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During their growth, tomatoes are impacted by several illnesses and pests. The most crucial step in effectively assisting vegetable farmers in increasing tomato yield is combating diseases by the precise and early identification of these problems. Utilizing deep learning for detecting objects saves time, effort and allows real-time judgment, thereby reducing the substantial losses in the yield. This study uses the Open-source dataset of Tomato Leaf Diseases under natural conditions and trains them with multiple YOLO models(YOLOv5, YOLOv7) for the characteristics of tomato diseases' images. Transfer learning is subsequently applied to augment the adaptability of these YOLO models, while the integration of a YOLO-YOLO cascaded ensembling technique enhances multiscale feature detection. This approach significantly elevates the accuracy of disease detection, effectively pinpointing both the location and classification of tomato leaf diseases. The prominent YOLO v5-v7 ensemble transfer learning model, when evaluated, exhibits noteworthy performance metrics during training. It achieves a mean average precision (mAP) of 98.8% at a detection threshold of 0.5 and a mAP of 98.7% at a more stringent threshold of 0.95. Additionally, the model demonstrates high precision at 95.8% and a strong recall rate of 96.8%. [ABSTRACT FROM AUTHOR]

One of the structure of economic development is farming involved with niche skills and technologies. More than 25% of total tomato crop production is lost annually because of various plant diseases. The farmers who grow tomatoes face huge economic losses every year, often due to various diseases that can infect tomatoes. The work carried aims to identify these diseases early and accurately, so can be informed well before the damage, which can minimize the waste. Convolution neural network (CNN) techniques, subset of Machine Learning (ML) are demonstrated to be very efficient for image datasets of various complex problems. Specifically, the work is focused to build and train a customized CNN model to classify the leaf images. Here we are using advanced deep learning architectures AlexNet and VGGNet-16 to recognize various diseases from image datasets collected from agricultural sources to develop models. Comparative analysis has resulted an improvement in accuracy by using deep learning architectures and CNN models. These models are responsible for helping to solve distribution problems, checking whether a leaf is healthy or not and, if so, what disease it is. Disease detected early and treated quickly minimizes the cost and improves the quality and quantity of the crop produced. [ABSTRACT FROM AUTHOR]

During their growth, tomatoes are impacted by several illnesses and pests. The most crucial step in effectively assisting vegetable farmers in increasing tomato yield is combating diseases by the precise and early identification of these problems. Utilizing deep learning for detecting objects saves time, effort and allows real-time judgment, thereby reducing the substantial losses in the yield. This study uses the Open-source dataset of Tomato Leaf Diseases under natural conditions and trains them with multiple YOLO models(YOLOv5, YOLOv7) for the characteristics of tomato diseases' images. Transfer learning is subsequently applied to augment the adaptability of these YOLO models, while the integration of a YOLO-YOLO cascaded ensembling technique enhances multiscale feature detection. This approach significantly elevates the accuracy of disease detection, effectively pinpointing both the location and classification of tomato leaf diseases. The prominent YOLO v5-v7 ensemble transfer learning model, when evaluated, exhibits noteworthy performance metrics during training. It achieves a mean average precision (mAP) of 98.8% at a detection threshold of 0.5 and a mAP of 98.7% at a more stringent threshold of 0.95. Additionally, the model demonstrates high precision at 95.8% and a strong recall rate of 96.8%. [ABSTRACT FROM AUTHOR]

One of the structure of economic development is farming involved with niche skills and technologies. More than 25% of total tomato crop production is lost annually because of various plant diseases. The farmers who grow tomatoes face huge economic losses every year, often due to various diseases that can infect tomatoes. The work carried aims to identify these diseases early and accurately, so can be informed well before the damage, which can minimize the waste. Convolution neural network (CNN) techniques, subset of Machine Learning (ML) are demonstrated to be very efficient for image datasets of various complex problems. Specifically, the work is focused to build and train a customized CNN model to classify the leaf images. Here we are using advanced deep learning architectures AlexNet and VGGNet-16 to recognize various diseases from image datasets collected from agricultural sources to develop models. Comparative analysis has resulted an improvement in accuracy by using deep learning architectures and CNN models. These models are responsible for helping to solve distribution problems, checking whether a leaf is healthy or not and, if so, what disease it is. Disease detected early and treated quickly minimizes the cost and improves the quality and quantity of the crop produced. [ABSTRACT FROM AUTHOR]

Copyright of Tecnología y Ciencias del Agua is the property of Instituto Mexicano de Tecnologia del Agua (IMTA) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Ciencia en su PC is the property of Instituto de Informacion Cientifica y Tecnologica and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

As non-chemical methods are not always effective to manage pests, the search for chemical solutions that efficiently combat pest species, while safeguarding the environment and beneficial organisms, is essential for advancing effective Integrated Pest Management (IPM) programs. Here, we investigated the efficacy of commercially available pesticides in managing Nesidiocoris tenuis , an emerging threat in the cultivation of tomato in Northwest Europe, while preserving the biocontrol agent Macrolophus pygmaeus. Fourteen pesticides were assessed for their residual toxicity on adults and nymphs in laboratory experiments, after which the most promising candidate was evaluated in cages and semi-commercial greenhouse conditions. Among the pesticides tested, chlorantraniliprole, hexythiazox, tebufenozide and azadirachtin had minimal impact on both insect species, with less than 37.5% corrected mortality. Pirimicarb, sulfoxaflor, acetamiprid, flupyradifurone, thiacloprid, tebufenpyrad, spinosad, indoxacarb, and a mixture of pyrethrins and piperonyl butoxide were harmful to N. tenuis , but also affected M. pygmaeus (up to 100% mortality). Emamectin benzoate was moderately effective to N. tenuis adults (87%) and nymphs (80%), but had limited effects on M. pygmaeus. Application of emamectin benzoate at the maximum recommended concentration resulted in a significantly higher mortality of N. tenuis adults (85%) compared to M. pygmaeus (53%) in cage experiments, while it significantly reduced N. tenuis nymphs and the number of necrotic rings in a greenhouse experiment. Emamectin benzoate emerges as a promising candidate for inclusion in IPM programs. However, further research is necessary to determine its compatibility with other management strategies for controlling N. tenuis and other tomato pests. Laboratory, cage and greenhouse trials indicate the potential of emamectin benzoate to be included in IPM programs to control Nesidiocoris tenuis , with less adverse effects on the beneficial Macrolophus pygmaeus. [Display omitted] • Nesidiocoris tenuis is an emerging pest in the cultivation of tomato in NW Europe. • 14 pesticides tested for their toxicity on N. tenuis and Macrolophus pygmaeus. • Emamectin benzoate most promising, killing N. tenuis and less affecting M. pygmaeus. • Higher mortality of N. tenuis compared to M. pygmaeus in cage experiments. • Dose-dependent reduced N. tenuis population and less necrotic rings in greenhouse. [ABSTRACT FROM AUTHOR]

Water containing ultrafine/nano bubbles (UFBs) promoted the growth of tomato (Solanum lycopersicum) in soil damaged by cultivation of tomato in the previous year or bacterial wilt-like disease and also promoted the growth of lettuce (Lactuca sativa) when lettuce was grown in the soil damaged by repeated cultivation of lettuce. On the other hand, UFB supply did not affect plant growth in rock wool or healthy soil. Furthermore, the growth of lettuce was not affected by UFB water treatment in the soil damaged by the cultivation of tomato. UFB water partly suppressed the growth of the pathogen of bacteria wilt disease, Ralstonia solanacearum in vitro. These data suggest that UFB water is effective to recover the plant growth from soil damage. [ABSTRACT FROM AUTHOR]

Copyright of Progress in Plant Protection is the property of Institute of Plant Protection and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Revista de la Academia Colombiana de Ciencias Exactas, Físicas y Naturales is the property of Academia Colombiana de Ciencias Exactas, Fisicas y Naturales and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

At present, consumer concern about the impact of food production on the environment is driving increased demand for high quality and healthy tomatoes. However, the yield of processing tomatoes in organic systems are generally lower than that in conventional systems and only a limited number of genotypes suitable for low input or organic systems are available for farmers. The technique of grafting commercial genotypes onto selected rootstocks offers a faster alternative to the classic breeding process. Therefore, in this study, the use of the interspecific rootstock RS01658654 (RT1) was assessed, aiming to improve the marketable yield of processing tomatoes grown in an organic cropping system. The non-commercial processing tomato genotype TC266 was grafted onto the interspecific rootstock RT1 and the plants were then grown under organic conditions. In two growing seasons, morphological, physiological and agronomic performances of grafted processing tomato plants were compared to non-grafted and self-grafted plants. TC226 grafted onto RT1 had a higher number of flowers and leaves compared with the non-grafted and the self-grafted plants. In addition, the marketable yield (significant in 2017 only), the number of fruits and the fruit dry weight were higher for plants grown on the interspecific rootstock RT1, without affecting the quality of the fruit. The results of this study showed that the use of the interspecific rootstock RT1 could provide a good option for improving the production of processing tomatoes in organic farming. [ABSTRACT FROM AUTHOR]

Copyright of Tecnología y Ciencias del Agua is the property of Instituto Mexicano de Tecnologia del Agua (IMTA) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The cultivation of vegetables under tunnels is considered a promising alternative to the use of chemicals to manage insect pests. However, higher temperatures under covers hinder their use in warm regions. We investigated the effect of high tunnels covered with plastic film or small or large mesh insect-nets on the performances of tomato plants in Tanzania over two years. Control plants were grown in open field plots. The temperature during the day was 4°C and 15°C higher under the nets and under the plastic than in the open. The total number of insect pests per plant over the season was generally lower under the nets than in open, with the small mesh better than the large mesh. The nets promoted plant growth compared with the open plots, with higher leaf area, plant dry weight, and plant height. Marketable yields were lower in open (2.7 and 10.8 t.ha−1), intermediate under the large mesh (7.9 and 38.4 t.ha−1), and high under the small mesh (16.8 and 50.1 t.ha−1). Higher productivity under the nets was associated with better plant growth and fewer insects than in the open. Contrasting results could be obtained in warmer climates because of the increase in temperatures recorded under tunnels. [ABSTRACT FROM AUTHOR]

Tomato is the most popular crop in the world and in every kitchen, it is found in different forms irrespective of the cuisine. After potato and sweet potato, it is the crop which is cultivated worldwide. India ranked 2 in the production of tomato. However, the quality and quantity of tomato crop goes down due to the various kinds of diseases. So, to detect the disease a deep learning-based approach is discussed in the article. For the disease detection and classification, a Convolution Neural Network based approach is applied. In this model, there are 3 convolution and 3 max pooling layers followed by 2 fully connected layer. The experimental results shows the efficacy of the proposed model over pre-trained model i.e. VGG16, InceptionV3 and MobileNet. The classification accuracy varies from 76% to 100% with respect to classes and average accuracy of the proposed model is 91.2% for the 9 disease and 1 healthy class. [ABSTRACT FROM AUTHOR]

Copyright of Progress in Plant Protection is the property of Institute of Plant Protection and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Tomato is an economically important vegetable crop which is attacked heavily by insect pests leading to reduction of yield and quality of the fruits. Field experiments were carried out to investigate the dissipation of methomyl (a common insecticide) used mainly on tomato fruits. LC-MS/MS coupled with the QuEChERS method were used for the determination of methomyl. The results showed that the recovery using matrix-matched standards ranged from 87.8 to 101.3%, with relative standard deviation of 2.5 to 7.5%. Kinetics equation, Log R = log R0 - 0.434 Kt, was used to calculate the rate of degradation in tomato, soil and water. Residue half-life calculated using kinetic rate ranged from 1.95 to 1.63 days in tomato and soil, respectively. From the results it was concluded that tomato fruits can be safely harvested for consumption after 15 days of application based on estimated preharvest interval (PHI). It is advisable to re-estimate the PHI regularly owing to data from the EU and Codex. [ABSTRACT FROM AUTHOR]

In the present study, the density of tomato leafminer (TLM), Tuta absoluta (Meyrick), the diversity and abundance of its predators and parasitoids, and crop yields were determined in a monoculture of tomato, Lycopersicon esculentum (Mill.), (T), a monoculture of sainfoin, Onobrychis viciifolia Scop., (S) and intercropping tomato and sainfoin in four configurations of rows, namely, one row of tomato and two rows of sainfoin (1T: 2S), two rows tomato and two rows sainfoin (2T: 2S), three rows tomato and two rows sainfoin (3T: 2S), and four rows tomato and two rows sainfoin (4T: 2S). These experiments were conducted on a farm during 2016 and 2017 in Iran. Intercrops caused a significant (P < 0.05) reduction in the number of TLM eggs, larvae and galleries and promoted a marked increase in the values of Shannon diversity index (H′) for the species composition of TLM predators. Orius niger (Wolff) and Nesidiocoris tenuis (Reuter) were the dominant TLM predators recorded. Moreover, larval parasitoids Bracon hebetor Say and Neochrysocharis formosus (Westwood) were found to be more abundant relative to the other parasitoids in both 2016 and 2017. The larval parasitism rate was higher in intercrops than in tomato monoculture. On the other hand, the land equivalent ratio (LER) in intercrops was greater than one specifically, in 1T:2S (1.22 in 2016 and 1.17 in 2017) and 2T:2S (1.22 in 2016 and 1.19 in 2017). These findings demonstrated that intercropping tomato with sainfoin particularly, in 1T: 2S and 2T: 2S row ratios were effective in controlling T. absoluta in tomato fields. Intercropping of tomato with sainfoin in the four patterns of 1T:2S, 2T:2S, 3T:2S, and 4T:2S (row ratios) were evaluated on the control of tomato leafminer (TLM), Tuta absoluta (Meyrick). • Intercrops especially 1T:2S and 2T:2S reduced the number of TLM eggs, larvae and galleries compared with tomato monoculture for the two tomato seasons. • The values of Shannon diversity index (H′) for the species composition of TLM predators in the four intercrops were more than tomato monoculture. • The lowest number of O. niger and mirid bugs were observed in tomato monoculture and their abundance became greater, among intercrops, with increasing in the ratio of sainfoin to tomato rows. • The larval parasitism rate was higher in intercrops especially 1T:2S and 2T:2S than in tomato monoculture. [ABSTRACT FROM AUTHOR]

Abstract A novel disease of tomato (Solanum lycopersicum L.) was observed in the Southeast Anatolia Region of Turkey. Symptoms were blight of all aerial parts of the plant, including stems, branches, leaves, petioles, flowers and fruits, defoliation, root rot, inner stem necrosis, and plant death. The disease was found in 13.9% of surveyed fields, with an incidence varying from 3% to nearly 75% (average 21.2%) of the plants in symptomatic fields. The average severity of blight on stem in fields with the symptomatic plant surveyed was 1.4%. A Botryosphaeriaceae species, identified as Neoscytalidium dimidiatum (Penz.) Crous & Slippers using morphological and cultural features, was consistently isolated from symptomatic roots, inner stems, and blighted leaves, shoots, stems, fruits and flowers. The partial nucleotide sequence data for three gene loci, including nuclear rDNA internal transcribed spacer (ITS), large subunit (LSU) genes and the translation elongation factor 1-alpha (TEF-1α), confirmed the morphological identification. Furthermore, sequence data of actin genes from N. dimidiatum was, for the first time, deposited to the GenBank. Koch's postulates were fulfilled by testing the susceptibility of different tomato tissues (leaves, stems, inner stems and roots of tomato seedlings, and detached tomato fruits and flowers) to N. dimidiatum inoculation. To our knowledge, this is the first report of N. dimidiatum on tomato. Highlights • A newly-isolated fungus from tomato caused significant loss in tomato cultivation areas in the Southeast Anatolia Region of Turkey. • The agent was identified as Neoscytalidium dimidiatum using morphological and molecular criteria. • N. dimidiatum was isolated from all aerial parts and roots of tomato and its pathogenicity was verified on/in various plant parts. • The pathogen was reported for the first time on tomato and it has great potential to spread out in tomato cultivation areas. [ABSTRACT FROM AUTHOR]

Grafting tomato (Solanum lycopersicum L.) onto disease resistant rootstocks has grown in use in North America over the past two decades. Rootstocks have traditionally been bred and used for their resistance packages to numerous soil-borne diseases but some rootstocks appear to improve scion yield and vigour in comparison to non-grafted plants in conditions lacking disease challenge. In this study, the tomato rootstocks 'Maxifort' or 'RST-106' were used to determine if vigour improved 'Tribute' scion traits, especially yield, and if marketable yield could be maintained in grafted plants at reduced nitrogen fertiliser inputs. Plants were grown in an open-field plasticulture production system at five rates of nitrogen from 0 kg ha−1 to 224 kg ha−1 applied via drip irrigation. Marketable yield was significantly affected by nitrogen rate and rootstock in both years. 'Tribute' grafted onto 'Maxifort' rootstock had the greatest, most consistent impact and increased marketable yield 15% and 30% in 2013 and 2014, respectively, compared to the non-grafted 'Tribute'. Our findings suggest that some rootstocks can increase scion growth and yield but do not differentially respond to decreased nitrogen rates. [ABSTRACT FROM AUTHOR]

Chromium (Cr) pollution caused by the discharge of industrial wastewater into rivers poses a significant threat to the environment, aquatic and human life, as well as agricultural crops irrigated by these rivers. This paper employs artificial intelligence (AI) to introduce a new framework for modeling the fate, transport, and estimation of Cr from its point of discharge into the river until it is absorbed by agricultural products. The framework is demonstrated through its application to the case study River, which serves as the primary water resource for tomato production irrigation in Mashhad city, Iran. Measurements of Cr concentration are taken at three different river depths and in tomato leaves from agricultural lands irrigated by the river, allowing for the identification of bioaccumulation effects. By employing boundary conditions and smart algorithms, various aspects of control systems are evaluated. The concentration of Cr in crops exhibits an accumulative trend, reaching up to 1.29 µg/g by the time of harvest. Using data collected from the case study and exploring different scenarios, AI models are developed to estimate the Cr concentration in tomato leaves. The tested AI models include linear regression (LR), neural network (NN) classifier, and NN regressor, yielding goodness-of-fit values (R 2 ) of 0.931, 0.874, and 0.946, respectively. These results indicate that the NN regressor is the most accurate model, followed by the LR, for estimating Cr levels in tomato leaves. • Chromium fate and transport is evaluated by experimental practices and estimated by AI. • A sensitive analysis is carried out for effective parameters in warm and cold seasons. • Machine learning computations are applied for Cr bioaccumulation in tomato. • The NN regressor and LR have the best goodness-of-fit for estimating Cr in tomato leaves. [ABSTRACT FROM AUTHOR]

Tomato yellow leaf curl disease (TYLCD), caused by viruses of the genus Begomovirus (family Geminiviridae ), is a major constraint to tomato production in Oman. The development of tomato lines with resistance to the viruses that cause TYLCD is an important target for tomato breeding programs worldwide. The study assessed fourteen tomato inbred lines harbouring one or more of five TYLCD resistance genes ( Ty-1/Ty-3, Ty-2, ty-5 ) for resistance against the TYLCD complex in Oman by screening in the field for TYLCD incidence, severity and viral load over two consecutive years. All Ty gene-containing lines performed better than the susceptible controls, with lower disease incidence, severity and viral DNA load. Lines harbouring Ty -2 alone or in combination with other Ty genes performed poorly. A line harbouring only ty -5 performed the best, displaying no symptoms and having the lowest viral load. The presence of a disease enhancing betasatellite in lines harbouring Ty -2 suggests that this may compromise Ty -2 resistance. The results indicate that the Ty genes are useful in Oman as well as other Gulf Cooperation Council countries and should be incorporated into local varieties. [ABSTRACT FROM AUTHOR]

The article discusses the history of the greenhouse tomato industry in Ohio from 1937 to 1982. Topics discussed include the spread of the fungal disease Fusarium wilt and the Tomato and Tobacco Mosaic Viruses across the U.S., which threatened the livelihood of tomato growers; the breeding of tomatoes resistant to both diseases by Leonard Jay Alexander in 1939 and 1942, respectively; and Alexander's election to the Ohio Academy of Science and the American Phytopathological Society.

Urbanisation is transforming human societies in many ways. Besides bringing benefits to people in cities, it also has negative impacts such as food security. One way to meet the challenge is urban agriculture; however, traditional agricultural practices are not suitable within urban areas due to limited availability of land. Therefore, the alternative option is to grow crops inside or on top of buildings, e.g. building integrated agriculture (BIA). But, there is limited research and information available for designers and planners to design such buildings. The presented research project bridges the gap between agriculture and architecture by proposing a building integrated agriculture information modelling tool in integration with Building information modelling. The plugin tool has data on plants’ requirements and automatic response to environmental factors. Environmental factors include temperature, light, water, nutrients, air, humidity, spacing and support. In this paper, seasonal tomato is selected as a reference crop, and the impact of environment (temperature, light, water, nutrients and spacing) on its health is discussed and simulated for germination stage. The undertaken project contributes to the concept of BIA and BIM maturity level, which would help to design an optimum environment building for plants. [ABSTRACT FROM AUTHOR]

Phytoremediation is a low cost technology based on the use of plants to remove a wide range of pollutants from the environment, including the insecticide DDT. However, some pollutants are known to enhance generation of reactive oxygen species (ROS), which can generate toxic effects on plants affecting the phytoremediation efficiency. This study aims to analyze the potential use of antioxidant responses as a measure of tolerance to select plants for phytoremediation purposes. Tomato and zucchini plants were grown for 15 days in soils contaminated with DDTs (DDT + DDE + DDD). Protein content, glutathione-S-transferase (GST), glutathione reductase (GR), glutathione peroxidase (GPx) and catalase (CAT) activities were measured in plant tissues. Exposure to DDTs did not affect protein content or CAT activity in any of the species. GST, GR and GPx activity showed different responses in exposed and control tomato plants. After DDTs exposure, tomato showed increased GR and GPX activity in stems and leaves, respectively, and a decrease in the GST activity in roots. As no effects were observed in zucchini, results suggest different susceptibility and/or defense mechanisms involved after pesticide exposure. Finally, both species differed also in terms of DDTs uptake and translocation. The knowledge about antioxidant responses induced by pesticides exposure could be helpful for planning phytoremediation strategies and for the selection of tolerant species according to particular scenarios. [ABSTRACT FROM AUTHOR]

Copyright of Brazilian Journal of Biology is the property of Instituto Internacional de Ecologia and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This paper presents a novel climate decision support system for tomatoes in high tunnels using fuzzy logic and adaptive neuro-fuzzy inference system. Three climate decision support systems are developed for high tunnels using fuzzy logic. First climate decision support system takes five inputs-temperature, relative humidity, solar radiations, wind velocity, and weather condition-and controls four outputs-tunnel's temperature, tunnel's humidity, fan speed, and shading. Second climate decision support system takes three inputs-temperature, solar radiations, and weather condition-and controls artificial sunlight. Third climate decision support system takes air quality index and controls air purification. We develop and implement the two main algorithms for climate control systems, one algorithm is for fuzzy logic climate decision support system, and other one is for neuro-fuzzy climate control system. We compute time complexity of both algorithms. We use software MATLAB for showing average error between calculated and targeted outputs. We also perform optimization of fuzzy membership functions using particle swarm optimization method and evaluate its results in MATLAB. Our generated results are very much precise and satisfied the desired range of outputs. [ABSTRACT FROM AUTHOR]

A previously developed grafting strategy was applied to graft a commercial cultivated variety of tomato (Solanum lycopersicumL. H-2274) ontoNicotiana rusticaL. (cv. Hasankeyf) andNicotiana tabacumL. (cv. Samsun) rootstocks. Higher growth and leafing, and earlier flower onset were found in grafted than in non-grafted and self-grafted plants. Significant 22.7 and 34.3% increases in fruit yield were obtained with Samsun and Hasankeyf rootstocks, respectively. Leaves of tobacco-grafted plants had significantly elevated levels of nicotine, and highest leaf nicotine levels were measured at 90 days after transplantation (DAT). Leaves from upper sampling levels of plants had significantly less nicotine independent of the sampling DAT. Fruits of all tobacco-grafted plants had significantly higher amounts of nicotine than non-grafted scions. Nicotine levels were higher in fruits sampled from lower parts of the plants for different harvest DAT and rootstocks. Increases in leaf nicotine concentration of tobacco-grafted plants were higher when compared with that of fruit. Grafting tomato onto tobacco rootstocks is a practical and feasible application for different tobacco–tomato unions. [ABSTRACT FROM PUBLISHER]

This study determined the fate of triclosan, a prevalent wastewater contaminant in recycled waters and surface streams, when soil and crop plants were irrigated at environmentally relevant concentrations. Soil triclosan concentrations were monitored in an 8-wk and in a 16-wk study without plants to determine triclosan degradation. Onion (Allium oschaninii O. Fedtsch.) and tomato (Solanum lycopersicum L.) were assessed for growth and triclosan accumulation at four levels of triclosan exposure (0, 0.015, 0.15, and 1.5 µg L-1) in irrigation waters within ranges of those found in recycled waters and associated receiving streams. Onions were grown for 8 wk and tomatoes were grown for 8 wk (short-term study) and 12 wk (long-term study) in potting soil. Soil triclosan concentrations increased (5-fold) with triclosan levels applied to soils alone. With repeated application, the half-life of triclosan was 18 d, with low-level accumulation in soil. Bioaccumulation of triclosan was observed in all edible portions of onions (115-435 ng g-1), primarily in bulbs, with no discernible impact on biomass. In both short- and long-term tomato studies, triclosan translocated to shoots and fruits (approaching a translocation factor of 1) at the highest level examined. Even at low triclosan concentrations typically found in recycled waters and receiving streams, agricultural irrigation presents an additional exposure route for organic contaminants to humans via commercial crops. Our study indicates that bulb crops, in particular, would likely accumulate high levels of triclosan. However, concentrations detected in both onions and tomato fruits determined here are below current human exposure limits. [ABSTRACT FROM AUTHOR]