a safer pathogen control agent in crops, due to it having shown an elicitor and biostimulant effect on plants. With this background, this manuscript presents in vitro results of the evaluation of the inhibitory effect for multiple bean pathogens and in vivo results of EASS in the common bean-Colletotrichum pathosystem by evaluation of the infection severity and defense activation, such as secondary metabolite production and antioxidant activity. EASS presence in growth media had a strong inhibitory effect at the beginning of experiments for some of the evaluated fungi. EASSs showed an effect against the development of the disease when applied in specific doses to prevent distress in plants.

Machine Learning for Plant Stress Modeling: A Perspective towards Hormesis Management.

Plant stress is one of the most significant factors affecting plant fitness and, consequently, food production. However, plant stress may also be profitable since it behaves hormetically; at low doses, it stimulates positive traits in crops, such as the synthesis of specialized metabolites and additional stress tolerance. The controlled exposure of crops to low doses of stressors is therefore called hormesis management, and it is a promising method to increase crop productivity and quality. Nevertheless, hormesis management has severe limitations derived from the complexity of plant physiological responses to stress. Many technological advances assist plant stress science in overcoming such limitations, which results in extensive datasets originating from the multiple layers of the plant defensive response. For that reason, artificial intelligence tools, particularly Machine Learning (ML) and Deep Learning (DL), have become crucial for processing and interpreting data to accurately model plant stress responses such as genomic variation, gene and protein expression, and metabolite biosynthesis. In this review, we discuss the most recent ML and DL applications in plant stress science, focusing on their potential for improving the development of hormesis management protocols.

Extracellular DNA: Insight of a Signal Molecule in Crop Protection.

Agricultural systems face several challenges in terms of meeting everyday-growing quantities and qualities of food requirements. However, the ecological and social trade-offs for increasing agricultural production are high, therefore, more sustainable agricultural practices are desired. Researchers are currently working on diverse sustainable techniques based mostly on natural mechanisms that plants have developed along with their evolution. Here, we discuss the potential agricultural application of extracellular DNA (eDNA), its multiple functioning mechanisms in plant metabolism, the importance of hormetic curves establishment, and as a challenge: the technical limitations of the industrial scale for this technology. We highlight the more viable natural mechanisms in which eDNA affects plant metabolism, acting as a damage/microbe-associated molecular pattern (DAMP, MAMP) or as a general plant biostimulant. Finally, we suggest a whole sustainable system, where DNA is extracted from organic sources by a simple methodology to fulfill the molecular characteristics needed to be applied in crop production systems, allowing the reduction in, or perhaps the total removal of, chemical pesticides, fertilizers, and insecticides application.

Effect of hydric stress-related acoustic emission on transcriptional and biochemical changes associated with a water deficit in Capsicum annuum L.

At specific vibration frequencies like ones generated by insects such as caterpillar chewing and bee's buzz-pollination turn on the plants secondary metabolism and their respective pathways gets activated. Thus, studies report that vibrations and sound waves applied to plants improves their fitness performance. Commonly, acoustic treatments for plants have used arbitrarily random frequencies. In this work, a group of signals obtained from hydric-stressed plants was recorded as vibrational patterns using a laser vibrometer. These vibration-signals were classified as representative of each condition and then externally applied as Acoustic Emission Patterns (AEP). The present research hypothesized that specific vibration frequencies could "emulate" a plant signal through mechanical energy based on tplant's ability to recognize vibration pattern similarity to a hydric status. This investigation aimed to apply the AEP's as characteristic vibrations classified as Low hydric stress (LHS), medium hydric stress (MHS), and high hydric stress (HHS) to evaluate their effect on healthy-well watered plants at two developmental stages. In the vegetative stage, the gene expression related to antioxidant and hydric stress responses was assessed. The LHS, MHS, and HHS acoustic treatments up-regulated the peroxidase (Pod) (~2.8, 1.9, and 3.6-fold change, respectively). The superoxide dismutase (Mn-sod) and phenylalanine ammonia-lyase (Pal) genes were up-regulated by HHS (~0.23 and ~0.55-fold change, respectively) and, the chalcone synthase (Chs) gene was induced by MHS (~0.63-fold-change). At the fructification stage, the MHS treatment induced a significant increase in Capsaicin content (5.88-fold change), probably through the at3and kas gene activation. Findings are correlated for a better understanding of plant responses to different multi frequency-signals tones from vibrations with potential for agricultural applications.

FPGA-based fused smart sensor for real-time plant-transpiration dynamic estimation.

Plant transpiration is considered one of the most important physiological functions because it constitutes the plants evolving adaptation to exchange moisture with a dry atmosphere which can dehydrate or eventually kill the plant. Due to the importance of transpiration, accurate measurement methods are required; therefore, a smart sensor that fuses five primary sensors is proposed which can measure air temperature, leaf temperature, air relative humidity, plant out relative humidity and ambient light. A field programmable gate array based unit is used to perform signal processing algorithms as average decimation and infinite impulse response filters to the primary sensor readings in order to reduce the signal noise and improve its quality. Once the primary sensor readings are filtered, transpiration dynamics such as: transpiration, stomatal conductance, leaf-air-temperature-difference and vapor pressure deficit are calculated in real time by the smart sensor. This permits the user to observe different primary and calculated measurements at the same time and the relationship between these which is very useful in precision agriculture in the detection of abnormal conditions. Finally, transpiration related stress conditions can be detected in real time because of the use of online processing and embedded communications capabilities.

Effects of hydric stress on vibrational frequency patterns of Capsicum annuum plants.

Plants that experience a lack of sufficient irrigation undergo hydric stress, which causes the modification of their mechanical properties. These changes include a complex network of chemical and physical signals that interact between plant-plant and plant-environment systems in a mechanism that is still not well understood, and that differs among species. This mechanical response implies different levels of vibration when the plant experiences structural modifications from self-hydraulic adjustments of flux exchange at specific frequencies, with these carrying behavioral information. To measure these signals, highly sensitive instrumentation that allows the decoding of displacement velocity and displacement of plants, which is possible through calibrated equipment such as 3D scanning laser vibrometers, is necessary. Laser vibrometry technology allows for noninvasive measurements in real-time. Physiological changes could reasonably affect the biomechanical condition of plants in terms of the frequency (hertz) and intensity of the plant's vibration. In this research, it is proposed that the frequency changes of a plant's vibration are related to the plant's hydric condition and that these frequency vibrations have the ecological potential to communicate water changes and levels of hydric stress. The peak of the velocity of plant displacements was found to vary from 0.079 to 1.74 mm/s, and natural frequencies (hertz) range is between 1.8 and 2.6 Hz for plants with low hydric stress (LHS), between 1.3 and 1.6 Hz for plants with medium hydric stress (MHS), and between 6.7 and 7.8 Hz for plants with high hydric stress. These values could act as preliminary references for water management using noninvasive techniques and, knowledge of the range of natural frequencies of hydric stress risk in chili pepper crops can be applied in precision agriculture practices.