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Plastic mulch is used for agricultural production to improve crop performance and mediate local environmental concerns. Paper mulch is an alternative to plastic mulch, it is effective and more biodegradable. The performance of plastic and paper mulch materials in agricultural production varies according to climatic environments, production practices, and crops. The objective of this study was to investigate the performance of paper mulch compared with plastic mulch and bare soil for tomato (Solanum lycopersicum L.) field production in 2018 and 2021. Data on weed control, soil temperature, and tomato yield were recorded. Weed coverage and weeding time were reduced in paper and plastic mulch treatments compared to bare soil. Soil temperature in 2021 was lower in paper and plastic mulch treatments than in bare soil. Tomato yield was greater in plastic mulch than paper mulch and bare soil. Tomato yield for paper mulch treatments was not different from that of bare soil treatments. Paper mulch offered comparable weed control benefits as plastic mulch, and comparable tomato yield as bare soil. [ABSTRACT FROM AUTHOR]

The efficiency of classical Fenton (CF) and modified Fenton (MF) as well as photo‐Fenton processes in real wastewater treatment of pulp and paper (P&P) mill was investigated in this study. The chemical oxygen demand (COD) was chosen as the reference measurement for evaluating the treatment's efficiency. After determining the optimum parameters for each process, the effect of adding ultrasound (US) on improving treatment efficiency was examined. In addition, kinetic study and phytotoxicity analysis were conducted under optimum conditions for all processes. With pH 4, reaction time 50 min, 1.2 g/L Fe2+ and 8 g/L H2O2 dosages, the best removal efficiency (RE) of COD was determined to be 82.18% in CF process, and this rate rose to 90.1% when US was added. The best RE in MF process was 84.16% with the application of UV‐C, pH 4, reaction time 50 min, 1 g/L Fe0 and 8 g/L H2O2 doses, although it increased to 93.4% when US was applied. The greatest results in the seed germination test were achieved in US processes with 100% of germination percentage (GP) for spinach and tomato and 90% for cress. In the economic evaluation, when conducting the treatment without US, the estimated relative cost decreased in a 15 and 16%, for CF/UV‐C and CF processes respectively, whereas the CF process was 64% cheaper than the MF process in all applications. The US contributed to enhanced water treatment efficiency by having a significant synergistic impact on Fenton applications. Hence, the combination of photo‐Fenton and ultrasound to treat effluent from P&P mills proved to be an effective and promising technique. [ABSTRACT FROM AUTHOR]

• Cellulose-based hydrogels act as a fertilizer amendment for crop production. • Freeze-dried Hydrogels (FDH) enabled higher water use efficiency. • FDH with the 95% Available Water Content treatment produced the highest average crop yield of 0.88 kg plant−1. Increased water and nitrogen use efficiency enhances crop production and reduces environmental degradation. Cellulosic hydrogels derived from paper waste can retain and gradually release water and nitrogen for plant growth. This study assessed tomato response to water and NO 3 − − N in soils amended with hydrogels. The experiment was conducted in a greenhouse on a tomato crop, comprising of following treatments: freeze-dried hydrogels (FDH), oven-dried hydrogels (ODH), control (CO) (without hydrogels) as well as two irrigation treatments (95% and 75% available water content (AWC)). The results indicated that FDH- 95% AWC treatment produced the highest average crop yield of 0.88 kg plant−1, compared to the ODH (0.32 kg plant−1) and control treatments (0.40 kg plant−1). The hydrogel and AWC combinations did not significantly (p > 0.05) impact plant height and stem diameter, while these treatment combinations significantly enhanced crop yield, leaf area index and plant biomass (p < 0.05). FDH and ODH produced higher yields and saved 15 % and 20% of irrigation water (225mm), respectively, compared to the control treatment. There was a noticeably higher water use efficiency in the FDH-95 (3.9 kg m−1 plant−1) treatment compared to the ODH-95 (1.46 kg m−1 plant−1) and control (1.5 kg m−1 plant−1) treatments. The results that under FDH and ODH treatments, excess nitrate was stored in the soil vacuoles and was remobilized for uptake by the plant roots. Moreover, the re-swelling ratio of freeze-dried beads (415.62 ± 18.13%) was recognizably higher than that of oven-dried beads (224.16 ± 5.51%), as freeze-dried beads could re-swell 185.4% times more in water than the oven-dried beads. Therefore, this research demonstrates a clean technology whereby wastepaper can be recycled into biodegradable hydrogels to increase crop productivity. [Display omitted] [ABSTRACT FROM AUTHOR]

Fruit bagging protects fruits from pest infestation and multiple biotic and abiotic stresses during development without reducing yield. It also can potentially improve the quality and market value of fruits. Hence, the present study aimed to determine the effect of pre-harvest fruit bagging on the yield and quality of tomato (Solanum lycopersicum) cv. BARI Tomato-2. The experiment was laid out in a Randomized Complete Block Design (RCBD) with three replications that constituted the various treatments-T0: Control (no bagging), T1: White paper bag (single layer), T2: Brown paper bag (double layer), T3: Non-woven fabric bag, and T4: Jute bag from October 2021 to March 2022. The results showed that the non-woven fabric bag had the highest fruit length (57.79 mm), diameter (57.81 mm), number of fruits plant-1 (55.9), yield (4.06 kg plant-1), vitamin C (24.63mg/ 100g-1), and lycopene content (0.12 mg g-1). The maximum insect incidence (18.41%) was recorded in control and the minimum was counted in T3 (2.17%). The highest total soluble solid (6.73%0Brix) and dry matter (8.63%) were observed in a white paper bag. The highest mean color score (6.08), firmness (26.81%), and shelf life (13 days) found in fabric bags outperformed non-bagged fruits. The fabric bag showed maximum net income (21.12 TK) and the highest benefit: cost ratio (2.36). Thus, the fabric bag was the best for improving the physical and chemical qualities of tomatoes (Solanum lycopersicum) among the various fruitcovering materials. [ABSTRACT FROM AUTHOR]

Plant biomass in the form of waste materials and by-products from various industries can be a valuable material for the production of composts and growing media for urban gardening. In this study, pulp and paper mill sludge, fruit-vegetable waste, mushroom spent substrate and rye straw were used to produce compost that was further used as a medium component in container cultivation of tomato. The plants were grown in containers with a capacity of 3 dm3 filled with three types of compost-based growing media supplemented with high peat, fen peat, pine bark and wood fiber. The tomato plants grown in 100% peat substrate served as controls. The plants grown in the compost-enriched media had a higher leaf greening index and percentage of ripe fruit, and exhibited an increased content of total polyphenols and flavonoids, potassium, calcium, magnesium and copper in fruit as compared with the control. The tomatoes grown in a medium consisting of 25% compost, 30% high peat, 15% low peat, 20% pine bark and 10% wood fiber reached the highest fresh fruit weight, total polyphenol content and L-ascorbic acid levels. This study demonstrated that the compost produced from natural materials from various sources was a valuable potting medium supplement with positive effects on tomato yield and nutritional value. [ABSTRACT FROM AUTHOR]

Main conclusion: A simple, low cost and safe method using homemade filter paper-based 96-well spin plates and homemade solutions was developed for high throughput plant DNA extraction to use in molecular marker analysis. A low cost and safe method was developed for high throughput extraction of plant DNA for molecular marker analysis. In this method, we describe a simple way to prepare 96-well spin plate using filter paper, a plant material product for DNA binding. Such filter paper-based spin plate can combine with homemade non-toxic buffers for high throughput extraction of plant DNA. We confirmed that filter paper is an efficient solid-phase DNA binding material and comparable to silicon-based glass fiber filters adopted in commercial DNA extraction kits, and that plant DNA extracted by this method can be readily used as template for PCR. The efficacy of this method was also fully demonstrated by molecular marker analysis in segregating populations of tomato. Due to greatly reduced expense compared to commercial kits, this method is of great value for small labs with limited resources. [ABSTRACT FROM AUTHOR]

Highlights • Plastic film (PF) mulching increased both soil temperature (ST) and moisture (SM). • Biodegradable paper (BP) mulching reduced ST but increased SM. • Bio-based film (BB) mulching reduced both ST and SM. • BP mulching enhanced plant growth and increased fruit quality and yield. Abstract Plastic film (PF) is a common soil mulching material used worldwide in crop production. In recent years there is an increasing demand for reducing the use of PF because of potential environmental- and ecological-risks. Although biodegradable paper (BP) and bio-based film (BB) have been used as alternatives to PF, little information is available regarding their efficiency in summer crop production, which is often restricted by the combination of heat stress and drought stress. To meet this need, PF (silver-gray plastic film made from polyethylene), BP (biodegradable paper made from plant straw) and BB (bio-based film made from plant straw combined with decomposed coal) were used as soil mulching materials to investigate their influence on soil properties (temperature, moisture, pH, electricity conductivity (EC), nutrients and enzymes), plant growth (root morphology, the relative growth rate (RGR), plant biomass and the root/shoot ratio), fruit yield and fruit quality. The un-mulched soil served as control. The trial was conducted in a five-year-old commercial greenhouse located in Helan county, Ningxia, China, with the mean air temperatures of about 32 °C and 31 °C during the summer season in 2016 and 2017, respectively. In general, although PF mulching increased soil moisture, it increased soil temperature, pH and EC and decreased available P, resulting in the reduction of plant growth (root length and diameter, the RGRs of plant height and leaf area, and shoot and root biomass) and fruit yield. The BB mulching decreased soil temperature, however, it decreased soil moisture and invertase and increased soil EC, leading to reduced root growth (root diameter and biomass, and the root/shoot ratio). The BP mulching not only decreased soil temperature, but also increased soil moisture. As a result, it showed several advantages such as decreasing soil EC, increasing soil enzyme activities, improving plant growth (root length, the RGRs of plant height and leaf area, plant biomass and the root/shoot ratio) and increasing fruit yield. Moreover, the fruit quality was partly improved by BP mulching due to reduced nitrate but increased vitamin C. Our results infer that BP was a potential mulching material that could be used for summer tomato production. [ABSTRACT FROM AUTHOR]

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Purpose: Due to environmental concerns, efforts are made to replace the use of peat in horticultural growth media by organic wastes. Four growth media were prepared with the purpose of achieving adequate physical and chemical properties for plant production. Materials and methods: Growth media prepared from mixtures of coir (C) and paper sludge (P), respectively, with two biogas digestates from food waste (D1 and D2), were tested. These mixtures, 20% D1 or D2 + 80% C or P (v/v), were evaluated as growth media for tomato (Solanum lycopersicum L.) and lettuce (Lactuca sativa L.). Results and conclusion: The growth media were all physically stable during the growing period, provided all the macronutrients and most of the micronutrients necessary for plant growth, adequate pH conditions, as well as an adequate electrical conductivity. The mixture of D2 and P produced the highest biomass compared to a mineral fertilised peat (control), with a biomass production of 76% of the control for lettuce and 54% for tomato. Causes for the biomass reduction relative to the control may be related to ammonium toxicity effects, and/or limited plant-available water. The digestates, particularly D1, seemed also to have a phytotoxic effect on the germination. [ABSTRACT FROM AUTHOR]

Abstract: Detection of phytohormones in situ has gained significant attention due to their critical roles in regulating developmental processes and signaling for defenses in plants at low concentration. As one type of plant hormones, salicylic acid has recently been found to be one of pivotal signal molecules for physiological behaviors of plants. Here we report the application of paper-based electroanalytical devices for sensitively in situ detection of salicylic acid in tomato leaves with the sample volume of several microliters. Specifically, disposable working electrodes were fabricated by coating carbon tape with the mixture of multiwall carbon nanotubes and nafion. We observed that the treatment of the modified carbon tape electrodes with oxygen plasma could significantly improve electrochemical responses of salicylic acid. The tomato leaves had a punched hole of 1.5mm diameter to release salicylic acid with minor influence on continuous growth of tomatoes. By incorporating the tomato leaf with the paper-based analytical device, we were able to perform in situ determination of salicylic acid based on its electrocatalytic oxidation. Our experimental results demonstrated that the amounts of salicylic acid differed statistically in normal, phytoene desaturase (PDS) gene silent and diseased (infected by Botrytis cinerea) tomato leaves. By quantifying salicylic acid at the level of several nanograms in situ, the simple paper-based electroanalytical devices could potentially facilitate the study of defense mechanism of plants under biotic and abiotic stresses. This study might also provide a sensitive method with spatiotemporal resolution for mapping of chemicals released from living organisms. [Copyright &y& Elsevier]

[Display omitted] • Method for determination of pesticide residues in tomatoes was developed using PSI-MS. • PSI-MS has been successfully applied to pesticide quantification in tomato varieties. • The quantification method by PSI-MS offers an excellent LOD, LOQ and recovery values. • Application of PSI-MS combined with PCA to differentiate the metabolites from tomato varieties. • PSI-MS is a rapid and efficient technique for analyzing pesticides in foodstuffs. The determination of pesticide residues in tomatoes is usually performed by GC–MS and LC–MS, which provide high analytical performance but require several steps of sample preparation. Thus, this work employed a simpler and faster technique, the Paper Spray Ionization Mass Spectrometry (PSI-MS), combined with Principal Component Analysis (PCA) to differentiate the tomatoes varieties and for quantification of pesticides residues. It was determined a metabolic profiling by PSI-MS and the samples were grouped according to the variety using PCA. A quantitative method was developed for sensitive and accurate quantitative analysis of the fungicide cyazofamid by PSI(+)-MS using fruit peels of two varieties of tomatoes. The method showed a calibration curve with R2> 0.99, and low limits of detection (LOD) and quantification (LOQ) of 10 ng.mL−1 and 25 ng.mL−1, respectively. The intra-day and inter-day recoveries percentage were in a range of 93.87–109.32 % and 99.54–100.81 % for variety Sweet Heaven, respectively. Similar results were obtained for variety BS II0012 with a satisfactory average recovery ranged from 89 % to 108 %, within the standards established by the regulatory agency. The results obtained reinforce the potential of PSI-MS as a rapid and simple analytical tool for determination of pesticide residue in agricultural products. [ABSTRACT FROM AUTHOR]

• No mulching reduced soil temperature (ST), soil moisture (SM) and water use efficiency (WUE). • Biodegradable paper (BP) mulching reduced ST but increased SM and WUE. • Red and blue BP mulching improved tomato growth, fruit quality, and yield. Plastic film (PF) is the primary material applied for soil mulching, but it may also cause heat and drought stresses to crops in the summer season. In a previous study, we revealed that biodegradable paper (BP) was a potential mulching material for summer crop production, because it could not only decrease soil temperature, but also increase soil moisture during the summer season. However, it is still unclear whether different colored BPs have various efficiencies in improving soil environment, when used as soil mulching materials. Therefore, in this study, gray PF and different colors (red, dark, blue and yellow) of BPs were used as soil mulching materials to explore their influences on soil temperature and moisture, spectral and photosynthetic characteristics, plant growth, and fruit quality and yield of tomato in the summer season. The non-mulching (NM) served as control. In general, PF exhibited the highest illuminance and photosynthetic irradiance, and thereby led to the highest soil temperature. However, soil temperature decreased on average of 5.73% and 20.28% in BPs as compared to NM and PF, respectively. Soil moisture increased on average of 8.15%, 5.41%, and 10.98% in dark, red, and blue BPs, respectively, as compared to NM. The ratio of blue-violet to red-orange irradiance were 1:2, 2:3, 1:4, 1:3, and 1:1 in NM, PF, yellow BP, red BP, and blue BP, respectively. The red and blue BPs generally enhanced root growth, and thereby increased total fruit yield by 36.56% and 41.64% on average, respectively, as compared to PF. In addition, an overall improvement in fruit quality (vitamin C, soluble sugar, and sugar-acid ratio) was observed in red and blue BPs as compared to PF and other colored BPs. Our results demonstrated that red and blue BPs can be applied to improve soil environment and spectral characteristics, and thereby enhance fruit yield and quality, during the summer crop production. [ABSTRACT FROM AUTHOR]

• Paper-based analytical devices and microsampling for evaluation of fruit ripening. • ITO glass was used to fabricate disposable working electrodes in paper-based devices. • IAA and SA in tiny parts of tomato fruit could be differentiated during ripening. • Profiling of IAA and SA in immature and mature tomato fruit was visually compared. Fruit ripening is a progress involving many physiological changes regulated by various plant hormones such as indole-3-acetic acid (IAA) and salicylic acid (SA). The levels of these hormones in fruit with the spatial-temporal resolution can provide detailed information for the study and evaluation of the fruit ripening. Herein paper-based analytical devices and the microsampling technique were coupled for the analysis of IAA and SA in different locations of the tomato fruit during ripening. Because of its excellent electric conductivity, the ITO glass was used as the substrate of the disposable working electrodes and modified with conductive carbon cement. The ITO modified electrodes in the paper-based analytical devices were utilized for analysis of IAA and SA in tiny tomato samples obtained with the technique of microsampling. Our results showed that the amounts of IAA and SA in the locular tissue of tomato fruit decreased via time in the process of ripening. More importantly, the spatial profiling of IAA and SA in immature and mature tomatoes could be directly differentiated at the molecular level. This study suggested that low-cost paper-based electroanalytical devices could become an effective platform for rapid evaluation of the fruit ripening. [ABSTRACT FROM AUTHOR]

Target detection algorithms can greatly improve the efficiency of tomato leaf disease detection and play an important technical role in intelligent tomato cultivation. However, there are some challenges in the detection process, such as the diversity of complex backgrounds and the loss of leaf symmetry due to leaf shadowing, and existing disease detection methods have some disadvantages in terms of deteriorating generalization ability and insufficient accuracy. Aiming at the above issues, a target detection model for tomato leaf disease based on deep learning with a global attention mechanism, TDGA, is proposed in this paper. The main idea of TDGA includes three aspects. Firstly, TDGA adds a global attention mechanism (GAM) after up-sampling and down-sampling, as well as in the SPPF module, to improve the feature extraction ability of the target object, effectively reducing the interference of invalid targets. Secondly, TDGA uses a switchable atrous convolution (SAConv) in the C3 module to improve the model's ability to detect. Thirdly, TDGA adopts the efficient IoU loss (EIoU) instead of complete IoU loss (CIoU) to solve the ambiguous definition of aspect ratio and sample imbalance. In addition, the influences of different environmental factors such as single leaf, multiple leaves, and shadows on the performance of tomato disease detection are extensively experimented with and analyzed in this paper, which also verified the robustness of TDGA. The experimental results show that the average accuracy of TDGA reaches 91.40%, which is 2.93% higher than that of the original YOLOv5 network, which is higher than YOLOv5, YOLOv7, YOLOHC, YOLOv8, SSD, Faster R-CNN, RetinaNet and other target detection networks, so that TDGA can be utilized for the detection of tomato leaf disease more efficiently and accurately, even in complex environments. [ABSTRACT FROM AUTHOR]

Tomato (cv. Mongal) value chain practices of a farmers' cooperative as pilot model in Siem Reap, Cambodia, were assessed and improved through the introduction of postharvest technologies and best practices. The existing value chain (EVC) practices included harvesting tomatoes at the breaker stage, use of harvesting container with smooth surface (plastic pail), use of plastic crate in hauling harvested tomatoes to the farmers' house where packaging in ordinary plastic bags at 10 kg fruit per bag was done. No sorting and special storage were practiced except for overnight storage at ambient when harvesting was done in the afternoon. The packed fruit were then transported to the city market about 12 km from the farm or 30-45 minutes ride using a motorcycle-driven carrier (locally named 'TukTuk'). Marketing tomatoes usually took half day. Using the cooperative's simple packhouse which linked farm production and marketing, improved value chain (IVC) practices were introduced, including sorting to ensure more uniform quality and damage-free fruit, sanitizing with 0.01% calcinated calcium (non-chlorine sanitizer) by dipping fruit in the solution for 3 minutes, modified atmosphere packaging (MAP) using perforated 50 µm thick low-density polyethylene bag at 10 kg fruit per bag, and transporting and direct retailing in ice box using a dedicated motorcycle-driven carrier. In another set of trials, three-day storage simulating extended period of distribution and marketing was included using ambient condition in the EVC while in the IVC, three storage options were introduced: ice box (3 kg ice per box with about 25 kg fruit replenished every day); low-cost cold storage using the Coolbot chamber; or evaporative cooler (EC). Results revealed that without storage (direct marketing after harvest) total postharvest loss was about 14% in the EVC; this was remarkably reduced to 4% in the IVC. IVC fruit were also firmer, had higher soluble solids and much reduced microbial load than EVC fruit. No pesticide residue was detected in both EVC and IVC fruit. With the three-day storage, the three storage options in the IVC did not differ much in reducing postharvest loss to about 3-6% from 22% in the EVC. IVC fruit also ripened slowly resulting in higher firmness than EVC fruit. Other quality attributes were not affected. Vitamin C content was slightly higher in IVC fruit than in EVC fruit. From the results, there is potential for integrating postharvest management options in value chains to reduce postharvest loss and enhance quality of tomatoes. [ABSTRACT FROM AUTHOR]

Fruit recognition and location are the premises of robot automatic picking. YOLOv3 has been used to detect different fruits in complex environment. However, for the object with definite features, the complex network structure will increase the computing time and may cause overfitting. Therefore, this paper has carried out a lightweight design for the YOLOv3. This paper proposed an improved T-Net to detect tomato images. Firstly, the T-Net reduces the residual network layers. This paper changed the number of cycles in each group of the residual unit to 1, 2, 2, 1, and 1. Second, two feature layers with different scales are selected according to the features of tomatoes. Meanwhile, the convolutional layer at the neck has been reduced by two layers. Finally, the location and approximate diameter of the ripe tomato are obtained by combining the node information of the Intel D435i camera and T-Net in the Robot Operation System. T-Net obtains mean average precision (mAP) of 99.2%, F1-score of 98.9%, precision of 99.0%, and recall of 98.8% at a detection rate of 104.2 FPS. The proposed T-Net has outperformed the YOLOv3 with 0.4%, 0.1%, and 0.2% increase in precision, mAP, and F1-score. The detection speed of T-Net is 1.8 times faster than YOLOv3. The mean errors of the center coordinates and diameter of the tomato are 8.5 mm and 2.5 mm, respectively. This model provides a method for efficient real-time detection and location of tomatoes. [ABSTRACT FROM AUTHOR]

The paper entitled “An economic analysis of energy requirements and input costs for tomato production in Turkey” used energy input–output analysis in tomato (industrial type) production and compare it with production costs. The research also pursued to analyze the effect of farm size. As a result of that research, the authors claimed that the better benefit–cost ratio was found on the large farms and it was more successful in economic performance. However, there are some minor and major miscalculations that could, and perhaps should be added to the result section. [ABSTRACT FROM AUTHOR]

• Paper spray ionization mass spectrometry was used for determination of pesticides in tomato peels. • Acephate, chlorpyrifos, and cyazofamid were monitored during the pre-harvest intervals. • Samples were separated into stored (low temperature) and field groups. • Pesticide amounts decreased throughout the pre-harvest intervals. • Samples from field group demonstrated a higher decrease in the pesticide concentrations. A rapid analysis of acephate, chlorpyrifos, and cyazofamid in tomato peels during pre-harvest intervals using paper spray ionization mass spectrometry (PSI-MS) has been demonstrated. LODs of 0.01 ppm and LOQs of 0.03 ppm were achieved. Relative standard deviations were below 9%, and recoveries close to 100%. For pesticides monitoring, samples were separated into stored and field groups. Stored group contained fruits that were harvested and stored at low temperature during the pre-harvest interval, and the field group contained fruits that were on the agricultural field over the pre-harvest interval. The decrease in pesticide amounts was lower for the stored samples. At the end of the pre-harvest intervals, acephate, chlorpyrifos, and cyazofamid were determined at 0.86, 0.96, and 0.23 ppm in the stored group, and at 0.26, 0.37, and 0.09 ppm in the field group, respectively. This work demonstrates the utility of PSI–MS for performing rapid quality control of fruit harvesting. [ABSTRACT FROM AUTHOR]

Tomato leaf disease identification is difficult owing to the variety of diseases and complex causes, for which the method based on the convolutional neural network is effective. While it is challenging to capture key features or tends to lose a large number of features when extracting image features by applying this method, resulting in low accuracy of disease identification. Therefore, the ResNet50-DPA model is proposed to identify tomato leaf diseases in the paper. Firstly, an improved ResNet50 is included in the model, which replaces the first layer of convolution in the basic ResNet50 model with the cascaded atrous convolution, facilitating to obtaining of leaf features with different scales. Secondly, in the model, a dual-path attention (DPA) mechanism is proposed to search for key features, where the stochastic pooling is employed to eliminate the influence of non-maximum values, and two convolutions with one dimension are introduced to replace the MLP layer for effectively reducing the damage to leaf information. In addition, to quickly and accurately identify the type of leaf disease, the DPA module is incorporated into the residual module of the improved ResNet50 to obtain an enhanced tomato leaf feature map, which helps to reduce economic losses. Finally, the visualization results of Grad-CAM are presented to show that the ResNet50-DPA model proposed can identify diseases more accurately and improve the interpretability of the model, meeting the need for precise identification of tomato leaf diseases. [ABSTRACT FROM AUTHOR]

Pests have caused significant losses to agriculture, greatly increasing the detection of pests in the planting process and the cost of pest management in the early stages. At this time, advances in computer vision and deep learning for the detection of pests appearing in the crop open the door to the application of target detection algorithms that can greatly improve the efficiency of tomato pest detection and play an important technical role in the realization of the intelligent planting of tomatoes. However, in the natural environment, tomato leaf pests are small in size, large in similarity, and large in environmental variability, and this type of situation can lead to greater detection difficulty. Aiming at the above problems, a network target detection model based on deep learning, YOLONDD, is proposed in this paper. Designing a new loss function, NMIoU (Normalized Wasserstein Distance with Mean Pairwise Distance Intersection over Union), which improves the ability of anomaly processing, improves the model's ability to detect and identify objects of different scales, and improves the robustness to scale changes; Adding a Dynamic head (DyHead) with an attention mechanism will improve the detection ability of targets at different scales, reduce the number of computations and parameters, improve the accuracy of target detection, enhance the overall performance of the model, and accelerate the training process. Adding decoupled head to Head can effectively reduce the number of parameters and computational complexity and enhance the model's generalization ability and robustness. The experimental results show that the average accuracy of YOLONDD can reach 90.1%, which is 3.33% higher than the original YOLOv5 algorithm and is better than SSD, Faster R-CNN, YOLOv7, YOLOv8, RetinaNet, and other target detection networks, and it can be more efficiently and accurately utilized in tomato leaf pest detection. [ABSTRACT FROM AUTHOR]

Diseases of tomato leaves can seriously damage crop yield and financial rewards. The timely and accurate detection of tomato diseases is a major challenge in agriculture. Hence, the early and accurate diagnosis of tomato diseases is crucial. The emergence of deep learning has dramatically helped in plant disease detection. However, the accuracy of deep learning models largely depends on the quantity and quality of training data. To solve the inter-class imbalance problem and improve the generalization ability of the classification model, this paper proposes a cycle-consistent generative-adversarial-network-based Transformer model to generate diseased tomato leaf images for data augmentation. In addition, this paper uses a Transformer model and densely connected CNN architecture to extract multilevel local features. The Transformer module is utilized to capture global dependencies and contextual information accurately to expand the sensory field of the model. Experiments show that the proposed model achieved 99.45% accuracy on the PlantVillage dataset. The 2018 Artificial Intelligence Challenger dataset and the private dataset attained accuracies of 98.30% and 95.4%, and the proposed classification model achieved a higher accuracy and smaller model size compared to previous deep learning models. The classification model is generalizable and robust and can provide a stable theoretical framework for crop disease prevention and control. [ABSTRACT FROM AUTHOR]

The structured water is obtained by quantum technology, exclusively on the basis of natural elements, under the influence of electromagnetic field with magnitude of the waves in the order of 10-40. The aim of this paper is to present the results of research carried out in greenhouse conditions to assess the influence of irrigation with quantum structured water, as compared with irrigation with tap water, on biodiversity and structure of microbial communities in rhizosphere of tomato (Solanum lycopersicum L.) FLAVIOLA variety and to evaluate the biological quality of plants. The paper presents the total counts and species of bacteria and fungi estimated by dilution plate method, diversity index of Shannon (H), equitability and similarity index. The influence of structured water on plants hight, fresh biomass accumulated, biological quality and vitality are discussed comparatively with tap water using image-forming methods, namely biocrystallization, circular chromatography and capillary dynamolysis by the evaluation of structures formed consequently to the reaction of plant extracts with certain inorganic salts. [ABSTRACT FROM AUTHOR]

Tomato yield prediction plays an important role in agricultural production planning and management, market supply and demand balance, and agricultural risk management. To solve the problems of low accuracy and high uncertainty of tomato yield prediction methods in solar greenhouses, based on experimental data for water and fertilizer consumption by greenhouse tomatoes in different regions over many years, this paper investigated the prediction models of greenhouse tomato yields under three different soil fertility conditions (low, medium, and high). Under these three different soil fertility conditions, greenhouse tomato yields were predicted using the neural network prediction model (NN), the neural network prediction model based on particle swarm optimization (PSO–NN), the neural network prediction model based on an adaptive inertia weight particle swarm optimization algorithm (AIWPSO–NN), and the neural network prediction model based on the improved particle swarm optimization algorithm (IPSO–NN). The experimental results demonstrate that the evaluation indexes (mean square error, mean absolute error, and R2) of the IPSO–NN prediction model proposed in this paper were superior to the other three prediction models (i.e., NN prediction model, AIWPSO–NN prediction model, and IPSO–NN prediction model) under three different soil fertility conditions. Among them, compared with the NN prediction model, the MSE of the other three prediction models under high soil fertility decreased to 0.0082, 0.0041, and 0.0036; MAE decreased to 0.0759, 0.0511, and 0.0489; R2 decreased to 0.8641, 0.9323, and 0.9408. These results indicated that the IPSO–NN prediction model had a higher predictive ability for greenhouse tomato yields under three different soil fertility conditions. In view of the important role of tomato yield prediction in greenhouses, this technology may be beneficial to agricultural management and decision support. [ABSTRACT FROM AUTHOR]

This study presents the background and the evaluation of integrating eXtended Reality (XR) in the permanent exhibition at the Tomato Industrial Museum "D. Nomikos". This paper firstly provides the context of this study by outlining the methodological, technological, and museological approaches undertaken to integrate XR in a quest to enhance visitors' meaningful engagement with the industrial heritage-related exhibition. Storytelling and narration are key elements of the museum's museological design through audio tours and interactive AR-based visualization n of the machinery's role and function. This paper presents and discusses the design of the evaluation methodology employed in the context of pertinent methods and approaches used in related research. This study investigates the effectiveness of XR for audience engagement and the added value that such technologies offer to the user experience as well as possible improvements. The main method adopted for gathering quantitative data is the User Experience Questionnaire (UEQ), and for qualitative data, through semi-structured interviews. Moreover, the findings are analyzed, interpreted, and discussed. Based on a case study, this paper offers a broader discussion of the challenges and prospects connected to the quest of employing emerging technologies and assessing their impact on visitors' museum experience, reaching conclusions for future directions in the field. [ABSTRACT FROM AUTHOR]

Plant diseases have a significant impact on leaves, with each disease exhibiting specific spots characterized by unique colors and locations. Therefore, it is crucial to develop a method for detecting these diseases based on spot shape, color, and location within the leaves. While Convolutional Neural Networks (CNNs) have been widely used in deep learning applications, they suffer from limitations in capturing relative spatial and orientation relationships. This paper presents a computer vision methodology that utilizes an optimized capsule neural network (CapsNet) to detect and classify ten tomato leaf diseases using standard dataset images. To mitigate overfitting, data augmentation, and preprocessing techniques were employed during the training phase. CapsNet was chosen over CNNs due to its superior ability to capture spatial positioning within the image. The proposed CapsNet approach achieved an accuracy of 96.39% with minimal loss, relying on a 0.00001 Adam optimizer. By comparing the results with existing state-of-the-art approaches, the study demonstrates the effectiveness of CapsNet in accurately identifying and classifying tomato leaf diseases based on spot shape, color, and location. The findings highlight the potential of CapsNet as an alternative to CNNs for improving disease detection and classification in plant pathology research. Highlights: This paper is interested in tracking plant diseases such as leaf disease in Tomatoes, based on optimized Deep Learning Capsule Net. Early detection of the most common diseases based on leaf images can help us track and enhance food quality. Capsule Networks is an optimized alternative method more efficient than traditional CNN for detecting all leaf images and determining all locations. However, it tackles the shortage of conventional CNN. [ABSTRACT FROM AUTHOR]

The paper aims to analyse the functioning of territorial agri-food chains through an institutional lens. The approach tries to explore the influence of endogenous and exogenous factors on the capacity to respond to complex transition challenges. Our working hypothesis is that agri-food supply chains are embedded in the territory they belong to, economic performances and market competitiveness are strongly influenced by a combination of organisational capabilities and good governance solutions. These topics are developed by examining the innovative socioeconomic features of two of the largest world and European supply chains: the processing tomato supply chains of Northern Italy and Extremadura (Spain), both representing most of the processed tomato national production, governed by an overarching organisation gathering producers and processing firms on a parity basis, characterised by an innovative path developed to face the changing conditions of policies and markets. The paper shows how governance capabilities and their implications on the competitiveness and chain’s performance need to be explored by combining qualitative and quantitative analysis and indicators. [ABSTRACT FROM AUTHOR]

An efficient plant disease identification is prime important for Agricultural productivity. This paper proposes a method of automatic leaf extraction and classification with improved accuracy compared with existing techniques. Images acquired from natural fields are complex, with background information that needs to be accurately segmented to identify areas of interest. Grab cut is a semi-automatic approach to extract foreground objects, which might occasionally degrade an object's properties. This paper suggests a better Grab cut for automatically extracting leaves from real-field images. The enhanced dataset of 23,617 images with eleven tomato leaf classes are created using the plant village dataset and actual field images. This paper addresses a wide range of issues related to convolutional neural network optimization, including how the number of layers affects the results of leaf disease detection and the creation of tiny models for portable devices. Five models, VGG 16, MobileNet, and custom architecture with input sizes of 98 × 98, 160 × 160, and 256 × 256, have been optimized and evaluated through several trials. Training and testing involve varying the input size, the number of layers, the optimizer, the dropout, the batch normalization, and the transfer learning. This paper presents and analyzes the findings of thirty experiments on various architecture. The proposed modified sequential eleven-layer architecture achieves accuracy compared with MobileNet and a reduced model size of 3.9 MB for input size 160 and 4.5 MB for input size 98. [ABSTRACT FROM AUTHOR]

Greenhouse production of fruits and vegetables in developed countries is challenged by labour scarcity and high labour costs. Robots offer a good solution for sustainable and cost-effective production. Acquiring accurate spatial information about relevant plant parts is vital for successful robot operation. Robot perception in greenhouses is challenging due to variations in plant appearance, viewpoints, and illumination. This paper proposes a keypoint-detection-based method using data from an RGB-D camera to estimate the 3D pose of peduncle nodes, which provides essential information to harvest the tomato bunches. Specifically, this paper proposes a method that detects four anatomical landmarks in the colour image and then integrates 3D point-cloud information to determine the 3D pose. A comprehensive evaluation was conducted in a commercial greenhouse to gain insight into the performance of different parts of the method. The results showed: (1) high accuracy in object detection, achieving an Average Precision (AP) of AP@0.5=0.96 ; (2) an average Percentage of Detected Joints (PDJ) of the keypoints of PhDJ@0.2 = 94.31%; and (3) 3D pose estimation accuracy with mean absolute errors (MAE) of 11o and 10o for the relative upper and lower angles between the peduncle and main stem, respectively. Furthermore, the capability to handle variations in viewpoint was investigated, demonstrating the method was robust to view changes. However, canonical and higher views resulted in slightly higher performance compared to other views. Although tomato was selected as a use case, the proposed method has the potential to be applied to other greenhouse crops, such as pepper, after fine-tuning. • Accurate 3D pose-estimation of peduncle nodes in a commercial greenhouse. • Combination of colour image and 3D point cloud to estimate 3D pose. • Comprehensive evaluation covering object, keypoints, pose and, viewpoints aspects. • Robust to variations caused by view change. • Canonical and higher views are superior while lateral views should be avoided. [ABSTRACT FROM AUTHOR]

This document is an editorial from the journal "Viruses" discussing tobamoviruses, which are plant-infecting viruses with an ancient lineage dating back to the Cretaceous period. Tobamoviruses are known for their resilience and ability to remain viable for long periods of time. Some tobamoviruses pose a threat to crops, and efforts are being made to understand their transmission and develop biosecurity measures. The editorial highlights several research papers on topics such as the host range of cucumber green mottle mosaic virus, the transmission of tomato brown rugose fruit virus, and the effectiveness of biosecurity measures in Australia. Additionally, the editorial mentions a study on using tobamovirus particles for biomedical applications. The author concludes by acknowledging that more tobamoviruses will likely be discovered in the future, presenting both challenges and opportunities for scientists. [Extracted from the article]

Tomatoes are a major crop worldwide, and accurately classifying their maturity is important for many agricultural applications, such as harvesting, grading, and quality control. In this paper, the authors propose a novel method for tomato maturity classification using a convolutional transformer. The convolutional transformer is a hybrid architecture that combines the strengths of convolutional neural networks (CNNs) and transformers. Additionally, this study introduces a new tomato dataset named KUTomaData, explicitly designed to train deep-learning models for tomato segmentation and classification. KUTomaData is a compilation of images sourced from a greenhouse in the UAE, with approximately 700 images available for training and testing. The dataset is prepared under various lighting conditions and viewing perspectives and employs different mobile camera sensors, distinguishing it from existing datasets. The contributions of this paper are threefold: firstly, the authors propose a novel method for tomato maturity classification using a modular convolutional transformer. Secondly, the authors introduce a new tomato image dataset that contains images of tomatoes at different maturity levels. Lastly, the authors show that the convolutional transformer outperforms state-of-the-art methods for tomato maturity classification. The effectiveness of the proposed framework in handling cluttered and occluded tomato instances was evaluated using two additional public datasets, Laboro Tomato and Rob2Pheno Annotated Tomato, as benchmarks. The evaluation results across these three datasets demonstrate the exceptional performance of our proposed framework, surpassing the state-of-the-art by 58.14%, 65.42%, and 66.39% in terms of mean average precision scores for KUTomaData, Laboro Tomato, and Rob2Pheno Annotated Tomato, respectively. This work can potentially improve the efficiency and accuracy of tomato harvesting, grading, and quality control processes. [ABSTRACT FROM AUTHOR]

Post-harvest loss continues to be a significant problem in the food industry. Different packaging materials, designed to reduce fruit damage, are anticipated for various applications in the supply chain. Recently, stamped paper (SP) and expandable polystyrene (EPS) trays have been introduced as tomato retail packaging. Although the combination of paper trays and clear plastic are still not 100% biodegradable packaging, they are promising alternatives to the heavy utilisation of petrochemical-based polymers. This study investigated the effects of different packaging materials and storage conditions on the 'Nema-Netta' tomatoes' quality attributes. The treatments consisted of a stamped paper (SP) + polyvinyl chloride (PVC), expandable polystyrene (EPS) + polyvinyl chloride (PVC), stamped paper (SP)+ flow wrap, expandable polystyrene (EPS) + flow wrap, polypropylene (PP), and unpackaged tomatoes stored at cold and ambient conditions. Firmness, physiological weight loss (PWL), pH value, titratable acidity (TA) and total sugars were evaluated at seven-day intervals, over 28 days. Temperature and relative humidity at cold storage ranged between 8–12 °C, 78–80% RH and 22–26 °C, 68–72% RH at ambient storage conditions. The packaging and storage conditions significantly affected the PWL, firmness, pH, TA and total sugars. Samples in the EPS Tray combined with the PVC wrap at cold storage maintained the quality of the tomatoes better than the other packaging. The combination of packaging and cold storage created an ideal environment for maintaining the quality of tomatoes. The relative differences between EPS Tray + PVC (non-biodegradable) and SP Tray + PVC (biodegradable) were less than 5% in multiple tests. [ABSTRACT FROM AUTHOR]

The objective this paper is determine the highest performance of fresh mass and number of fruits in tomato crops using different nutrient solution. An absolute hybrid with a semi-determined growth was used. It was grown in a protected environment using a fertigated substrate inside plastic bags containing ten liters of solution. The experiment was completely randomized with four levels of fertilization and five replications. Two experiments were carried out in two cultivation cycles (spring 2018 and autumn 2019) by performing an analysis of variance and Scott & Knott test and estimating the parameters of nonlinear logistic model and its critical points for both variables in each treatment. The mean fruit mass per plant was 3.70 kg for the spring experiment and 3.80 kg for the autumn experiment. The mean number of fruits per plant was 10.50 and 10.70 fruits for spring and autumn, respectively. There are significant differences between the treatments KO46, KO45 and KO56 compared to KO69 for fruit mass in the autumn experiment. For the other variables and cultivation cycles, the treatments did not show statistical differences. The logistic growth model fitted the weight and number of tomato fruits according to days after transplanting the seedlings and evidenced production cycle data, highlighting the main differences between the nutrient solutions. The nutritional solutions KO46, KO45 and KO56 are recommended for growing Gaúcho tomatoes in substrate. The nutrient solution KO56 has the best performance because it has a higher K availability, meets the balance of loads and antagonism between nutrients, provides equal response of means mass and number of fruits, and has a lower N:K ratio and balance of K over Ca and Mg, thus favoring fruit production, precocity, and development. [ABSTRACT FROM AUTHOR]

Physiological disorders impact the yield and quality of marketable fruit in tomato. Puffy fruit caused by cavities inside the locule can be problematic for processing and fresh market quality. In this paper, we used a recombinant inbred line (RIL) and three derived processing tomato populations to map and validate quantitative trait loci (QTLs) for fruit puffiness across environments. Binary interval mapping was used for mapping the incidence of fruit puffiness, and non-parametric interval mapping and parametric composite interval mapping were used for mapping severity. Marker–trait regressions were carried out to validate putative QTLs in subsequent crosses. QTLs were detected on chromosome (Chr) 1, 2, and 4. Only the QTL on Chr 1 was validated in progeny from subsequent crosses. This QTL explained up to 22.5% of the variance in the percentage of puffy fruit, with a significant interaction between loci on Chr 2 and 4, increasing the percentage of puffy fruit by an additional 15%. The allele responsible for puffy fruit on Chr 1 was inherited from parent FG02-188 and was dominant towards increased incidence and severity. Marker-assisted selection (MAS) for the QTL on Chr 1 was as efficient as genomic selection (GS) in reducing the incidence and severity of puffy fruit, despite the potential contribution of other loci. [ABSTRACT FROM AUTHOR]

A 1 millisecond (1‐ms) vision system that guarantees high efficiency and timely response for tomato defect detection is essential for factory automation. Because of various defect appearances, recently many existing researches focus on CNN based defect detection, but few of them attempt to reach high processing speed to adapt to the factorial assembly line. This paper proposes a global to multi‐scale local based parallel architecture with hardwired CNN for tomato defect detection. This architecture breaks down image‐wise detection into pixel‐wise localization and block‐wise classification. The pixel‐wise localization utilizes tomato‐aware information as constraints for localization performance. The block‐wise classification uses a fully pipelined network structure to obtain the classification result for each block as the pixel stream moves through the network. The classification network has a six‐layer lightweight network structure with quantization for hardwired type implementation on FPGA. The experiment results show that the proposed architecture processes 1000 FPS images with 0.9476 ms/frame delay. And for detection performance, this architecture keeps f1−score$f_{1}-score$ at 80.18%, only 1.31% lower than ResNet50 based detection system. [ABSTRACT FROM AUTHOR]

Tomato (Solanum lycopersicum) is a widely consumed vegetable crop with significant economic and nutritional importance. This review paper discusses the recent advancements in gene-based approaches to enhance the quality of tomatoes, particularly focusing on firmness, shelf life, and adaptations to pre- and post-harvest stresses. Utilizing genetic engineering techniques, such as Clustered Regularly Interspaced Short Palindromic Repeats/CRISPR-associated proteins 9 (CRISPR/Cas9) and Transcription Activator-like Effector Nucleases (TALENs), researchers have made remarkable progress in developing tomatoes with improved traits that address key challenges faced during cultivation, storage, and transportation. We further highlighted the potential of genetic modifications in enhancing tomato firmness, thereby reducing post-harvest losses and improving consumer satisfaction. Furthermore, strategies to extend tomato shelf life through genetic interventions are discussed, emphasizing the importance of maintaining quality and freshness for sustainable food supply chains. Furthermore, the review delves into the ways in which gene-based adaptations can bolster tomatoes against environmental stresses, pests, and diseases, thereby enhancing crop resilience and ensuring stable yields. Emphasizing these crucial facets, this review highlights the essential contribution of genetic advancements in transforming tomato production, elevating quality standards, and promoting the sustainability of tomato cultivation practices. [ABSTRACT FROM AUTHOR]

Tomato is one of the most popular and most important food crops consumed globally. The quality and quantity of yield by tomato plants are affected by the impact made by various kinds of diseases. Therefore, it is essential to identify these diseases early so that it is possible to reduce the occurrences and effect of the diseases on tomato plants to improve the overall crop yield and to support the farmers. In the past, many research works have been carried out by applying the machine learning techniques to segment and classify the tomato leaf images. However, the existing machine learning-based classifiers are not able to detect the new types of diseases more accurately. On the other hand, deep learning-based classifierswith the support of swarm intelligence-based optimization techniques are able to enhance the classification accuracy, leading to the more effective and accurate detection of leaf diseases. This research paper proposes a new method for the accurate classification of tomato leaf diseases by harnessing the power of an ensemble model in a sample dataset of tomato plants, containing images pertaining to nine different types of leaf diseases. This research introduces an ensemble model with an exponential moving average function with temporal constraints and an enhanced weighted gradient optimizer that is integrated into fine-tuned Visual Geometry Group-16 (VGG-16) and Neural Architecture Search Network (NASNet) mobile training methods for providing improved learning and classification accuracy. The dataset used for the research consists of 10,000 tomato leaf images categorized into nine classes for training and validating the model and an additional 1,000 images reserved for testing the model. The results have been analyzed thoroughly and benchmarked with existing performance metrics, thus proving that the proposed approach gives better performance in terms of accuracy, loss, precision, recall, receiver operating characteristic curve, and F1-score with values of 98.7%, 4%, 97.9%, 98.6%, 99.97%, and 98.7%, respectively. [ABSTRACT FROM AUTHOR]

Tomato leaf disease control in the field of smart agriculture urgently requires attention and reinforcement. This paper proposes a method called LAFANet for image-text retrieval, which integrates image and text information for joint analysis of multimodal data, helping agricultural practitioners to provide more comprehensive and in-depth diagnostic evidence to ensure the quality and yield of tomatoes. First, we focus on six common tomato leaf disease images and text descriptions, creating a Tomato Leaf Disease Image-Text Retrieval Dataset (TLDITRD), introducing image-text retrieval into the field of tomato leaf disease retrieval. Then, utilizing ViT and BERT models, we extract detailed image features and sequences of textual features, incorporating contextual information from image-text pairs. To address errors in image-text retrieval caused by complex backgrounds, we propose Learnable Fusion Attention (LFA) to amplify the fusion of textual and image features, thereby extracting substantial semantic insights from both modalities. To delve further into the semantic connections across various modalities, we propose a False Negative Elimination-Adversarial Negative Selection (FNE-ANS) approach. This method aims to identify adversarial negative instances that specifically target false negatives within the triplet function, thereby imposing constraints on the model. To bolster the model's capacity for generalization and precision, we propose Adversarial Regularization (AR). This approach involves incorporating adversarial perturbations during model training, thereby fortifying its resilience and adaptability to slight variations in input data. Experimental results show that, compared with existing ultramodern models, LAFANet outperformed existing models on TLDITRD dataset, with top1, top5, and top10 reaching 83.3% and 90.0%, and top1, top5, and top10 reaching 80.3%, 93.7%, and 96.3%. LAFANet offers fresh technical backing and algorithmic insights for the retrieval of tomato leaf disease through image-text correlation. [ABSTRACT FROM AUTHOR]

Simple Summary: Fungi of the genus Trichoderma have a number of positive effects on plants that are manifested in the stimulation of plant growth and tolerance to abiotic and biotic stress factors. Our findings provide evidence for the swift response of tomato seeds to Trichoderma spp. exposure under in vitro conditions. The response was examined after 48 h of germination, before any physical contact was established between Trichoderma spp. and tomato seeds. Spectroscopic methods were used to reveal the nature of tomato seed germination stimulation by Trichoderma. Induction of the higher synthesis of phenolic compounds through plant specific responses within seed radicles was indicated. Observed differences between treatments were predominantly based on modifications in the pectin content in the middle lamella, as well as alterations in hemicelluloses and xyloglucan within the primary cell wall. Significant alterations in the composition of tomato seed radicles treated with Trichoderma spp., characterized by changes in total protein and a concurrent reduction in pectin and/or xyloglucan levels were observed. Applied spectroscopic methods are valuable tools to investigate the stimulation of seed germination by symbiotic microorganisms. Characterized by low cost and high measurement speed, they could be used in large-scale selection of the best genotype-strain combinations for better germination. Seed germination is a complex process that can be negatively affected by numerous stresses. Trichoderma spp. are known as effective biocontrol agents as well as plant growth and germination stimulators. However, understanding of the early interactions between seeds and Trichoderma spp. remains limited. In the present paper, Fourier-transform infrared spectroscopy (FTIR) and Raman spectroscopy were used to reveal the nature of tomato seed germination as stimulated by Trichoderma. A rapid response of tomato seeds to Trichoderma spp. was observed within 48 h on Murashige and Skoog medium (MS) substrate, preceding any physical contact. Raman analysis indicated that both Trichoderma species stimulated phenolic compound synthesis by triggering plant-specific responses in seed radicles. The impact of T. harzianum and T. brevicompactum on two tomato cultivars resulted in alterations to the middle lamella pectin, cellulose, and xyloglucan in the primary cell wall. The Raman spectra indicated increased xylan content in NA with T9 treatment as well as increased hemicelluloses in GZ with T4 treatment. Moreover, T4 treatment resulted in elevated conjugated aldehydes in lignin in GZ, whereas the trend was reversed in NA. Additionally, FTIR analysis revealed significant changes in total protein levels in Trichoderma spp.-treated tomato seed radicles, with simultaneous decreases in pectin and/or xyloglucan. Our results indicate that two complementary spectroscopic methods, FTIR and Raman spectroscopy, can give valuable information on rapid changes in the plant cell wall structure of tomato radicles during germination stimulated by Trichoderma spp. [ABSTRACT FROM AUTHOR]

When detecting tomato leaf diseases in natural environments, factors such as changes in lighting, occlusion, and the small size of leaf lesions pose challenges to detection accuracy. Therefore, this study proposes a tomato leaf disease detection method based on attention mechanisms and multi-scale feature fusion. Firstly, the Convolutional Block Attention Module (CBAM) is introduced into the backbone feature extraction network to enhance the ability to extract lesion features and suppress the effects of environmental interference. Secondly, shallow feature maps are introduced into the re-parameterized generalized feature pyramid network (RepGFPN), constructing a new multi-scale re-parameterized generalized feature fusion module (BiRepGFPN) to enhance feature fusion expression and improve the localization ability for small lesion features. Finally, the BiRepGFPN replaces the Path Aggregation Feature Pyramid Network (PAFPN) in the YOLOv6 model to achieve effective fusion of deep semantic and shallow spatial information. Experimental results indicate that, when evaluated on the publicly available PlantDoc dataset, the model's mean average precision (mAP) showed improvements of 7.7%, 11.8%, 3.4%, 5.7%, 4.3%, and 2.6% compared to YOLOX, YOLOv5, YOLOv6, YOLOv6-s, YOLOv7, and YOLOv8, respectively. When evaluated on the tomato leaf disease dataset, the model demonstrated a precision of 92.9%, a recall rate of 95.2%, an F1 score of 94.0%, and a mean average precision (mAP) of 93.8%, showing improvements of 2.3%, 4.0%, 3.1%, and 2.7% respectively compared to the baseline model. These results indicate that the proposed detection method possesses significant detection performance and generalization capabilities. [ABSTRACT FROM AUTHOR]