Disposable Stainless-Steel Wire-Based Electrochemical Microsensor for In Vivo Continuous Monitoring of Hydrogen Peroxide in Vein of Tomato Leaf

As one of the pivotal signal molecules, hydrogen peroxide (H2O2) has been demonstrated to play important roles in many physiological processes of plants. Continuous monitoring of H2O2 in vivo could help understand its regulation mechanism more clearly. In this study, a disposable electrochemical microsensor for H2O2 was developed. This microsensor consists of three parts: low-cost stainless-steel wire with a diameter of 0.1 mm modified by gold nanoparticles (disposable working electrode), an untreated platinum wire with a diameter of 0.1 mm (counter electrode), and an Ag/AgCl wire with a diameter of 0.1 mm (reference electrode), respectively. The microsensor could detect H2O2 in levels from 10 to 1000 µM and exhibited excellent selectivity. On this basis, the dynamic change in H2O2 in the vein of tomato leaf under high salinity was continuously monitored in vivo. The results showed that the production of H2O2 could be induced by high salinity within two hours. This study suggests that the disposable electrochemical microsensor not only suits continuously detecting H2O2 in microscopic plant tissue in vivo but also reduces the damage to plants. Overall, our strategy will help to pave the foundation for further investigation of the generation, transportation, and elimination mechanism of H2O2 in plants.

The diversity of Passalora fulva Isolates Collected from Tomato Plants in US high tunnels

High tunnels extend the growing season of high value crops, including tomatoes, but the environmental conditions within high tunnels favor the spread of the tomato leaf mold pathogen, Passalora fulva (syn. Cladosporium fulvum). Tomato leaf mold results in defoliation, and if severe, losses in yield. Despite substantial research, little is known regarding the genetic structure and diversity of populations of P. fulva associated with high tunnel tomato production in the United States. From 2016 to 2019, a total of 50 P. fulva isolates were collected from tomato leaf samples in high tunnels in the Northeast and Minnesota. Other Cladosporium species were also isolated from the leaf surfaces. Koch’s postulates were conducted to confirm that P. fulva was the cause of the disease symptoms observed. Race determination experiments revealed that the isolates belonged to either race 0 (six isolates) or race 2 (44 isolates). Polymorphisms were identified within four previously characterized effector genes Avr2, Avr4, Avr4e, and Avr9. The largest number of polymorphisms were observed for Avr2. Both mating type genes, MAT1-1-1 and MAT1-2-1, were present in the isolate collection. For further insights into the pathogen diversity, the 50 isolates were genotyped at 7,514 single-nucleotide polymorphism loci using genotyping-by-sequencing: differentiation by region but not by year was observed. Within the collection of 50 isolates, there were 18 distinct genotypes. Information regarding P. fulva population diversity will enable better management recommendations for growers, as high tunnel production of tomatoes expands.

CRISPR-Cas9-mediated mutagenesis of the SlSRM1-like gene leads to abnormal leaf development in tomatoes

Background Leaves, which are the most important organs of plants, can not only fix carbon sources through photosynthesis, but also absorb nutrients through transpiration. Leaf development directly determines the growth, flowering and fruiting of plants. There are many factors that affect leaf development, such as the growth environment, gene expression, and hormone synthesis. In this study, tomatoes were used to study the role of the transcription factor Solanum lycopersicum salt-related MYB1-like (SlSRM1-like) in the development of tomato leaves. Results Loss-of-function of the SlSRM1-like gene mediated by clustered, regularly interspaced, short palindromic repeat (CRISPR)/CRISPR-associated 9 (Cas9) resulted in abnormal tomato leaf morphology, including thinner leaves, wrinkled edges, raised veins, disordered edge veins, and left and right asymmetry. An analysis of the transcription levels of genes related to leaf development revealed that the expression of these genes was significantly altered in the SlSRM1-like mutants (SlSRM1-like-Ms). Moreover, the SlSRM1-like gene was expressed at higher transcription levels in young tissues than in old tissues, and its expression was also induced in response to auxin. In addition, the transcription levels of genes related to the auxin pathway, which regulates tomato growth and development, were severely affected in the SlSRM1-like-Ms. Therefore, it is hypothesized that the SlSRM1-like gene functions in the regulation of tomato leaf development through the auxin-related pathway. Conclusions In this study, we successfully knocked out the SlSRM1-like gene in the tomato variety Ailsa Craig using CRISPR technology and found that knockout of the SlSRM1-like gene resulted in abnormal development of tomato leaves. Further research indicated that SlSRM1-like regulated tomato leaf development through auxin-related pathways. The results provide an important reference for the functional study of other SRM1-like genes in plants and provide new insights into the regulation of leaf development in tomato and other plants.

Optimized Deep Learning Algorithms for Tomato Leaf Disease Detection with Hardware Deployment

Smart agriculture has taken more attention during the last decade due to the bio-hazards of climate change impacts, extreme weather events, population explosion, food security demands and natural resources shortage. The Egyptian government has taken initiative in dealing with plants diseases especially tomato which is one of the most important vegetable crops worldwide that are affected by many diseases causing high yield loss. Deep learning techniques have become the main focus in the direction of identifying tomato leaf diseases. This study evaluated different deep learning models pre-trained on ImageNet dataset such as ResNet50, InceptionV3, AlexNet, MobileNetV1, MobileNetV2 and MobileNetV3.To the best of our knowledge MobileNetV3 has not been tested on tomato leaf diseases. Each of the former deep learning models has been evaluated and optimized with different techniques. The evaluation shows that MobileNetV3 Small has achieved an accuracy of 98.99% while MobileNetV3 Large has achieved an accuracy of 99.81%. All models have been deployed on a workstation to evaluate their performance by calculating the prediction time on tomato leaf images. The models were also deployed on a Raspberry Pi 4 in order to build an Internet of Things (IoT) device capable of tomato leaf disease detection. MobileNetV3 Small had a latency of 66 ms and 251 ms on the workstation and the Raspberry Pi 4, respectively. On the other hand, MobileNetV3 Large had a latency of 50 ms on the workstation and 348 ms on the Raspberry Pi 4.

Tomato SlBES1.8 influences leaf morphogenesis by mediating gibberellin metabolism and signaling

Leaf morphogenetic activity determines its shape diversity. However, our knowledge to the regulatory mechanism in maintaining leaf morphogenetic capacity is still limited. In tomato, gibberellin (GA) negatively regulates leaf complexity by shortening the morphogenetic window. We here reported a tomato BRI1-EMS-SUPPRESSOR 1 (BES1) transcription factor, SlBES1.8, that promoted the simplification of leaf pattern in a similar manner as GA functions. Enhanced level of SlBES1.8 dramatically decreased the sensibility of tomato to GA whereas increased the sensibility to the GA biosynthesis inhibitor, PAC. In line with the phenotypic observation, the endogenous bioactive GA contents were increased in OE-SlBES1.8 lines, which certainly promoted the degradation of the GA signaling negative regulator, SlDELLA. Moreover, transcriptomic analysis uncovered a set of overlapping genomic targets of SlBES1.8 and GA, and most of them were regulated in the same way. Expression studies showed the repression of SlBES1.8 to the transcriptions of two GA deactivated genes, SlGA2ox2 and SlGA2ox6, and one GA receptor, SlGID1b-1. Further experiments confirmed the direct regulation of SlBES1.8 to their promoters. On the other hand, SlDELLA physically interacted with SlBES1.8 and further inhibited its transcriptional regulation activity by abolishing SlBES1.8-DNA binding. Conclusively, by mediating GA deactivation and signaling, SlBES1.8 greatly influenced tomato leaf morphogenesis.