Greenhouse Tomato Research Articles

Study on the optimal water−biochar management mode based on pan evaporation for solar greenhouse tomato (Solanum lycopersicum L.) in Northeast China

Biochar is commonly employed to enhance soil structure. However, the soil structure degradation due to multi-year continuous cropping poses a significant obstacle to the advancement of solar greenhouse agriculture. This study aimed to optimize the water−biochar management mode for seasonal solar greenhouse tomatoes (Solanum lycopersicum L.), which have been continuously cultivated over three years, with irrigation guided by the evaporation of a 20 cm evaporation pan (Ep-20). The experimental design employed a split–plot arrangement with two factors. The primary factor, irrigation amount, was tested at three levels: 0.6 Ep-20 (W1), 0.8 Ep-20 (W2), and 1.0 Ep-20 (W3). The secondary factor, biochar amount, was tested at three levels: 0 (B0), 20 t ha−1 (B1), and 40 t ha−1 (B2). The results revealed that biochar reduced soil bulk density and enhanced field capacity, tomato plant height, and photosynthesis. Increased irrigation improved tomato appearance quality but reduced nutritional quality, whereas higher biochar application improved both aspects. Principal component analysis (PCA) and grey relational analysis (GRA) results identified W2B2 as the optimal mode for the quality of spring tomato and autumn tomato. Compared to the control (W3B0), W2B2 exhibited an average increase of 18.0 % in yield and 47.5 % in irrigation water productivity (WPI) with a 20 % reduction in irrigation amount across two seasons. The technique for order preference by similarity to ideal solution (TOPSIS) comprehensive evaluation determined that the W2B2 mode maximized comprehensive benefits, enhancing both the overall quality of tomato and achieving higher yield and WPI. This study utilized the Ep-20 to characterize the reference crop evapotranspiration (ETo) and the crop evapotranspiration (ETc). The investigation demonstrated the feasibility of utilizing Ep-20 to guide irrigation under biochar application conditions and develop ETc estimation models for water−biochar management modes to optimize solar greenhouse tomato production.

Tomato Recognition Method Based on the YOLOv8-Tomato Model in Complex Greenhouse Environments

Tomatoes are a critical economic crop. The realization of tomato harvesting automation is of great significance in solving the labor shortage and improving the efficiency of the current harvesting operation. Accurate recognition of fruits is the key to realizing automated harvesting. Harvesting fruit at optimum ripeness ensures the highest nutrient content, flavor and market value levels, thus maximizing economic benefits. Owing to foliage and non-target fruits obstructing target fruits, as well as the alteration in color due to light, there is currently a low recognition rate and missed detection. We take the greenhouse tomato as the object of research. This paper proposes a tomato recognition model based on the improved YOLOv8 architecture to adapt to detecting tomato fruits in complex situations. First, to improve the model’s sensitivity to local features, we introduced an LSKA (Large Separable Kernel Attention) attention mechanism to aggregate feature information from different locations for better feature extraction. Secondly, to provide a higher quality upsampling effect, the ultra-lightweight and efficient dynamic upsampler Dysample (an ultra-lightweight and efficient dynamic upsampler) replaced the traditional nearest neighbor interpolation methods, which improves the overall performance of YOLOv8. Subsequently, the Inner-IoU function replaced the original CIoU loss function to hasten bounding box regression and raise model detection performance. Finally, the model test comparison was conducted on the self-built dataset, and the test results show that the mAP0.5 of the YOLOv8-Tomato model reached 99.4% and the recall rate reached 99.0%, which exceeds the original YOLOv8 model detection effect. Compared with faster R-CNN, SSD, YOLOv3-tiny, YOLOv5, and YOLOv8 models, the average accuracy is 7.5%, 11.6%, 8.6%, 3.3%, and 0.6% higher, respectively. This study demonstrates the model’s capacity to efficiently and accurately recognize tomatoes in unstructured growing environments, providing a technical reference for automated tomato harvesting.

Evaluation the impact of silicon nanoparticle on growth and water use efficiency of greenhouse tomato in drought stress condition

This study focused on the effect of silicon nanoparticles (Si-Nps) use on growth and water use efficiency (WUE) of tomato in hydroponic cultivation under drought stress. Experimental treatments included full irrigation, supplying 85 and 70% of crop water requirement (I100, I85 and I70) and use of Si-Nps in three levels of 0, 50 and 100 ppm (N0, N50 and N100) which was performed in a completely randomized design with three replications. Si-Nps were applied in two ways: leaf feeding (L) and root feeding (R). Data analysis showed that different levels of irrigation, Si-Nps and the interaction effect of theirs had a significant effect on fruit weight, leaf fresh and dry weight, stem fresh weight and WUE at 1% level. Si-Nps had a significant effect on stem dry weight at 1% and fruit sugar at 5%. The interaction effect of irrigation and Si-Nps had a significant effect on stem dry weight and fruit sugar at 1%. The maximum fresh fruit weight was related to treatment I85LN100 and compared to the control treatment, it was 7.9% more. The maximum WUE was observed in I70RN50 treatment, which was 56.3% higher than control treatment. Generally, applying irrigation I70RN50 gives the best result for hydroponic tomato cultivation in greenhouse conditions.

Enhancing the effects of solarization-based approaches to suppress Verticillium dahliae inocula affecting tomato in greenhouse

The main concerns regarding the management of soilborne pathogens of vegetable crops through fumigants are due to their detrimental effects on the human health and environment. Due to global restrictions, soil solarization alone or combined with low-impact measures are being re-evaluated as eco-friendly alternatives to replacement of phasing-out or banned fumigants. Three experiments were performed to compare the performance of traditional and innovative films in increasing thermal performance under different partially opened or kept closed greenhouses. The effects of treatment alone or combined with Brassica pellet (Biofence®) incorporation were tested in suppressing microsclerotia of Verticillium dahliae, causing wilt of tomato and vegetable crops. Although inducing different thermal regimes at depths 15 and 30-cm depths, all films were effective in reducing microsclerotia in soil compared to the bare plots under different greenhouse conditions. The performances achieved in closed greenhouse were better than those achieved in greenhouses with the side openings kept open. In these latter conditions, green-colored ethylene-vinyl-acetate (EVA) and uncolored EVA film well performed when combined with Brassica pellet, achieving better results than those obtained in the plots mulched with film or amended alone, respectively. Smoky gray low-density polyethylene (LDPE), followed by uncolored EVA and polyamide virtually impermeable film (PA-VIF) were the most effective in reducing/suppressing V. dahliae microsclerotia within 3-to-8 weeks of treatment in closed greenhouse whereas green EVA showed lesser performance. PA-VIF and EVA green film are attractive since they may be used in combination with biofumigants or left on as mulching for weed control of crops. This paper indicates that sustainable solarization-based approaches with different films alone or integrated with Brassica incorporation can also be successfully pursued against thermo-tolerant pathogens as V. dahliae. Moreover, our findings preliminarily show that the choice of film and treatment duration should be modulated based on the greenhouse operative conditions.

3MSP2: Sequential picking planning for multi-fruit congregated tomato harvesting in multi-clusters environment based on multi-views

Fast and effective picking planning from candidate fruits is important to improve the efficiency and success rate of robotic selective harvesting. However, it is a challenge for tomato picking decisions to simultaneously meet low- damage and high-performance requirements in multi-fruits congregation and multi-clusters environments. For such robotic application scenario, a novel sequential picking planning method is proposed for tomatoes harvesting sequence and picking posture estimation combined eye-on-shoulder and eye-in-hand views. First, the detection and localization of multiple tomatoes in both views are achieved by Yolov5. Then, a cluster Optimal Sequence Planning method was developed by us based on the view of eye-on-shoulder. The method combined the density clustering idea and the shortest movement path to automatically classify multiple neighboring tomatoes into candidate picking clusters and make decisions on the optimal picking order of the clusters. Finally, we developed a tomato Optimal Sequential Grasping method based on the view of eye-in-hand. The method combines collision-free constraints and a shortest time objective to determine the optimal grasping posture estimate and the optimal post-grasping observation position estimate for the target tomato. In indoor picking experiments, the proposed method improves the collision-free picking success rate of picking by 17.2 % and 36.1 %, and the picking efficiency by 25 % and 41.4 %, respectively, compared with the picking planning methods based on random traversal and scanning traversal. In the picking experiments in industrial tomato greenhouse, the collision-free picking success rate reaches 70.9 %, and the average cycle time for each picking task from target-recognizing to success-picking is 8.6 s. This approach offers a promising solution for picking planning of harvesting robot to lower damages and improve success rate, efficiency in the congregated fruit environment.

First report of bacterial wilt caused by Clavibacter michiganensis subsp. michiganensis affecting tomato in Iğdır

Tomato wilt disease caused by Clavibacter mihiganensis subsp. michiganensis (Cmm) is one of the most destructive tomato diseases and causes significant crop loss in both greenhouse and field tomato production areas worldwide. In this study, the presence of the causal agent of bacterial wilt disease in tomato plants was investigated in Aras Valley. Isolation was made from diseased plant samples and it was determined whether the strains were pathogenic by cellulase activity and HR test. The virulence, morphological and biochemical characteristics of the strains were determined. Strains that fatty acid methyl ester extraction, isolation and purification were performed were identified at species and subspecies level with % similarity index using gas chromatography system. The diagnosis was confirmed with the Biolog Gen III System and all strains were identified at the subspecies level with a % similarity index. As a result of this study, 57 strains were obtained in the isolation, and 39 of the strains were determined not to be pathogenic. Strain 18 was determined as the pathogen causing the most damage to tomato plants with 100 % disease severity. Strains were identified as Cmm at subspecies level with a similarity index of 71-87 % using gas chromatography system and 54-75 % similarity index with Biolog Gen III System. According to the heat map created, it was determined that the strains consisted of two main clusters. The presence of pathogen in Aras Valley was proved for the first time by this study.

Peer review of the pesticide risk assessment of the active substance Phthorimaea operculella granulovirus.

The conclusions of the European Food Safety Authority (EFSA) following the peer review of the initial risk assessments carried out by the competent authority of the rapporteur Member State, The Netherlands for the pesticide active substance Phthorimaea operculella granulovirus are reported. The context of the peer review was that required by Regulation (EC) No 1107/2009 of the European Parliament and of the Council. The conclusions were reached on the basis of the evaluation of the representative uses of Phthorimaea operculella granulovirus as an insecticide on tomato (field and greenhouse uses) and on potato (field use) via spraying (tractor drawn or knapsack sprayers) or overhead irrigation. The reliable endpoints, appropriate for use in regulatory risk assessment are presented. Missing information identified as being required by the regulatory framework is listed.

Populations of Bemisia tabaci Mediterranean in São Paulo state are inefficient vectors of Brazilian begomoviruses

AbstractBegomoviruses cause losses in several crops around the world. Whiteflies of the Bemisia tabaci cryptic species MEAM1 and MED are efficient vectors of begomoviruses. MEAM1 has been the predominant species in Brazil since the mid‐1990s. MED was detected in Brazil in 2014, and since then, high insect infestations have been reported, mainly in greenhouse production of solanaceous crops. In this study, we compared the transmission efficiency of the begomoviruses tomato severe rugose virus (ToSRV) and tomato rugose mosaic virus (ToRMV) to tomato (Solanum lycopersicum) and bean golden mosaic virus (BGMV) to common bean (Phaseolus vulgaris) by a population of MEAM1 and four populations of MED. B. tabaci MEAM1 efficiently transmitted ToSRV and ToRMV to tomato and BGMV to common bean. B. tabaci MED populations did not transmit ToSRV and ToRMV to tomato. Only one population of B. tabaci MED transmitted BGMV with an efficiency of 3%. PCR analysis indicated that B. tabaci MED can acquire ToSRV, ToRMV and BGMV after a 24 h acquisition access period. Furthermore, some greenhouse tomato plantings from São Paulo state were surveyed for the presence of viruses and whiteflies, and B. tabaci MED were predominant, whereas begomoviruses were absent. Our results demonstrate that populations of MED from São Paulo state are inefficient vectors of begomoviruses, being able to acquire the virus but not transmit it to plants. Understanding this interaction is important and suggests that the spread of B. tabaci MED could change the epidemiological scenario of begomoviruses in areas where this insect predominates.

Research on Multi-Step Fruit Color Prediction Model of Tomato in Solar Greenhouse Based on Time Series Data

Color change is the most obvious characteristic of the tomato ripening stage and an important indicator of the tomato ripening condition, which directly affects the commodity value of tomato. To visualize the color change of tomato fruit during the mature stage, this paper proposes a gated recurrent unit network with an encoder–decoder structure. This structure dynamically simulates the growth and development of tomatoes using time-dependent lines, incorporating real-time information such as tomato color and shape. Firstly, the .json file was converted into a mask.png file, the tomato mask was extracted, and the tomato was separated from the complex background environment, thus successfully constructing the tomato growth and development dataset. The experimental results showed that for the gated recurrent unit network with the encoder–decoder structure proposed, when the hidden layer number was 1 and hidden layer number was 512, a high consistency and similarity between the model predicted image sequence and the actual growth and development image sequence was realized, and the structural similarity index measure was 0.746. It was proved that when the average temperature was 24.93 °C, the average soil temperature was 24.06 °C, and the average light intensity was 11.26 Klux, the environment was the most suitable for tomato growth. The environmental data-driven tomato growth model was constructed to explore the growth status of tomato under different environmental conditions, and thus, to understand the growth status of tomato in time. This study provides a theoretical foundation for determining the optimal greenhouse environmental conditions to achieve tomato maturity and it offers recommendations for investigating the growth cycle of tomatoes, as well as technical assistance for standardized cultivation in solar greenhouses.

Growth Monitoring of Greenhouse Tomatoes Based on Context Recognition

To alleviate social problems in agriculture such as aging and labor force shortages, automatic growth monitoring based on image measurement has been introduced to tomato cultivation in greenhouses. The overlap of leaves and fruits makes precise observations challenging. In this study, we applied context recognition to tomato growth monitoring by using a Bayesian network. The proposed method combines image recognition using convolutional networks and context recognition using Bayesian networks. It enables not only the recognition of individual tomatoes but also the evaluation of tomato plants. An accurate number of tomatoes and the condition of the stocks can be estimated based on the number of ripe and unripened tomatoes in addition to their density information. The verification experiments clarified that a more accurate number of tomatoes could be estimated than with simple tomato detection, and the stock states could also be evaluated correctly. Compared to conventional methods, the method used in this study has improved tomato decision accuracy by 23%.

Effect of Sex, Age and Temperature on the Functional Response of Macrolophus pygmaeus Ramber and Nesidiocoris tenuis Reuter (Heteroptera: Miridae) on Eggs of Tuta absoluta.

The predatory mirids Macrolophus pygmaeus Ramber (Heteroptera: Miridae) and Nesidiocoris tenuis Reuter (Heteroptera: Miridae) are used for the biological control of Tuta absoluta Meyrick (Lepidoptera: Gelechiidae) and other pests in tomato greenhouses. The functional response of 1-day-old (young) and 10-day-old (old) adult females and males of M. pygmaeus and N. tenuis on eggs of T. absoluta was determined on tomato at two temperatures (20 °C and 25 °C) and LD 16:8. Females of M. pygmaeus exhibited higher predation efficiency than males at both tested temperatures. Young M. pygmaeus females had a higher efficiency than old ones, whereas males had a low efficiency irrespective of age. The predation efficiency of N. tenuis was high (but lower than M. pygmaeus) in both young females and males, although old females had a higher efficiency than the respective males. Our results show that the two predatory species have different functional response characteristics to their prey depending mainly on sex and age, which may affect their role as biological control agents.

Utilizing Machine Learning to Detect Whiteflies on Tomatoes in a Controlled Environment

The tomato is the vegetable crop with the most economic impact globally, and its production has expanded significantly over time. Recent years have seen the discovery of whiteflies as a serious loss-maker in producing fresh-market and greenhouse tomatoes. Tomato plants are harmed both directly and indirectly by whitefly nymphs and adults. The leaves are attacked by whiteflies, causing them to turn yellow and curl up, resulting in their destruction. Currently, early whitefly migrations are detected using yellow sticky traps. However, executing this activity takes a lot of time and effort. In order to identify plant pests more rapidly and precisely, a method of early detection that significantly reduces economic losses was created. In this research, we proposed to use an image analysis and machine learning technique to develop a model for detecting whiteflies on tomatoes in a greenhouse. Images of leaves covered in whiteflies were obtained, and the EfficientDet-D0 model was used to train the machine learning algorithm. Results indicate that this new method might detect whiteflies with acceptable precision and an F1 score of 0.40, indicating that EfficientDet-D0 models reliably recognize the distinctive characteristics of whiteflies.

SSR-BASED ASSESSMENT OF GENETIC DIVERSITY IN TOMATO CULTIVARS AND LINES GROWN IN KAZAKHSTAN

Tomato (Solanum lycopersicum L.) is a versatile crop known for its nutritional value and health benefits, thriving in diverse climates worldwide. Nevertheless, regional variations persist in tomato yield, with Kazakhstan serving as an example of lower productivity in contrast to global averages. Closing this disparity requires comprehensive genetic studies and breeding efforts. This study is focused on the genetic diversity of 49 tomato cultivars and hybrids sourced from Kazakh Research Institute of Fruit and Vegetable Growing (Almaty), employing 10 SSR markers associated with important agronomic traits. SSR genotyping unveiled polymorphisms across 6 markers with variable allele numbers. Genetic diversity metrics highlighted significant genetic diversity within both outdoor and greenhouse tomato cultivars and lines. Bayesian clustering, Neighbor-joining (NJ) clustering, and principal coordinate analysis (PCoA) delineated genetic differentiation between outdoor and greenhouse tomatoes with small admixture, indicating distinct breeding directions for these two types. Highly polymorphic SSRs (PIC > 0.5) associated with essential fruit traits emerge as promising targets for marker-assisted selection (MAS) that can be used to enhance tomato breeding efficiency in Kazakhstan. According to 8 SSRs, 22 out of 30 outdoor accessions and 8 greenhouse tomato accessions were genetically uniform. This study offers comprehensive insights into the genetic diversity and population structure of tomato cultivars and lines in Kazakhstan, laying the foundation for informed breeding endeavors aimed at bolstering yield and resilience in tomato crops.

Novel solid oxide fuel cell system integrating adsorption cooling, heating, and carbon dioxide fertilization for greenhouse tomato production

Fuel cells have been widely used for power sources in the power plant, building, and transportation sectors, while few attempts have been made to use fuel cells for agriculture. Heating costs make up a high proportion of agricultural operating costs, and fossil fuels are the primary fuels used, which affects agricultural income according to changes in international oil prices. This study proposes a novel solid oxide fuel cell(SOFC) system integrates adsorption cooling, heating, and carbon dioxide fertilization for greenhouse tomato production. To confirm the superiority of the proposed system (Case 3) in terms of efficiency, its energy consumption is compared with that of the reference systems (cases 1 and 2). Case 1 uses only the air-cooled scroll chiller while Cases 2 and 3 utilize both the air-cooled scroll chiller and the adsorption chiller as a heating and cooling device for the horticulture. In order to reduce the energy consumption for the horticulture thermal management, electrical and thermal energy required for the adsorption chiller are supplied by the SOFC. Case 3 supplied SOFC exhaust gas to fertilize carbon dioxide in horticulture for photosynthesis of the tomato. To determine the heating and cooling loads for horticulture, horticulture energy modeling has been developed based on target temperature, weather data, and tomato evapotranspiration using TRNSYS®. The total power consumption in Case 3 is reduced by up to 12 % during the heating season compared to Case 1, but it increases by up to 6.2 % during the cooling season. For Case 2, there is a savings of 9.8 % during the heating season but an increase of up to 4.1 % during the cooling season compared to Case 1. The study contributes to reduction of the energy consumption for the greenhouse crops production. This finally results in the significant reduction of the carbon emission and the enhancement of the economics in agricultural field.

Design and Experiment of Nighttime Greenhouse Tomato Harvesting Robot

In response to the issue of high tomato yield, low efficiency in harvesting tomatoes grown in greenhouses, and low recognition accuracy of nighttime harvesting robots, a design was developed and a robotic system was created specifically for nighttime greenhouse tomato harvesting. The robot employs a vision system and YOLOv5+HSV fusion algorithm to recognize and locate tomatoes. It then transmits this information to the robotic arm. By coordinating the visual system, the robotic arm, the end effector, and the lifting mechanism, the robot accurately picks ripe tomatoes. The robot was subjected to simulated field tests for visual recognition and harvesting, both during daytime and nighttime conditions. The results showed that the success rate of nighttime harvesting was slightly lower than during the daytime but remained at a relatively high level. The daytime harvesting success rate and the average time to pick a single fruit were 87.78% and 15.99 seconds, respectively. The nighttime harvesting success rate and the average time to pick a single fruit were 87.55% and 17.26 seconds, respectively. This approach effectively improves the recognition accuracy and harvesting speed of the harvesting robot, reducing damage to tomatoes during harvesting, and addresses the issues of supplementary lighting and image noise reduction for nighttime harvesting robots.

Twindemic Threats of Weeds Coinfected with Tomato Yellow Leaf Curl Virus and Tomato Spotted Wilt Virus as Viral Reservoirs in Tomato Greenhouses.

Tomato yellow leaf curl virus (TYLCV) and tomato spotted wilt virus (TSWV) are well-known examples of the begomovirus and orthotospovirus genera, respectively. These viruses cause significant economic damage to tomato crops worldwide. Weeds play an important role in the ongoing presence and spread of several plant viruses, such as TYLCV and TSWV, and are recognized as reservoirs for these infections. This work applies a comprehensive approach, encompassing field surveys and molecular techniques, to acquire an in-depth understanding of the interactions between viruses and their weed hosts. A total of 60 tomato samples exhibiting typical symptoms of TYLCV and TSWV were collected from a tomato greenhouse farm in Nonsan, South Korea. In addition, 130 samples of 16 different weed species in the immediate surroundings of the greenhouse were collected for viral detection. PCR and reverse transcription-PCR methodologies and specific primers for TYLCV and TSWV were used, which showed that 15 tomato samples were coinfected by both viruses. Interestingly, both viruses were also detected in perennial weeds, such as Rumex crispus, which highlights their function as viral reservoirs. Our study provides significant insights into the co-occurrence of TYLCV and TSWV in weed reservoirs, and their subsequent transmission under tomato greenhouse conditions. This project builds long-term strategies for integrated pest management to prevent and manage simultaneous virus outbreaks, known as twindemics, in agricultural systems.

Efficacy of Commercial Biocontrol Products for the Management of Verticillium and Fusarium Wilt in Greenhouse Tomatoes: Impact on Disease Severity, Fruit Yield, and Quality

Verticillium dahliae (Vd) and Fusarium oxysporum f. sp. lycopersici (Fol) are two major fungal pathogens that infect tomato plants, causing significant challenges in their control since both pathogens can persist in the soil for several years even in the absence of a host plant and no effective fungicides are available at present. This study investigated the efficacy of two biocontrol formulations, Clonotri (containing Trichoderma and Clonostachys microorganisms) and Strepse (comprising Streptomyces and Pseudomonas microorganisms), against Vd and Fol and their impact on tomato fruit quality and yield under greenhouse conditions. The pathogenicity experiment demonstrated that the Clonotri formulation, containing Trichoderma and Clonostachys spores, significantly reduced Fusarium wilt disease by 32% compared to the control group. However, in the Vd pathogenicity experiment, the formulations did not exhibit disease reduction, although treatment with Strepse, containing Streptomyces and Pseudomonas microorganisms, resulted in a preserved total fruit number when compared to uninfected plants. Analysis of fruit quality attributes revealed no significant differences among the various interventions. Furthermore, Fol infection in the first fruit set significantly increased fruit firmness, while Vd infection resulted in elevated levels of total soluble solids in fruits. These findings demonstrate that the evaluated biocontrol formulations provide a degree of protection against fungal wilt pathogens in tomato plants and can increase yield in greenhouse conditions while having minimal impact on overall fruit quality attributes.

Non-Herbivore-Induced Plant Organic Volatiles of Tomato Cultivars and Their Effect on Pest Biological Control

Herbivore-induced plant organic volatiles (HIPVs) have recently been studied to improve biological pest control. In contrast, the effects of volatile organic compounds (VOCs) that are not induced by herbivory (non-HIPVs) have received less attention. The latter are essential in the first stages of crop colonization by entomophagous insects (predators and parasitoids) used in biological pest control programs. Furthermore, the effects on entomophagous insects of different cultivars of a cultivated botanical species have not been studied. The aim of this work was to study the different non-HIPVs found in 10 tomato cultivars used in tomato greenhouses on two entomophages: the egg parasitoid Trichogramma achaeae (Hymenoptera, Trichogrammatidae) and the zoo-phytophagous predator Nesidiocoris tenuis (Hemiptera, Miridae). The results indicate that although there is considerable quantitative and qualitative variation in the emission of VOCs in the 10 tomato cultivars analysed, this variability made it difficult to determine the influence of the volatiles on the attraction of the predatory species N. tenuis, with only one cultivar (Rebelion) exhibiting a significantly higher attractiveness than the rest of the cultivars. For the parasitoid T. achaeae, these same volatiles had a significant effect (in part) on parasitoid behaviour. However, this attraction was not reflected in the discriminant analysis, at least for the volatiles analysed. The analysis showed four groups of well-differentiated cultivars, according to the non-HIPV composition, and this bore no relation to the levels of attractiveness registered in the different cultivars, with the exception again of the Rebelion cultivar, which seems not to be very attractive for the parasitoid and its parasitism activity. The implications of non-herbivore-induced (non-HPV) VOCs in the biological control of greenhouse pest species are described and discussed.

Prediction Model of Nitrogen, Phosphorus, and Potassium Fertilizer Application Rate for Greenhouse Tomatoes under Different Soil Fertility Conditions

To reach the target yield of crops, nutrient management is essential. Selecting the appropriate prediction model and adjusting the nutrient supply based on the actual situation can effectively improve the nutrient utilization efficiency, crop yield, and product quality. Therefore, a prediction model of the NPK fertilizer application rate for greenhouse tomatoes under the target yield was studied in this study. Under low, medium, and high soil fertility conditions, a neural network prediction model based on the sparrow search algorithm (SSA-NN), a neural network prediction model based on the improved sparrow search algorithm (ISSA-NN), and a neural network prediction model based on the hybrid algorithm (HA-NN) were used to predict the NPK fertilizer application rate for greenhouse tomatoes. The experimental results indicated that the evaluation indexes (i.e., the mean square error (MSE), explained variance score (EVS), and coefficient of determination (R2)) of the HA-NN prediction model proposed in this study were superior than the SSA-NN and ISSA-NN prediction models under three different soil fertility conditions. Under high soil fertility, compared with the SSA-NN prediction model, the MSE of the ISSA-NN and HA-NN prediction models decreased to 0.007 and 0.005, respectively; the EVS increased to 0.871 and 0.908, respectively; and the R2 increased to 0.862 and 0.899, respectively. This study showed that the HA–NN prediction model was superior in predicting the NPK fertilizer application rate for greenhouse tomatoes under three different soil fertility conditions. Due to the significance of NPK fertilizer application rate prediction for greenhouse tomatoes, this technique is expected to bring benefits to agricultural production management and decision support.

Foliar applications of a Malvaceae-derived protein hydrolysate and its fractions differentially modulate yield and functional traits of tomato under optimal and suboptimal nitrogen application.

Protein hydrolysates (PHs) can enhance plant nitrogen nutrition and improve the quality of vegetables, depending on their bioactive compounds. A tomato greenhouse experiment was conducted under both optimal (14 mM) and suboptimal (2 mM) nitrogen (N-NO3) conditions. Tomatoes were treated with a new Malvaceae-derived PH (MDPH) and its molecular fractions (MDPH1, >10 kDa; MDPH2, 1-10 kDa and MDPH3, <1 kDa). Under optimal N conditions, the plants increased biomass and fruit yield, and showed a higher photosynthetic pigment content in leaves in comparison with suboptimal N, whereas under N-limiting conditions, an increase in dry matter, soluble solid content (SSC) and lycopene, a reduction in firmness, and changes in organic acid and phenolic compounds were observed. With 14 mM N-NO3, MDPH3 stimulated an increase in dry weight and increased yield components and lycopene in the fruit. The MDPH2 fraction also resulted in increased lycopene accumulation in fruit under 14 mM N-NO3. At a low N level, the PH fractions showed distinct effects compared with the whole MDPH and the control, with an increase in biomass for MDPH1 and MDPH2 and a higher pigment content for MDPH3. Regardless of N availability, all the fractions affected fruit quality by increasing SSC, whereas MDPH2 and MDPH3 modified organic acid content and showed a higher concentration of flavonols, lignans, and stilbenes. The molecular weight of the peptides modifies the effect of PHs on plant performance, with different behavior depending on the level of N fertilization, confirming the effectiveness of fractioning processes. © 2024 Society of Chemical Industry.