Search Results (4,525)

Gelatin is a promising biopolymer for edible coatings thanks to its low cost and gelling properties. However, its weak mechanical properties limit its use. This study aimed to develop a gelatin coating with tomato extract, analyzing its antioxidant activity and rheological properties for food applications. Gelatin concentrations (2, 5, and 7%) were evaluated, and it was determined that 7% with 7.5% glycerol was the optimal mixture. Three concentrations of tomato extract (0.5, 1, and 1.5%) were added, and antioxidant activity was evaluated using the ABTS technique, as well as the interaction of components through FT-IR and physicochemical analysis. The results showed that there were no significant differences in terms of their physicochemical characterization, maintaining a pH of 5 and a yellowish hue. The FT-IR spectra indicated there were hydrogen bond interactions between gelatin and the extract. The antioxidant capacity was higher with the 1.5% extract, achieving an inhibition of 58.9%. It was found that the combination of the different materials used improved the rheological (specifically the viscosity and stability of the material) and antioxidant properties of the gelatin. These findings suggest that modified gelatin coatings may be effective in extending the shelf life of foods. Full article

Climate change has emerged as a crucial global issue that significantly threatens the survival of plants. In particular, low temperature (LT) is one of the critical environmental factors that influence plant morphological, physiological, and biochemical changes during both the vegetative and reproductive growth stages. LT, including abrupt drops in temperature, as well as winter conditions, can cause detrimental effects on the growth and development of tomato plants, ranging from sowing, transplanting, truss appearance, flowering, fertilization, flowering, fruit ripening, and yields. Therefore, it is imperative to understand the comprehensive mechanisms underlying the adaptation and acclimation of tomato plants to LT, from the morphological changes to the molecular levels. In this review, we discuss the previous and current knowledge of morphological, physiological, and biochemical changes, which contain vegetative and reproductive parameters involving the leaf length (LL), plant height (PH) stem diameter (SD), fruit set (FS), fruit ripening (FS), and fruit yield (FY), as well as photosynthetic parameters, cell membrane stability, osmolytes, and ROS homeostasis via antioxidants scavenging systems during LT stress in tomato plants. Moreover, we highlight recent advances in the understanding of molecular mechanisms, including LT perception, signaling transduction, gene regulation, and fruit ripening and epigenetic regulation. The comprehensive understanding of LT response provides a solid basis to develop the LT-resistant varieties for sustainable tomato production under the ever-changing temperature fluctuations. Full article

Timely and accurate detection of diseases in vegetables is crucial for effective management and mitigation strategies before they take a harmful turn. In recent years, convolutional neural networks (CNNs) have emerged as powerful tools for automated disease detection in crops due to their ability to learn intricate patterns from large-scale image datasets and make predictions of samples that are given. The use of CNN algorithms for disease detection in important vegetable crops like potatoes, tomatoes, peppers, cucumbers, bitter gourd, carrot, cabbage, and cauliflower is critically examined in this review paper. This review examines the most recent state-of-the-art techniques, datasets, and difficulties related to these crops’ CNN-based disease detection systems. Firstly, we present a summary of CNN architecture and its applicability to classify tasks based on images. Subsequently, we explore CNN applications in the identification of diseases in vegetable crops, emphasizing relevant research, datasets, and performance measures. Also, the benefits and drawbacks of CNN-based methods, covering problems with computational complexity, model generalization, and dataset size, are discussed. This review concludes by highlighting the revolutionary potential of CNN algorithms in transforming crop disease diagnosis and management strategies. Finally, this study provides insights into the current limitations regarding the usage of computer algorithms in the field of vegetable disease detection. Full article

Improving the efficiency and performance of control systems in food processing remains a significant challenge for engineers and researchers. In this paper, Proportional, Integral, and Derivative (PID) control; Model Predictive Control (MPC); and Adaptive Model Predictive Control (AMPC) were implemented on a Continuous Flow Ohmic Heater (CFOH) pilot plant to process tomato basil sauce. The sauce, composed of tomato puree, basil, spices, and other ingredients, was used to assess the effectiveness of these advanced control strategies. This research presents a case study on the pilot-scale heating of tomato basil sauce, with applications in the broader food industry. The performances and energy efficiencies of the different control techniques were compared, demonstrating significant improvements in controlling the CFOH process. The results highlight the industrial practicality of using CFOH technology with advanced process controls for food processing. Full article

Agricultural productivity is one of the critical factors towards ensuring food security across the globe. However, some of the main crops, such as potato, tomato, and mango, are usually infested by leaf diseases, which considerably lower yield and quality. The traditional practice of diagnosing disease through visual inspection is labor-intensive, time-consuming, and can lead to numerous errors. To address these challenges, this study evokes the AgirLeafNet model, a deep learning-based solution with a hybrid of NASNetMobile for feature extraction and Few-Shot Learning (FSL) for classification. The Excess Green Index (ExG) is a novel approach that is a specified vegetation index that can further the ability of the model to distinguish and detect vegetative properties even in scenarios with minimal labeled data, demonstrating the tremendous potential for this application. AgirLeafNet demonstrates outstanding accuracy, with 100% accuracy for potato detection, 92% for tomato, and 99.8% for mango leaves, producing incredibly accurate results compared to the models already in use, as described in the literature. By demonstrating the viability of a deep learning/IoT system architecture, this study goes beyond the current state of multi-crop disease detection. It provides practical, effective, and efficient deep-learning solutions for sustainable agricultural production systems. The innovation of the model emphasizes its multi-crop capability, precision in results, and the suggested use of ExG to generate additional robust disease detection methods for new findings. The AgirLeafNet model is setting an entirely new standard for future research endeavors. Full article

Tomato blight significantly threatened tomato yield and quality, making precise disease detection essential for modern agricultural practices. Traditional segmentation models often struggle with over-segmentation and missed segmentation, particularly in complex backgrounds and with diverse lesion morphologies. To address these challenges, we proposed Unet with Vision Mamba and ConvNeXt (VMC-Unet), an asymmetric segmentation model for quantitative analysis of tomato blight. Built on the Unet framework, VMC-Unet integrated a parallel feature-aware backbone combining ConvNeXt, Vision Mamba, and Atrous Spatial Pyramid Pooling (ASPP) modules to enhance spatial feature focusing and multi-scale information processing. During decoding, Vision Mamba was hierarchically embedded to accurately recover complex lesion morphologies through refined feature processing and efficient up-sampling. A joint loss function was designed to optimize the model’s performance. Extensive experiments on both tomato epidemic and public datasets demonstrated VMC-Unet superior performance, achieving 97.82% pixel accuracy, 87.94% F1 score, and 86.75% mIoU. These results surpassed those of classical segmentation models, underscoring the effectiveness of VMC-Unet in mitigating over-segmentation and under-segmentation while maintaining high segmentation accuracy in complex backgrounds. The consistent performance of the model across various datasets further validated its robustness and generalization potential, highlighting its applicability in broader agricultural settings. Full article

The plant-specific IDD transcription factors (TFs) are vital for regulating plant growth and developmental processes. However, the characteristics and biological roles of the IDD gene family in tomato (Solanum lycopersicum) are still largely unexplored. In this study, 17 SlIDD genes were identified in the tomato genome and classified into seven subgroups according to the evolutionary relationships of IDD proteins. Analysis of exon–intron structures and conserved motifs reflected the evolutionary conservation of SlIDDs in tomato. Collinearity analysis revealed that segmental duplication promoted the expansion of the SlIDD family. Ka/Ks analysis indicated that SlIDD gene orthologs experienced predominantly purifying selection throughout evolution. The analysis of cis-acting elements revealed that the promoters of SlIDD genes contain numerous elements associated with light, plant hormones, and abiotic stresses. The RNA-seq data and qRT-PCR experimental results showed that the SlIDD genes exhibited tissue-specific expression. Additionally, Group A members from Arabidopsis thaliana and rice are known to play a role in regulating plant shoot gravitropism. QRT-PCR analysis confirmed that the expression level of SlIDD15 in Group A was high in the hypocotyls and stems. Subcellular localization demonstrated that the SlIDD15 protein was localized in the nucleus. Surprisingly, the loss-of-function of SlIDD15 by CRISPR/Cas9 gene editing technology did not display obvious gravitropic response defects, implying the existence of functional redundant factors within SlIDD15. Taken together, this study offers foundational insights into the tomato IDD gene family and serves as a valuable guide for exploring their molecular mechanisms in greater detail. Full article

Tomato is an economically and nutritionally important crop and is vulnerable to drought. Under drought, soil microbes provide beneficial effects to plants and alleviate stress. We suggest a reconstruction of the soil microbiome using biofumigation, an organic farming method, to protect tomatoes. In this study, we treated soil in four ways with varied concentrations: biofumigation (BF0.5, BF1.0, and BF1.5), green manure treatment (GM0.5, GM1.0, and GM1.5), autoclaving (AT), and non-treatment (NT). Tomatoes were grown in each treated soil, subjected to water shortages, and were rewatered. We investigated plant phenotypes and soil properties, focused on microbial communities using the Illumina MiSeq^® System. Relative Water Content and malondialdehyde were measured as plant stress. The results showed that the 1% biofumigation treatment had 105% and 108.8% RWC during drought and after rewatering, compared to the non-treated soil. The highest concentration, the 1.5% treatment, lowered RWC due to an excess of NO₃⁻, K⁺, Ca²⁺, and decreased alpha diversity. Through PLS-PM, bacterial alpha diversity was found to be the largest factor in the increase in RWC (coefficient = 0.3397), and both biofumigant and green manure significantly increased the Shannon index and observed species. In addition, biofumigation increased beneficial functional genes (purine metabolism, pyrimidine metabolism, carbon fixation pathways, and zeatin bio-synthesis) of soil microorganisms (p value < 0.05, <0.01, >0.05, and <0.05, respectively). The 1% biofumigation treatment enriched the core five genera of the fungal network (Enterocarpus, Aspergillus, Leucothecium, Peniophora, and Wallemia) of the fungal network which might suppress the most dominant pathogen, Plectosphaerella. In conclusion, biofumigation-derived soil microbiome alterations have the potential to lower plant stress under drought. Full article

Agricultural waste is rich in bioactive molecules. When evaluating the viability of circular models for the development of health-promoting substances and final products, it is important to highlight that the industrial processing of fruits and other valuable herbal materials generates a considerable number of by-products and significant amounts of waste that contain health-promoting components. These by-products can be utilized purposefully in pharmaceuticals and related areas for the development of health-promoting products. The linear utilization of agricultural waste results in the loss of a range of valuable bioactive compounds, including polyphenols (anthocyanins, flavonoids, phenolic acids, and related compounds), antioxidants from other groups, phytosterols, tocopherols, and fatty acids. As an illustrative example, the waste materials of species belonging to the Vaccinium L. genus represent a notable secondary resource that can be purposefully applied to the development of health-promoting preparations. The fruits of these wasted herbal materials have been found to contain beneficial polyphenols, which play a pivotal role in the prevention of various chronic conditions, including precancerous conditions, inflammatory diseases, and other ailments. In addition, the fruits of blackberries, elderberries, and purple corn—which are similarly rich in anthocyanins—also provide a promising avenue for further development. Phenolic compounds suitable for recycling are also found in the by-products of sugarcane harvesting. Tomato waste contains a significant amount of lycopene, which is a valuable carotenoid. Other physiological functions may be attributed to the aforementioned by-products of fruit processing which, if used properly, can contribute to the prevention of certain diseases and improving quality of life. This review assesses the gaps in the existing literature on the development of health-promoting substances from herbal secondary materials. Full article

Localized calcium deficiency at the tomato flower end causes a physiological disorder called blossom end rot, resulting in yield losses of up to 50 percent. Fruit cracking is another physiological disorder of tomatoes that most often occurs when the movement of water and solutes to the tomato is protracted or rapid, but the underlying cause of fruit cracking is, again, calcium deficiency. Therefore, the present field experiment was conducted with the aim of increasing yield and reducing physiological disorders in tomatoes with a foliar application of calcium and micronutrients (zinc and boron). Four levels of calcium (0, 0.3, 0.6, and 0.9%), three levels of boron (0, 0.25, and 0.5%), and three levels of Zinc (0, 0.25, and 0.5%) were applied foliarly three times (starting at flowering, the 2nd application was repeated when the fruits set, and the 3rd after a period of 15 days from the fruits set). An addition of 0.6% calcium increased yield and associated traits with a decreased flower drop. Likewise, a 0.9% calcium addition increased fruit Ca content and decreased blossom end rot, fruit cracking, and Zn content. Foliar spraying with 0.25% boron (compound B) improved flowering and production while reducing flower drop and tomato fruit cracking. Similarly, an application of 0.5% B significantly increased Ca and B content with minimal blossom end rot and Zn content. Likewise, a 0.5% Zn application resulted in yield and yield-related traits with increased fruit B and Zn contents while blossom end rot, fruit cracking, and fruit Ca content were lower when 0.5% of foliar Zn was applied. Therefore, it is concluded that a foliar application of Ca, B, and Zn can be used alone or in combination to minimize the physiological disorders, increase production, and improve tomato fruit quality. Full article

In the context of agricultural modernization and intelligentization, automated fruit recognition is of significance for improving harvest efficiency and reducing labor costs. The variety of fruits commonly planted in orchards and the fluctuations in market prices require farmers to adjust the types of crops they plant flexibly. However, the differences in size, shape, and color among different types of fruits make fruit recognition quite challenging. If each type of fruit requires a separate visual model, it becomes time-consuming and labor intensive to train and deploy these models, as well as increasing system complexity and maintenance costs. Therefore, developing a general visual model capable of recognizing multiple types of fruits has great application potential. Existing multi-fruit recognition methods mainly include traditional image processing techniques and deep learning models. Traditional methods perform poorly in dealing with complex backgrounds and diverse fruit morphologies, while current deep learning models may struggle to effectively capture and recognize targets of different scales. To address these challenges, this paper proposes a general fruit recognition model based on the Multi-Scale Attention Network (MSA-Net) and a Hough Transform localization compensation mechanism. By generating multi-scale feature maps through a multi-scale attention mechanism, the model enhances feature learning for fruits of different sizes. In addition, the Hough Transform ellipse detection compensation mechanism uses the shape features of fruits and combines them with MSA-Net recognition results to correct the initial positioning of spherical fruits and improve positioning accuracy. Experimental results show that the MSA-Net model achieves a precision of 97.56, a recall of 92.21, and an [email protected] of 94.81 on a comprehensive dataset containing blueberries, lychees, strawberries, and tomatoes, demonstrating the ability to accurately recognize multiple types of fruits. Moreover, the introduction of the Hough Transform mechanism reduces the average localization error by 8.8 pixels and 3.5 pixels for fruit images at different distances, effectively improving the accuracy of fruit localization. Full article

The search for natural product-based biopesticides from endophytic fungi is an effective tool to find new solutions. In this study, we studied a pre-selected fungal endophyte, isolate YCC4, from the paleoendemism Persea indica, along with compounds present in the extract and the identification of the insect antifeedant and nematicidal ones. The endophyte YCC4 was identified as Phyllosticta sp. by molecular analysis. The insect antifeedant activity was tested by choice bioassays against Spodoptera littoralis, Myzus persicae, and Rhopalosiphum padi, and the in vitro and in vivo mortality was tested against the root-knot nematode Meloidogyne javanica. Since the extract was an effective insect antifeedant, a strong nematicidal, and lacked phytotoxicity on tomato plants, a comprehensive chemical study was carried out. Two new metabolites, metguignardic acid (4) and (-)-epi-guignardone I (14), were identified along the known dioxolanones guignardic acid (1), ethyl guignardate (3), guignardianones A (5), C (2), D (7), and E (6), phenguignardic acid methyl ester (8), the meroterpenes guignardone A (9) and B (10), guignarenone B (11) and C (12), (-)-guignardone I (13), and phyllomeroterpenoid B (15). Among these compounds, 1 and 4 were effective antifeedants against S. littoralis and M. persicae, while 2 was only active on the aphid M. persicae. The nematicidal compounds were 4, 7, and 8. This is the first report on the insect antifeedant or nematicidal effects of these dioxolanone-type compounds. Since the insect antifeedant and nematicidal activity of the Phyllosticta sp. extract depend on the presence of dioxolanone components, future fermentation optimizations are needed to promote the biosynthesis of these compounds instead of meroterpenes. Full article

This paper investigates the economic feasibility and benefits of introducing genetically modified (GM) crops into Lebanon’s agricultural sector. The methodology combines a rigorous cost–benefit analysis with qualitative insights from local farmers and agricultural scientists to ensure relevance to Lebanon’s unique agricultural context. Through this study, we identified tomatoes and potatoes as the most suitable crops for GM implementation. The findings indicate that GM tomatoes could increase net income by USD 10,000 per hectare in the short term and USD 50,000 over five years. These economic benefits are primarily driven by higher yields and reduced pesticide costs. This study emphasizes the necessity of a holistic approach, including financial support, infrastructure development, farmer education, and robust market access strategies, to maximize the potential of GM crops. This research provides a strategic framework for leveraging GM technology to address Lebanon’s agricultural challenges, promoting sustainable practices, enhancing food security, and ensuring long-term economic stability. By integrating local context and stakeholder perspectives, this paper offers a unique and actionable pathway for successful GM crop implementation in Lebanon. Full article

Plant disease identification is a crucial issue in agriculture, and with the advancement of deep learning techniques, early and accurate identification of plant diseases has become increasingly critical. In recent years, the rise of vision transformers has attracted significant attention from researchers in various vision-based application areas. We designed a model with an encoder–decoder architecture to efficiently classify plant diseases using a transfer learning approach, which effectively recognizes a large number of plant diseases in multiple crops. The model was tested on the “PlantVillage”, “FGVC8”, and “EMBRAPA” datasets, which contain leaf information from crops such as apples, soybeans, tomatoes, and potatoes. These datasets cover diseases caused by fungi, including rust, spot, and scab, as well as viral diseases such as leaf curl. The model’s performance was rigorously evaluated on datasets, and the results demonstrated its high accuracy. The model achieved 99.9% accuracy on the “PlantVillage” dataset, 97.4% on the “EMBRAPA” dataset, and 91.5% on the “FGVC8” dataset, showcasing its competitiveness with other state-of-the-art models. This study provides a robust and reliable solution for plant disease classification and contributes to the advancement of precision agriculture. Full article

Tetranychus urticae (Acari: Tetranychidae) is a widespread and serious mite pest that infests tomato plants and causes economic losses worldwide. We investigated the acaricidal efficacy of two isolates of entomopathogenic fungi (EPF) Metarhizium robertsii (WG-7) and Beauveria bassiana (WG-12) alone and in combination with abamectin when applied topically to tomato leaf discs in the laboratory against T. urticae. We also evaluated the establishment and proliferation of T. urticae mite life stages on tomato plants in the greenhouse after application of each of the above treatments. The combination of abamectin with each EPF caused 100% mortality in T. urticae immatures after 2 days while each EPF or abamectin alone caused moderate mortality, not exceeding 74.2% 3 days post-exposure. Complete (100%) mortality of adults was observed after 5 days in leaf discs treated with M. robertsii plus abamectin whereas B. bassiana plus abamectin caused 100% mortality after 7 days. The mean number of eggs, emerged immatures, and adults were significantly reduced on both sides of the leaves (i.e., abaxial and adaxial sides) after using the combined application of M. robertsii or B. bassiana plus abamectin, compared to abamectin alone and controls. Our results reveal that the acaricidal efficacy of abamectin combined with either EPF was significantly better in managing the T. urticae life stages than either treatment alone under greenhouse conditions. Full article