Evaluating Tomato productivity using hydrogels in a greenhouse environment in Zimbabwe

In a greenhouse study on tomato, cucumber, and paprika, the effectiveness of TUVA product, a biostimulant and plant growth regulator manufactured from plant-origin amino acids used as organic fertilizer, was assessed. The goal of this study was to determine whether using the TUVA biostimulant, which is based on soybean and seaweed extracts and contains rich microelements essential for plant growth, can ensure good crop yield and quality in tomato, cucumber, and paprika plants growth and productivity. Over six months, the study was held in Mersin, Turkey, at three distinct sites. We looked at the physiological reactions of tomato, cucumber, and paprika plants that were grown in greenhouses and given TUVA biostimulant treatments. The use of the TUVA biostimulant increased plant height by 15% and biomass by 20%, as well as the number of fruits produced and total yield (up to 20.2%). The findings demonstrated that the TUVA product considerably improved all three crops' tonnage, homogeneous size, fruit quantity, and earliness. The fruits' quality was also improved by the TUVA product, which increased their marketability. The findings demonstrate a substantial positive correlation (R=0.9) between the TUVA product and each of the three crops' properties. The correlation coefficients further demonstrate the significant relationship (p ≤0.05) among variables. According to the trial's findings, the TUVA product has promise as a plant growth regulator for usage in greenhouse environments. It can considerably (p ≤0.05) increase the productivity and quality of tomato, cucumber, and paprika crops, and it is safe to use. According to the findings of this research, TUVA is a promising novel growth biostimulant that has the potential to enhance the performance of tomato, cucumber, and paprika plants cultivated in greenhouse environments. The methods by which TUVA improves plant development and productivity need more investigation, but the results of this study indicate that TUVA is a useful tool for producers who want to raise the yield and quality of their crops.

In the modern agricultural system, the necessity for greenhouses is increasingly demanding due to unfavorable climatic conditions that hugely impact crop yields. The production of essential but very much weather-sensitive crops like tomatoes, beans, etc, can be improved by considering a greenhouse environment by regulating temperature, humidity, etc. In West Bengal’s lush plains tomato production has not been able to keep up with the growing demand, and prices for tomatoes in the state’s major cities have increased significantly in the recent past. However, designing the ideal greenhouse entails some ambiguity and complexity, given the variability in the climatic patterns and crop requirements. This uncertainty can be efficiently examined utilizing cylindrical neutrosophic set (CNS), which helps to manage the ambiguous and contradictory information inherent in decision-making processes, resulting in more exact and reliable greenhouse planning. Furthermore, the Dombi logarithmic law produces a very strong and consistent output result with a slight variation in operating parameters. In this research article, we have applied our proposed decision-making process to determine the best greenhouse site for cultivating tomato crops. For this purpose, we have defined Dombi logarithmic aggregation operational laws in the framework of cylindrical neutrosophic numbers (CNN) and utilized these laws to establish a new aggregation operator namely cylindrical neutrosophic Dombi weighted logarithmic aggregation operator (CNDWLA). The said aggregation operational laws & aggregation operator have been applied to present a new and novel decision-making process where full consistency method (FUCOM) and multi-objective optimization (MOO) have been integrated and embedded fruitfully. Here, the objective functions were formulated using the concept of a single-layer neural network and then MOO and FUCOM methods are implemented to assess criterion weights. We have resolved the most favorable pareto optimal solution derived from MOO by employing simulation and the method for order of preference by similarity to ideal solution (TOPSIS) approach. We also discovered that measurement alternatives and ranking according to compromise solution (MARCOS) and the multi-objective optimization on the basis of ratio analysis (MOORA) methods have not been utilized in the CN environment. Therefore, we have applied our proposed decision-making method with MARCOS and MOORA techniques to determine the optimal greenhouse site for tomato production in West Bengal. An exhaustive sensitivity and comparison analysis have been conducted to assess the stability and robustness of our multi-criteria group decision-making (MCGDM) method. The analysis of our study points out that South Bengal is the most appropriate greenhouse place for cultivating tomatoes in West Bengal.

Scaling up from crop to farm level: Co-innovation framework to improve vegetable farm systems sustainability

Tomatoes are popular worldwide and represent a high water-dependent horticultural crop cultivated both in open fields and greenhouses. Several irrigation management strategies are currently...

Comparative Assessment of the Effect of Wastewater Sludge Biochar on Growth, Yield and Metal Bioaccumulation of Cherry Tomato

Increasing commercial use of controlled release fertilizer (CRF) has prompted the need to predict N release simply and viably in the greenhouse environment. Two CRFs were tested, i.e., P40d and P100d by incubating them for 40 or 100 days either in static water at 10, 15, 20, 25, and 35 °C or in the soil of vegetable plots in a greenhouse lacking temperature controls. Cumulative nitrogen release (CNR) from a CRF was represented by a parabola curve and significantly affected by the incubation temperature. A method to calculate N m (the maximum N release percentage from CRF) was established using a first-order kinetic equation and the method of least squares. N m was 90.9% to 99.9% for P40d and 72.1% to 87.1% for P100d at 10–35 °C, respectively. A relationship function between the N release rate and naturally fluctuating greenhouse soil temperatures was established using the activation energy of the N release reaction. Then a model was constructed with field temperature as the variable to predict N release throughout the entire greenhouse crop production season. The value of ψ representing a property of the coating material of a CRF is ≈ 1.0 for the release period of the CRF of 35–55 days and ≈ 1.2 of 80–120 days. We validated the model using two seasons of greenhouse tomato, Solanum lycopersicum L., and cucumber, Cucumis sativus L., production data, and found that the error was less than 12% points. This indicated that the constructed model was sufficiently simple, practical, and accurate for use by growers, and fertilizer industry and regulatory personnel.

Control of vapor pressure deficit (VPD) in greenhouse enhanced tomato growth and productivity during the winter season

A study was conducted from January to February 2018 to determine bacterial wilt disease (BWD) incidence and severity in open-field and greenhouse environments in twelve tomato growing districts in Tanzania. About 220 farmers were interviewed to assess their knowledge on BWD by using a semi structured questionnaire. Results indicated significant (p 80% of 220 respondents) of farmers could not identify sources of BWD in environment and do not adhere to sanitation measures recommended for greenhouse tomato production. 90% of the interviewed famers ventured into greenhouse tomato production by imitating from neighbors without technical guidance. To manage BWD, majority (70%) of farmers use chemicals which they reported as ineffective, 13% use botanical, 10% do crop rotation which was reported to be not practical because of land scarcity and long time that Ralstonia solanacearum can survive. Rest (7%) of farmers do not use any BWD management measure. There was no report of either use of disease resistant cultivars or biological control as a strategy for BWD management in the study area. There is therefore need to develop techniques for farmers to manage the BWD by exploring promising options such as use of effective botanical extracts.

Grafting is an important means to overcome the obstacles of continuous cropping of solanaceous vegetables. The objective of this subject was to evaluate the performance of different rootstocks in grafted tomato (Solanum lycopersicum L.) under coconut coir cultivation. This research was carried out on a scion ‘Ruifen 882’ grafted onto four rootstocks (‘Guangzhen 1’, ‘Zhenai 1’, ‘Ganzhen 1’, and ‘Guozhen 1’) in comparison with non-grafted and self-grafted ‘Ruifen 882’ plants. The experiment was conducted in a greenhouse environment and adopted the casing grafting method with three replicates; 20 plants per replicate were employed in a randomized block design. The following variables were analyzed: graft survival rate, growth parameters (plant height, stem diameter, fresh and dry weight of above-ground part and under-ground part, root-shoot ratio and strong seedling index), physiological characteristics (chlorophyll and mineral element contents), fruit yield, and fruit quality (hardness, soluble solid, soluble sugar, titratable acid, vitamin C and lycopene). The results of growth monitoring indicated that grafting could improve the growth and development of tomato plants at the seedling stage and ‘Ruifen 882’/‘Guozhen 1’ (R/GUO) had high grafting survival rate of nearly 98%, which is close to the self-grafted plants. Physiological analysis showed that R/GUO and ‘Ruifen 882’/‘Zhenai 1’ (R/ ZA) significantly increased the chlorophyll content and absorption of K, Ca, Mg, Fe, Na, Mn and Cu. On fruit yield, grafts ‘Ruifen 882’/‘Ganzhen 1’ (R/GAN) and R/GUO had better performance. Comprehensive analysis showed that the best results for tomato scion growth, development, fruit quality and yield were observed with the graft combination R/GUO.

ABSTRACTEffective irrigation management is crucial for optimizing crop production, particularly in water‐scarce regions. This study evaluated the performance of an Arduino‐based system designed to monitor and control soil moisture in a greenhouse setting, focusing on its impact on tomato plant growth, fruit yield, and fruit size under two different irrigation treatments. Treatment 1 (T1) involved low moisture with significant fluctuations (55%–85% soil moisture), while Treatment 2 (T2) maintained optimal and stable moisture levels (70%–85%). Soil moisture dynamics revealed that in T1, moisture levels oscillated significantly, dropping to 55% before irrigation restored them to 85%. This cyclical pattern indicates a stress‐response mechanism triggered by the system, which is essential for mitigating plant stress and ensuring optimal growth. Conversely, the optimal moisture treatment maintained more stable soil moisture levels between 70% and 85%, promoting healthy plant development and physiological functions. The correlation between sensor readings and gravimetric measurements was analyzed using a 45° diagonal correlation approach, demonstrating strong agreement between the two methods and reinforcing the reliability of sensor‐based irrigation. Physiological assessments indicated that seedlings under optimal irrigation experienced a 30% increase in fresh weight, a 6% increase in dry weight, a 16% increase in plant height, and a 25% higher SPAD values compared to T1 at the young stage. At maturity, T2 plants exhibited a 52% increase in fresh weight, a 78% increase in dry weight, and a 121% increase in plant height. Fruit yield increased by 47% in T2, with an average of 56 fruits per plant compared to 45 in T1, and the average fruit weight was 85 g in T2 compared to 56 g in T1. Future research should explore the integration of advanced sensors, machine learning algorithms, and predictive models to further optimize irrigation strategies, with an emphasis on scalability and environmental impact. By refining these technologies, agriculture can achieve more sustainable and productive outcomes in the face of increasing environmental challenges.

Plant growth depends heavily on Magnesium (Mg), particularly for crops like tomatoes (Solanum lycopersicum), which are particularly vulnerable to nutritional imbalances in soil science. Magnesium availability can be changed by calcareous soils that are typically found in agricultural areas, depending on their mineral composition, pH, and organic matter in plant science. Understanding the ideal magnesium concentrations for recently transplanted tomato seedlings to thrive in these soils is crucial for boosting crop production and reducing nutrient deficiencies in plant science. To maximize tomato production in calcareous soils, the research looks into the necessary magnesium concentrations in soil science. It also develops potential fertilizing techniques to improve tomato production in these types of soils. The experiment was set up in a controlled greenhouse environment to eliminate any outside influences like weather and pest activity. Positional bias was avoided by randomly assigning calcareous soil to pots that were similar in volume and texture. Mg levels varied throughout the five treatment groups: Group 1 as low (20 mg/kg), Group 2 as medium-low (40 mg/kg), Group 3 as medium (60 mg/kg), Group 4 as medium-high (80 mg/kg), and Group 5 as high (100 mg/kg). The tomato seed was demonstrated to be grown in a controlled environment concerning temperature, humidity, and lighting in each pot. Among the several metrics used to evaluate the impact of magnesium on plant growth were plant height, fruit yield, and chlorophyll content. The content of magnesium and plant growth is strongly positively correlated. The plants that grew and produced the most fruit had magnesium levels between 50 and 70 mg/kg. Reduced magnesium concentration (less than 50 mg/kg) was accompanied by decreased fruit yield. Mg's function in photosynthesis is demonstrated by the greatest concentration of chlorophyll, which was 50–70 mg/kg Mg. According to research, magnesium levels in calcareous soils should be kept below the recommended critical range to increase tomato yield in soil science. It implies that for higher yields in tomato growing, targeted magnesium fertilization is crucial in plant science.

The role of a proposed micro-fog system in regulating greenhouse environments and enhancing tomato (Solanum lycopersicum L.) productivity during summer season was studied. Experiments were carried out in a multi-span glass greenhouse, which was divided into two identical compartments involving different environments: (1) without environment control and (2) with a micro-fog system operating when the air vapor pressure deficit (VPD) of greenhouse was higher than 0.5 KPa. The micro-fog system effectively alleviated heat stress and evaporative demand in the greenhouse during summer season. The physiologically favourable environment maintained by micro-fog treatment significantly enhanced elongation of leaf and stem, which contributed to a substantial elevation of final leaf area and shoot biomass. These improvements in physiological and morphological traits resulted in around 12.3% increase of marketable tomato yield per plant. Relative growth rate (RGR) of micro-fog treatment was also significantly higher than control plants, which was mainly determined by the substantial elevation in net assimilation rate (NAR), and to a lesser extent caused by leaf area ratio (LAR). Measurement of leaf gas exchange parameters also demonstrated that micro-fog treatment significantly enhanced leaf photosynthesis capacity. Taken together, manipulation of VPD in greenhouses by micro-fog systems effectively enhanced tomato growth and productivity via improving photosynthesis during summer season.

This study shows the possibilities of how to replace tedious human labor—scouting of yellow sticky traps (YST) for whiteflies—using artificial cognitive vision, specifically the deep convolutional network (CNN), as a part of the more complex system—BERABOT. The used CNN is the Faster R-CNN trained by deep transfer learning to substitute human scouting when the low whiteflies infection phase was specifically targeted. The training was conducted on pictures taken inside the heated and lighted tomato production greenhouse of “Bezdínek Farm” in Dolni Lutyne, Czechia. Used pictures were collected in a way planned for future fully automated robotic applications in the BERABOT system. The achieved results were compared with the scouting results of a professional phytopathologist. The trained employee’s scouting results against the professional phytopathologist accomplished root-mean-square error (RMSE) equal to 4.23, while the developed CNN model was evaluated to be 5.83. The results presented here open up new frontiers for further CNN model tuning leading to the potential in substituting an employee(s) in the future and make tomato production less expensive and less human labor dependent.

Early detection and efficient management practices to control Tuta absoluta (Meyrick) infestation is crucial for safeguarding tomato production yield and minimizing economic losses. This study investigates the detection of T. absoluta infestation on tomato plants using object detection models combined with ensemble techniques. Additionally, this study highlights the importance of utilizing a dataset captured in real settings in open-field and greenhouse environments to address the complexity of real-life challenges in object detection of plant health scenarios. The effectiveness of deep-learning-based models, including Faster R-CNN and RetinaNet, was evaluated in terms of detecting T. absoluta damage. The initial model evaluations revealed diminishing performance levels across various model configurations, including different backbones and heads. To enhance detection predictions and improve mean Average Precision (mAP) scores, ensemble techniques were applied such as Non-Maximum Suppression (NMS), Soft Non-Maximum Suppression (Soft NMS), Non-Maximum Weighted (NMW), and Weighted Boxes Fusion (WBF). The outcomes shown that the WBF technique significantly improved the mAP scores, resulting in a 20% improvement from 0.58 (max mAP from individual models) to 0.70. The results of this study contribute to the field of agricultural pest detection by emphasizing the potential of deep learning and ensemble techniques in improving the accuracy and reliability of object detection models.

Tomato vascular wilt disease, caused by the pathogen Fusarium oxysporum f. sp. lycopersici, is a major challenge for tomato production, leading to significant crop losses and economic impact. Biological control using isolates of Trichoderma harzianum has emerged as a promising alternative to chemical fungicides, offering an environmentally friendly approach to disease management. In this study, we examined the effectiveness of seed coating with T. harzianum isolates in controlling F. oxysporum f. sp. lycopersici in tomatoes. Tomato seeds were coated with T. harzianum two weeks prior to inoculation with the pathogen in a controlled greenhouse environment. Disease incidence, plant growth, and flowering were monitored to assess the efficacy of the treatment. The results demonstrated that seed coating with T. harzianum effectively controlled the pathogen, leading to a marked reduction in disease incidence (index = 1.6). Additionally, treated plants showed enhanced growth factors, including improved germination and flowering. This study confirms the potential of T. harzianum as a biological control agent, particularly when applied before pathogen inoculation. The significance of this research lies in its potential to improve tomato yields and reduce economic losses due to vascular wilt disease. By providing an alternative to chemical fungicides, this method offers environmental and public health benefits while supporting sustainable agricultural practices.