Greenhouse Tomato Research Articles

Multi-model fusion method for predicting CO2 concentration in greenhouse tomatoes

With the rapid development of greenhouse agriculture, accurate prediction of environmental parameters such as temperature, humidity, and carbon dioxide concentration is crucial for optimal crop growth. Traditional forecasting models struggle with the nonlinear and complex nature of greenhouse data, leading to challenges in model robustness. This study addresses these issues by proposing a multi-model fusion strategy for predicting CO2 concentration in greenhouse tomatoes. The proposed method integrates wavelet denoising (WT), variational mode decomposition (VMD), and long short-term memory networks (LSTM). This innovative nonlinear ensemble model effectively extracts key time series features and removes noise, while an introduced attention mechanism enhances the model’s focus on essential time steps, improving prediction accuracy. Experimental results demonstrate that the multi-model fusion approach significantly outperforms single models in terms of accuracy and stability, achieving mean absolute error (MAE) and root mean square error (RMSE) of 0.0117 and 0.0194, respectively. The proposed method offers significant advantages for CO2 prediction in greenhouse crops, providing a theoretical basis and technical support for optimizing and controlling greenhouse parameters. This contributes to the advancement of smart agriculture by offering an efficient environmental monitoring and prediction tool. Additionally, the study presents new ideas and technical solutions for addressing similar agricultural environment prediction challenges, optimizing greenhouse environment control strategies, and improving crop production efficiency.

First report of paprika as a natural host plant for tomato chlorosis virus in Korea.

Paprika (Capsicum annuum var. grossum (L.) Sendtn.), also called bell pepper or sweet pepper, is a valuable greenhouse crop that is mostly consumed as fresh fruit in Korea. In 2022, it was cultivated on 726 ha in Korea and 82,042 tons were produced (KOSIS, 2023). In April 2023, interveinal chlorosis and yellowing were observed on the lower and middle leaves of greenhouse paprika in Jinju and Haman, South Korea, and many whiteflies (Bemisia tabaci) were present. The disease incidence was 30-40% on five farms (approximately 4.5 ha) in the two areas. The typical symptoms and presence of whiteflies in greenhouses indicated potential infection by the whitefly-transmitted crinivirus, tomato chlorosis virus (ToCV). Six samples collected from symptomatic plants were examined by leaf dip using transmission electron microscopy (TEM) and revealed filamentous virus particles about 800 nm long. To confirm the TEM results, six symptomatic paprika leaf samples were subject to RT-PCR using the specific primers ToCV-M-4F and ToCV-M-4R to detect ToCV (Choi, 2023). All tested samples were positive for ToCV. To confirm the presence of other viruses and obtain the complete genome sequences, one of the six ToCV-positive samples (ToCV-PAP-JJ6) was subject to high-throughput sequencing (HTS). Total RNA was extracted from symptomatic leaves using the RNeasy Plant Mini Kit (QIAGEN, Germany) and a transcriptome library was generated using the TruSeq Stranded Total RNA LT Sample Prep Kit (Illumina, San Diego, CA, USA) according to standard protocols. HTS was performed on an Illumina NovaSeq 6000 system (Macrogen, Korea). De novo transcriptome assembly of the 85,668,854 reads with Trinity software (r20140717) yielded 817,303 contigs of 201 to 38,987 nucleotides (nt). BLASTn and BLASTx analysis of the contigs against the NCBI viral reference database showed that three large contigs were virus sequences, two of which were homologous to ToCV-RNA1 and -RNA2 and one to bell pepper alphaendornavirus (BPEV). The ToCV-RNA1 (genome 8,594 nt, 11,212 mapped reads, mean read coverage 196.2 times) and ToCV-RNA2 (genome 8,242 nt, 40,122 mapped reads, mean read coverage 734.9 times) contigs showed 100% coverage and 99% base pair matching (8,583/8,594 and 8,233/8,242, respectively) with known genome sequences of ToCV-RNA1 (MG813908) and ToCV-RNA2 (KP114534) isolated from tomato in Korea (Lee et al. 2018). The sequences were deposited in GenBank as isolates ToCV-PAP-JJ6 RNA1 (OR865222) and ToCV-PAP-JJ6 RNA2 (OR865223). The BPEV contig (genome 14,728 nt, 2,928,519 mapped reads, mean read coverage 15,012.4 times) was detected at very high read depth and RT-PCR for BPEV confirmed that all six samples were coinfected with ToCV. BPEV was recently reported in paprika and red pepper in Korea (Jo et al., 2022). The BPEV-infected pepper cultivars have been reported not to produce any symptoms (Escalante & Valverde 2019). ToCV was first reported in tomato greenhouses in Korea in 2013 and is a problematic virus in tomato crops, along with tomato yellow leaf curl virus (Jo et al. 2023; Lee et al. 2018). Due to the continuous presence of ToCV, whiteflies, and natural weed hosts (Kil et al. 2015) in tomato greenhouses, ToCV has spread to other crops, including paprika. To the best of our knowledge, this is the first report of ToCV infecting paprika in Korea. A nationwide study is needed to prevent the spread of ToCV, a new threat to paprika farms.

Barrier-free tomato fruit selection and location based on optimized semantic segmentation and obstacle perception algorithm.

With the advancement of computer vision technology, vision-based target perception has emerged as a predominant approach for harvesting robots to identify and locate fruits. However, little attention has been paid to the fact that fruits may be obscured by stems or other objects. In order to improve the vision detection ability of fruit harvesting robot, a fruit target selection and location approach considering obstacle perception was proposed. To enrich the dataset for tomato harvesting, synthetic data were generated by rendering a 3D simulated model of the tomato greenhouse environment, and automatically producing corresponding pixel-level semantic segmentation labels. An attention-based spatial-relationship feature extraction module (SFM) with lower computational complexity was designed to enhance the ability of semantic segmentation network DeepLab v3+ in accurately segmenting linear-structured obstructions such as stems and wires. An adaptive K-means clustering method was developed to distinguish individual instances of fruits. Furthermore, a barrier-free fruit selection algorithm that integrates information of obstacles and fruit instances was proposed to identify the closest and largest non-occluded fruit as the optimal picking target. The improved semantic segmentation network exhibited enhanced performance, achieving an accuracy of 96.75%. Notably, the Intersection-over-Union (IoU) of wire and stem classes was improved by 5.0% and 2.3%, respectively. Our target selection method demonstrated accurate identification of obstacle types (96.15%) and effectively excluding fruits obstructed by strongly resistant objects (86.67%). Compared to the fruit detection method without visual obstacle avoidance (Yolo v5), our approach exhibited an 18.9% increase in selection precision and a 1.3% reduction in location error. The improved semantic segmentation algorithm significantly increased the segmentation accuracy of linear-structured obstacles, and the obstacle perception algorithm effectively avoided occluded fruits. The proposed method demonstrated an appreciable ability in precisely selecting and locating barrier-free fruits within non-structural environments, especially avoiding fruits obscured by stems or wires. This approach provides a more reliable and practical solution for fruit selection and localization for harvesting robots, while also being applicable to other fruits and vegetables such as sweet peppers and kiwis.

An Extended Method Based on the Geometric Position of Salient Image Features: Solving the Dataset Imbalance Problem in Greenhouse Tomato Growing Scenarios

Machine vision has significant advantages in a wide range of agricultural applications; however, acquiring a large number of high-quality image resources is often challenging in actual agricultural production due to environmental and equipment conditions. Therefore, crop image augmentation techniques are particularly important in crop growth analysis. In this paper, greenhouse tomato plants were used as research subjects to collect images of their different fertility stages with flowers and fruits. Due to the different durations of each fertility period, there is a significant difference in the number of images collected. For this reason, this paper proposes a method for balanced amplification of significant feature information in images based on geometric position. Through the geometric position information of the target in the image, different segmentation strategies are used to process the image and supervised and unsupervised methods are applied to perform balanced augmentation of the image, which is combined with the YOLOv7 algorithm to verify the augmentation effect. In terms of the image dataset, the mixed image dataset (Mix) is supplemented with mobile phone images on top of in situ monitoring images, with precision increased from 70.33% to 82.81% and recall increased from 69.15% to 81.25%. In terms of image augmentation, after supervised balanced amplification, the detection accuracy is improved from 70.33% to 77.29%, which is suitable for supervised balanced amplification. For the mobile phone dataset (MP), after amplification, it was found that better results could be achieved without any amplification method. The detection accuracy of the mixed dataset with different data sources matching the appropriate amplification method increased slightly from 82.81% to 83.59%, and accurate detection could be achieved when the target was shaded by the plant, and in different environments and light conditions.

Optimal Water and Nitrogen Regimes Increased Fruit Yield and Water Use Efficiency by Improving Root Characteristics of Drip-Fertigated Greenhouse Tomato (Solanum lycopersicum L.)

The growth of root system directly affects the absorption and utilization of soil water and nitrogen, and understanding the responses of root characteristics to water and nitrogen regimes is thus crucial for optimizing water and nitrogen management. The root characteristics of each soil layer, i.e., root length, root surface area, and root volume, as well as fruit yield and water use efficiency of greenhouse tomato under drip fertigation in response to different irrigation levels and nitrogen rates were explored in northwest China. There were four irrigation levels, i.e., 50% ETC (W1), 75% ETC (W2), 100% ETC (W3), and 125% ETC (W4), where ETC is the crop evapotranspiration, and four nitrogen rates, i.e., 0 kg ha−1 (N1), 150 kg ha−1 (N2), 250 kg ha−1 (N3), and 350 kg ha−1 (N4). The results showed that reasonable irrigation and nitrogen regimes (W3N3) significantly increased fruit yield by 31.64% and root length, root surface area, and root volume by 45.03%, 61.24%, and 148.21% compare to W3N1, respectively. The promoting effect of increasing irrigation level on root characteristics increased with soil depth and had the greatest increases in root volume by 27.07%, 123.43%, and 211.47% for the 0–10, 10–20, and 20–30 cm soil layers, respectively. In addition, reducing irrigation level significantly increased the percentages of roots in the top soil by 29.71%, 26.77%, and 18.53% for root length, root surface area, and root volume, respectively. The reasonable nitrogen rate (N3) significantly increased fruit yield by 41.11%, water use efficiency by 34.42%, and root length, root surface area, and root volume by 40.42%, 41.44%, and 112.76%, respectively. The over-application of nitrogen (N4) reduced root characteristics of all soil layers, fruit yield, and water use efficiency. The promoting effect of increasing nitrogen rate on root length of each soil layer decreased with soil depth, by 71.01%, 48.96%, and 15.71% for 0–10, 10–20, and 20–30 cm soil layers, respectively. Irrigation level was the main factor dominating the root growth of each soil layer. The correlation analysis showed that fruit yield had significantly positive correlations with root characteristics in all soil layers, while water use efficiency had significantly positive correlations with the percentages of root length and root surface area in the 0–10 cm soil layer. In conclusion, rational water and nitrogen regimes achieved better fruit yield by promoting root growth of greenhouse tomato, and the water use efficiency of greenhouse tomato was improved by increasing the root percentage in the topsoil layer to alleviate the adverse effects under water stress conditions. This study reveals how irrigation volume and nitrogen application can enhance tomato yield and water use efficiency by regulating root characteristics and vertical root distribution, providing support for understanding the response of root systems to changes in soil water and nitrogen conditions.

Modified Surface Drip Irrigation and Hydraulic Barrier Impacts on Soil Moisture and Water Productivity for Tomatoes in a Greenhouse

Considerable amounts of irrigation water of vegetable crops grown in homogenous sandy soil profiles could be subjected to deep percolation water losses due to inappropriately designed surface or subsurface drip irrigation methods. This study aimed to investigate the combined influence of implementing clay soil layer in homogenous sandy soil profile of low-tech greenhouse ridges and using modified surface drip irrigation (M-DI) on soil moisture distribution and water productivity of tomatoes. In the greenhouse, a 7.5 cm thick clay soil layer was implemented 15 cm from the soil surface of each ridge as a hydraulic barrier. Three irrigation regimes (100%, 70% and 50% of ETo) were imposed with the M-DI on tomato plants and 100%ETo with surface drip irrigation (DI) as control. Regarding economic valuation, viability was preserved for the M-DI and DI methods. The outcome indicated that soil moisture spreads more horizontally than vertically on the sandy soil above the clay soil layer. The combined effect of the homogenous sandy soil profile amendment and full irrigation (100%ETo) with the M-DI irrigation method increased the tomato fruit yield by 64.5%. Furthermore, the combined influence enhanced water productivity by the M-DI to 54.7 kg/m3 compared to 32 kg/m3 by the DI. However, M-DI demonstrated dominance over DI regarding returns, yield, and profit. Economic-wise, the M-DI requires 50% less of the lateral pipelines needed by the DI in low-tech greenhouses. Adopting the M-DI with a hydraulic barrier can improve soil moisture, water productivity, yield, and returns for tomato crops in low-tech greenhouses under sandy soil conditions. Also, the M-DI with the hydraulic clay barriers was an economically viable investment compared to the DI without clay barriers for growing tomatoes in low-tech greenhouses.

Intelligent agricultural robotic detection system for greenhouse tomato leaf diseases using soft computing techniques and deep learning

The development of soft computing methods has had a significant influence on the subject of autonomous intelligent agriculture. This paper offers a system for autonomous greenhouse navigation that employs a fuzzy control algorithm and a deep learning-based disease classification model for tomato plants, identifying illnesses using photos of tomato leaves. The primary novelty in this study is the introduction of an upgraded Deep Convolutional Generative Adversarial Network (DCGAN) that creates augmented pictures of disease tomato leaves from original genuine samples, considerably enhancing the training dataset. To find the optimum training model, four deep learning networks (VGG19, Inception-v3, DenseNet-201, and ResNet-152) were carefully compared on a dataset of nine tomato leaf disease classes. These models have validation accuracy of 92.32%, 90.83%, 96.61%, and 97.07%, respectively, when using the original PlantVillage dataset. The system then uses an enhanced dataset with ResNet-152 network design to achieve a high accuracy of 99.69%, as compared to the original dataset with ResNet-152’s accuracy of 97.07%. This improvement indicates the use of the proposed DCGAN in improving the performance of the deep learning model for greenhouse plant monitoring and disease detection. Furthermore, the proposed approach may have a broader use in various agricultural scenarios, potentially altering the field of autonomous intelligent agriculture.

Microclimate monitoring in commercial tomato (Solanum Lycopersicum L.) greenhouse production and its effect on plant growth, yield and fruit quality

IntroductionHigh annual tomato yields are achieved using high-tech greenhouse production systems. Large greenhouses typically rely only on one central weather station per compartment to steer their internal climate, ignoring possible microclimate conditions within the greenhouse itself.MethodsIn this study, we analysed spatial variation in temperature and vapour pressure deficit in a commercial tomato greenhouse setting for three consecutive years. Multiple sensors were placed within the crop canopy, which revealed microclimate gradients.Results and discussionDifferent microclimates were present throughout the year, with seasonal (spring – summer – autumn) and diurnal (day – night) variations in temperature (up to 3 °C, daily average) and vapour pressure deficit (up to 0.6 kPa, daily average). The microclimate effects influenced in part the variation in plant and fruit growth rate and fruit yield – maximum recorded difference between two locations with different microclimates was 0.4 cm d-1 for stem growth rate, 0.6 g d-1 for fruit growth rate, 80 g for truss mass at harvest. The local microclimate effect on plant growth was always larger than the bulk climate variation measured by a central sensor, as commonly done in commercial greenhouses. Quality attributes of harvested tomato fruit did not show a significant difference between different microclimate conditions. In conclusion, we showed that even small, naturally occurring, differences in local environment conditions within a greenhouse may influence the rate of plant and fruit growth. These findings could encourage the sector to deploy larger sensor networks for optimal greenhouse climate control. A sensor grid covering the whole area of the greenhouse is a necessity for climate control strategies to mitigate suboptimal conditions.

Drip Irrigation of Tomatoes in Biskra: Water Saving and Economic Profitability

A field survey of thirty tomato growing greenhouses in Biskara region of Algeria was undertaken to find out the kind of the irrigation method being used and to assess their water productivity and economics. 30% of the greenhouses employed drip irrigation (9 farms), 20% (6 farms) utilized furrow irrigation, while the rest used the mixed irrigation. The sowing/transplantation of tomato in greenhouses in the region was carried out from August to October. The production started from March and ended in June. The selling price per kilogram of tomatoes ranged between 25 and 105 Algerian Dinar (DA). Results revealed that greenhouses using drip irrigation had higher water productivity and lower total water requirements. Greenhouses using drip irrigation produced 71.7 tons tomatoes ha-1, which is 33.7% higher than that using furrow irrigation. Drip irrigated greenhouses exhibited maximum water productivity of 20.4 kg m3. Total water consumption in the greenhouses using drip was 3722.2 m3 ha-1 while it was 10248 m3 ha-1 in the furrow irrigated. Average financial returns from the drip irrigated greenhouses were about 36% higher than farms using furrow irrigation.

Agronomic, Biochemical, and Physiological Responses to Magnesium (Mg) Application in Tomato (Solanum lycopersicum L.) under Greenhouse Conditions

Magnesium fertilization positively impacts various physiological processes in plants, leading to increased yield and fruit quality. In the present study, we determined the effects of the foliar application of magnesium on the yield, fruit quality, fruit biochemical content, and enzyme activity of tomato plants. The study was performed in a plastic greenhouse. The study evaluated magnesium sulfate application through drip irrigation at a single dose of 0.005 kg m2 and foliar application at rates of 0%, 0.25%, 0.5%, and 0.75%. As a consequence, the highest average fruit weight was obtained with the foliar application of 0.50% Mg. In terms of the fruit color’s a* value, the best results were again obtained with the application of 0.50% Mg. Mg applications proportionally increased the leaf chlorophyll content. Similarly, the highest leaf Mg content, the leaf relative water content, the fruits’ malic acid content, and the best fruit juice pH value were determined with the application of 0.75%. On the other hand, the application of 0.25% Mg produced the best results on the leaves’ K, Ca, and Na content, as well as in the fruits’ ascorbic acid content. Significant differences were not found in terms of the effects of the treatments on antioxidant activity, total protein amount, and enzyme activity. The application of 0.50% Mg contributed to the fruit color; 0.75% Mg application contributed to the chlorophyll, pH, and malic acid content; and the application of 0.25% Mg contributed to an increase in ascorbic acid. In conclusion, for greenhouse tomato production, a foliar application of 0.50% Mg is recommended to achieve larger, red-colored fruits. A 0.75% Mg application is suggested for increasing the chlorophyll content, lowering fruit juice pH, and enhancing malic acid content in fruits. A 0.25% Mg application is recommended to increase ascorbic acid content in fruits.

Conversion to Greenhouse Cultivation from Continuous Corn Production Decreases Soil Bacterial Diversity and Alters Community Structure

Changes in crop types and long-term monoculture substantially impact soil microbial communities. Exploring these changes and their influencing factors is of great significance for addressing the challenges posed by continuous cropping. Soil surface layer samples from greenhouse tomatoes fields cultivated for 5 (Y5), 9 (Y9), 13 years (Y13), and a surrounding corn field (CK) as a control were analyzed. The Y13 sample showed a significant increase in the relative abundance of Pseudomonadota (43.1%) and a decrease in Actinobacteria (50.3%) compared to the CK sample. Soil bacterial alpha diversity generally declined from the CK to Y13 (0.1–22.2%) sample, with a small peak in Y9 for Chao1 and Observed_species. Significant differences in Chao1 and Observed_ species were observed between the CK and Y13 samples. Beta diversity analysis revealed a pronounced variation in soil bacterial community structure across planting years, with the divergence from the CK sample intensifying over time. In comparison to the Y5 vs. CK samples, Y9 and Y13 exhibited marked differences from the CK across the same and broader metabolic pathways, suggesting a potential convergence of microbial activities over time. The Y9 and Y13 samples showed significantly higher biosynthesis abundance (7.50% and 6.36%, respectively) than the CK. In terms of soil physicochemical indices, the carbon–nitrogen ratio was the primary factor influencing soil bacterial composition. In conclusion, we found that crop alteration and continued planting changed the soil’s bacterial composition and increasing planting years suppressed the soil’s bacterial diversity, leading to a stable bacterial ecology after nine years. Implementing appropriate measures during this critical period is vital for optimal soil utilization.

Two Genotypes of Tomato Cultivated in Gobi Agriculture System Show a Varying Response to Deficit Drip Irrigation under Semi-Arid Conditions

Water-saving irrigation is of extraordinary importance for tomato production in semi-arid areas of northwest China. For this purpose, we conducted a two-season trial in a solar greenhouse of two tomato genotypes named ‘181’ and ‘Mao Fen 802’ and cultivated with substrate, under four irrigation regimes, i.e., well-watered (WW), low (LWD, 80% WW), moderate (MWD, 60% WW) and high (HWD, 40% WW) water deficit. The substrate water content of WW treatment was 75%θf to 90%θf (where θf is the field capacity). The study results showed that the single fruit weight and yield of tomato were significantly declined with an increasing water deficit degree. Compared to WW treatment, the fruit weight and yield were decreased about 34.45% and 20.35% for ‘181’ and ‘Mao Fen 802’ under HWD treatment, respectively. Conversely, water deficit treatment led to an obvious promotion of WUE and showed an upward trend as the water deficit level increased. In addition, compared to WW treatment, the water deficit significantly decreased the total flavonoids of the ‘181’ tomato by 24.4–93.1%, whereas there was no significant impact on that of ‘Mao Fen 802’. Nonetheless, different individual polyphenols were increased by suitable deficit irrigation for two tomato cultivars. Gallic acid, 3,4-dihydroxybenzoic acid, and naringin of ‘181’tomato were increased by 128.4–195.2%, 8.6–43.7%, and 31–73-fold, respectively, under water deficit compared to WW treatment. Further, under water deficit treatment, p-coumaric acid, benzoic acid, and 3,4-dihydroxybenzoic acid of ‘Mao Fen 802’ were increased by 36.2–49.2%, 59.1–189.7%, and 36.3–106.4% compared to WW treatment. As the main carotenoid component, the lycopene content of tomato fruit exhibited a significant rise of 7.84–20.02% and 20.55–32.13% for ‘181’ and ‘Mao Fen 802’ under three degrees of water deficit compared to WW treatment. Linear regression showed a significantly positive relationship between irrigation amounts and yield, and total polyphenols, whereas there was a significantly negative relationship between irrigation amounts and WUE, and total carotenoids. Based on correlation and PCA, WW and LWD, and MWD and HWD, were gathered together for ‘181’, while LWD, MWD, and HWD, were gathered, and only WW scattered for ‘Mao Fen 802’, along the PC1 direction. It was proposed that ‘Mao Fen 802’ was more sensitive to water deficit than the ‘181’ tomato. In conclusion, water deficit is conductive to water-saving cultivation of the greenhouse tomato and the tomato genotypes, and water deficit level is a key factor necessary for consideration.

Optimal Irrigation and Fertilization Enhanced Tomato Yield and Water and Nitrogen Productivities by Increasing Rhizosphere Microbial Nitrogen Fixation

Irrigation and nitrogen application rates have significant effects on greenhouse tomato yields, as well as water and nitrogen use efficiencies, but little is known regarding how these rates affect plant–microbiome interactions and how the associated changes might impact tomato yields. In this greenhouse study conducted over two years, the effects of three irrigation levels (moderate deficit with 65–75% water holding capacity threshold, slight deficit with 75–85%, and sufficient irrigation with 85–95%) and four nitrogen application levels (60, 120, 240, and 360 kg ha−1) on tomato growth, yield, water and nitrogen productivities, and rhizosphere microbial diversities and functions were investigated. The results demonstrated that the highest tomato leaf area, dry biomass, yield, and water and nitrogen productivities were obtained under the treatment with sufficient irrigation. With increasing nitrogen application, the tomato leaf area, dry biomass, yield, and water and nitrogen productivities showed a trend of first increasing and then decreasing. Overall, the treatment (N2W3) with sufficient irrigation and 240 kg ha−1 N was associated with the highest tomato growth, yield, and water and nitrogen productivities. Moreover, optimal irrigation and nitrogen application obviously altered the structures of rhizosphere bacterial and fungal communities, particularly recruiting microbiota conferring benefits to tomato growth and nitrogen fixation—namely, Lysobacter and Bradyrhizobium. Ultimately, optimal irrigation and nitrogen application significantly increased the relative abundances of functions related to carbon, sulfur, and nitrogen metabolism, especially nitrogen fixation. In summary, optimal irrigation and fertilization enhanced tomato yield, as well as water and nitrogen productivities by increasing the nitrogen fixation functions of the rhizosphere microbiome. Our results provide significant implications for tomato cultivation in greenhouses, in terms of optimized irrigation and fertilization.

Biostimulants Enhance the Nutritional Quality of Soilless Greenhouse Tomatoes.

The application of biostimulants in vegetable cultivation has emerged as a promising approach to enhance the nutritional quality of crops, particularly in controlled environment agriculture and soilless culture systems. In this study, we employed a rigorous methodology, applying various biostimulants amino acids, Plant Growth-Promoting Rhizobacteria (PGPR), fulvic acid, chitosan, and vermicompost along with mineral fertilizers, both foliar and via the roots, to soilless greenhouse tomatoes during spring cultivation. The experiment, conducted in a coir pith medium using the 'Samyeli F1' tomato cultivar, demonstrated that plants treated with biostimulants performed better than control plants. Notable variations in nutritional components were observed across treatments. PGPR had the best effects on the physical properties of the tomato fruit, showing the highest fruit weight, fruit length, equatorial diameter, fruit volume, fruit skin elasticity, and fruit flesh hardness while maintaining high color parameters L, a, and b. PGPR and fulvic acid demonstrated significant enhancements in total phenolics and flavonoids, suggesting potential boosts in antioxidant properties. Amioacid and vermicompost notably elevated total soluble solids, indicating potential fruit sweetness and overall taste improvements. On the other hand, vermicompost stood out for its ability to elevate total phenolics and flavonoids while enhancing vitamin C content, indicating a comprehensive enhancement of nutritional quality. In addition, vermicompost had the most significant impact on plant growth parameters and total yield, achieving a 43% increase over the control with a total yield of 10.39 kg/m2. These findings underline the specific nutritional benefits of different biostimulants, offering valuable insights for optimizing tomato cultivation practices to yield produce with enhanced health-promoting properties.

Occurrence and epidemiological consequences of Erysiphe neolycopersici on tomato plants in Mauritius

Leveillula taurica has until recently been believed to be the causal pathogen of powdery mildew of tomatoes in Mauritius. However, in the year 2022, another powdery mildew species was detected on tomatoes under culture in various tomato growing localities of the island. Based on morphological comparison and molecular analysis, its identity was confirmed as Erysiphe neolycopersici (anam. Pseudoidium neolycopersici). This species has a worldwide distribution causing severe epidemics mainly on greenhouse tomatoes. There is evidence of the rapid spread of this pathogen during 2022–2023 causing serious infection of all tomato cultivars in greenhouse culture in Mauritius. The origin of these infections and its epidemiological consequences are discussed.

Prediction of yield based on fruit set and dry matter production in greenhouse tomato

Prediction of yield based on fruit set and dry matter production in greenhouse tomato

Early diagnosis of Cladosporium fulvum in greenhouse tomato plants based on visible/near-infrared (VIS/NIR) and near-infrared (NIR) data fusion

Plant diseases can inflict varying degrees of damage on agricultural production. Therefore, identifying a rapid, non-destructive early diagnostic method is crucial for safeguarding plants. Cladosporium fulvum (C. fulvum) is one of the major diseases in tomato growth. This work presents a method of data fusion using two hyperspectral imaging systems of visible/near-infrared (VIS/NIR) and near-infrared (NIR) spectroscopy for the early diagnosis of C. fulvum in greenhouse tomatoes. First, hyperspectral images of samples at health and different times of infection were collected. The average spectral data of the image regions of interest were extracted and preprocessed for subsequent spectral datasets. Then different classification models were established for VIS/NIR and NIR data, optimized through various variable selection and data fusion methods. The principal component analysis-radial basis function neural network (PCA-RBF) model established using low-level data fusion achieved optimal results, achieving accuracies of 100% and 99.3% for calibration and prediction, respectively. Moreover, both the macro-averaged F1 (Macro-F1) values reached 1, and the geometric mean (G-mean) values reached 1 and 1, respectively. The results indicated that it was feasible to establish a PCA-RBF model by using the hyperspectral technique with low-level data fusion for the early detection of C. fulvum in greenhouse tomatoes.

Enhanced precision in greenhouse tomato recognition and localization

The core challenge in realizing automatic tomato harvesting in greenhouse environments lies in the precise identification and localization of the fruits. This paper introduces a comprehensive approach based on an improved YOLOv5 detection algorithm and optimized binocular stereo vision technology. Firstly, by introducing the C3-Transformer Encoder (CTM) structure and Bidirectional Feature Pyramid Network (Bi-FPN), this study enhanced the model’s ability to recognize tomatoes, especially under complex backgrounds and occlusion conditions. After field testing, the mAP50 accuracy reached 97.1%, an increase of 1.2 percentage points, enhancing detection precision. In addition, the ZED binocular camera was used, and the census stereo matching algorithm was optimized, significantly reducing disparity errors, thereby improving the accuracy of depth information. This allows the model to accurately calculate the three-dimensional spatial position of tomatoes obscured by branches and leaves, greatly improving the efficiency of the harvesting robot. Through field debugging verification with the harvesting robot, the method proposed in this study has shown high accuracy and reliability in the recognition and localization of tomatoes in complex greenhouse environments.

Sweet Immunity in Action: Unlocking Stem Reserves to Improve Yield and Quality. A Potential Key Role for Jasmonic Acid.

Common agronomic practices such as stem topping, side branch removal, and girdling can induce wound priming, mediated by jasmonic acid (JA). Low light conditions during greenhouse tomato production make the leaves more sensitive to the application of exogenous sugar, which is perceived as a "danger" in accordance with the concept of "Sweet Immunity". Consequently, source-sink balances are altered, leading to the remobilization of stem starch reserves and enabling the redirection of more carbon toward developing fruits, thereby increasing tomato yield and fruit quality. Similarities are drawn with the mobilization of fructans following defoliation of fodder grasses (wounding) and the remobilization of fructan and starch reserves under terminal drought and heat stress in wheat and rice (microwounding, cellular leakage). A central role for JA signaling is evident in all of these processes, closely intertwining with sugar signaling pathways. Therefore, JA signaling, associated with wounding and sugar priming events, offers numerous opportunities to alter source-sink balances across a broader spectrum of agricultural and horticultural crops, for instance, through the exogenous application of JA and fructans or a combination. This may entail reconfiguring and reversing phloem connections, potentially leading to an enhanced yield and product quality. Such processes may also disengage the growth-defense trade-off in plants.

Duration, not timing during the photoperiod, of far-red application determines the yield increase in tomato

Supplementary far-red light may increase fruit yield in greenhouse tomato cultivation. Apart from end-of-day applications, research has focussed on the effects of far-red light during the entire photoperiod, while few studies focused on the timing of far-red application. In this study, we assessed the impact of timing and duration of far-red application on fruit yield and quality in a high-wire tomato crop. Two commercial tomato cultivars were grown under 226 μmol m−2 s−1 of red and white supplementary LED lighting. Far-red light (63 μmol m−2 s−1) was additionally supplied during either the first or the second half of the photoperiod. These treatments were compared to a negative control, where no far-red light was supplied, and a positive control, where far-red light was supplied during the whole photoperiod. A yield component analysis was conducted to identify the parameters responsible for changes in fruit yield. We determined that fruit yield, total fruit dry weight and fruit production per unit of supplementary PAR light (400–700 nm) increased linearly with the duration of far-red application. Fruit yield was increased to a similar extent whether far-red was supplemented in the first or in the second half of the photoperiod. While different light treatments included 0, 14, or 28 % far-red in their cumulative supplementary photon flux density, there were no significant differences in their fruit production per unit of supplementary radiation (400–800 nm). Supplementary far-red increased fruit dry weight per unit of cultivation area by 23 % to 25 % as a result of increasing both plant dry weight (10 % to 12 %) and dry weight partitioning to the fruits (10 % to 14 %). The increase in fruit yield was less than the corresponding increase in total fruit dry weight due to a simultaneous increase in fruit dry matter content. Total soluble solids and pH of the fruits were hardly affected by far-red application. We concluded that supplementary far-red increased tomato fruit yield without compromising fruit quality, with this increase being determined by the duration of far-red application, not by its timing during the photoperiod.