Search results for: leaf disease

Authors: Muhammad Yousaf Ali, Shahid Mansoor, Javeria Qazi, Imran Amin, Musarrat Shaheen

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Cotton leaf curl disease (CLCuD) is the major threat to the cotton crop and is transmitted by whitefly (Bemisia tabaci). Since multiple begomoviruses and associated satellites are involved in CLCuD, approaches based on the concept of broad-spectrum resistance are essential for effective disease control. Gro-El and G5 are two proteins from whitefly endosymbiont and M13 bacteriophage origin, respectively. Gro-El encapsulates the virus particle when it enters the whitefly and protects the virus from the immune system of the whitefly as well as prevents viral expression in it. This characteristic of Gro-El can be exploited to get resistance against viruses if expressed in plants. G5 is a single-stranded DNA binding protein, expression of which in transgenic plants will stop viral expression on its binding with ssDNA. The use of tissue-specific promoters is more efficient than constitutive promoters. Transgenics of Nicotiana benthamiana for Gro-El under constitutive promoter and Gro-El under phloem specific promoter were made. In comparison to non-transgenic plants, transgenic plants with Gro-El under NSP promoter showed promising results when challenged against cotton leaf curl Multan virus (CLCuMuV) along with cotton leaf curl Multan beta satellite (CLCuMB), cotton leaf curl Khokhran virus (CLCuKoV) along with cotton leaf curl Multan beta satellite (CLCuMB) and Pedilenthus leaf curl virus (PedLCV) along with Tobacco leaf curl beta satellite (TbLCB).

Keywords: cotton leaf curl disease, whitefly, endosymbionts, transgenic, resistance

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Authors: Gian Bhushan, Vinkel Arora, M. L. Aggarwal

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The objective of the present study is to determine better eye end design of a mono leaf spring used in light motor vehicle. A conventional 65Si7 spring steel leaf spring model with standard eye, casted and riveted eye end are considered. The CAD model of the leaf springs is prepared in CATIA and analyzed using ANSYS. The standard eye, casted, and riveted eye leaf springs are subjected to similar loading conditions. The CAE analysis of the leaf spring is performed for various parameters like deflection and Von-Mises stress. Mass reduction of 62.9% is achieved in case of riveted eye mono leaf spring as compared to standard eye mono leaf spring for the same loading conditions.

Keywords: CAE, leaf spring, standard, casted, riveted eye

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Authors: P. S. Akami, H. Nahunnaro, A. Zubainatu

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Cercospora leaf spot (Cercospora sesami. Zimm) has been identified as one of the most prevalent diseases, posing serious constraints to sesame production in producing areas. Two sets of experiments were carried out. The first and second experiments were conducted in the Modibbo Adama University of Technology Yola at the Crop Production and Horticulture and Plant Science Departments, respectively. The field experiment was carried out using a Randomized Complete Block Design and was replicated three times on a plot size of 4m x 5m with four sesame varieties and three Mancob-M fungicide levels (0g, 2g and 4g) to give a total of Twelve treatments. The laboratory experiment involved the isolation of the pathogens from diseased leaves with symptoms of Cercospora leaf spot, which was identified as Cercospora sesami. Data collected were subjected to analysis of variance for a randomized complete block design using SAS (1999) statistical package. The treatment means that are significantly different were separated using the Least Significant Difference at P=0.05. The result revealed that 4g Mancob M recorded the lowest mean value for disease incidence and severity at 8WAS, which was 90.30% and 35.60%, respectively, while the control (0g) recorded the highest mean value for disease incidence and severity at 90.30% and 59.80% respectively. Ex-Sudan recorded the lowest value of 720 kg/ha, while NCRIBEN 03 recorded the highest yield of 834 kg/ha-¹. For the concentrations, 2g recorded a higher yield of 843 kg/ha-¹ followed by 0g, which recorded 765 kg/ha-¹. Conclusively, Cercospora leaf spot of sesame was found to be prevalent. E8 has a higher resistance to the disease, while NCRIBEN 03 tends to be more susceptible. It is therefore recommended that further trials should be carried out using different varieties in different locations.

Keywords: disease, evaluation, prevalence, treatment, resistance

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Authors: Muhammad Mohsan, Mehboob Ur-Rahman, Sana Zulfiqar, Shumila Ashfaq

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Brown Rust (Puccinia triticina), also known as leaf rust, pose a serious threat to wheat cultivation in the world. Nine candidate wheat lines/mutants were subjected to rust inoculation, lodging and aphid population in vivo conditions. Four lines/mutants (E-284, E-505, 2008-6 MR and 2008-14MR) were found resistant to leaf rust attack. Two lines (PGMB 15-29 and 2011-1 MR) displayed moderately resistant reactions against the disease. Three lines/mutants were depicted as susceptible to leaf rust. The lowest population of aphids, i.e., 16.67, was observed on 2008-14MR. Three lines/mutants (NN1-47, NN1-89 and PGMB 15-29) were found under zero level of lodging. The presence and absence of different leaf rust-resistant genes like Lr13, Lr34, Lr46 and Lr67 were assessed with the help of molecular markers. All the wheat lines/mutants were found loaded with leaf rust-resistant genes such as Lr13 and Lr 34, while Lr46 and Lr67 were found in 66% of wheat lines/mutants. The resistant source can be exploited in the breeding program to develop rust, aphid and lodging with race-nonspecific resistant wheat variety.

Keywords: wheat, leaf rust, lodging, aphid

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Authors: Kayne Uriel K. Rodrigo, Jerriane Hillary Heart S. Marcial, Samuel C. Brillo

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Rice leaf diseases significantly impact yield production in rice-dependent countries, affecting their agricultural sectors. As part of precision agriculture, early and accurate detection of these diseases is crucial for effective mitigation practices and minimizing crop losses. Hence, this study proposes an enhancement to the Convolutional Neural Network (CNN), a widely-used method for Rice Leaf Disease Image Classification, by incorporating MobileViTV2—a recently advanced architecture that combines CNN and Vision Transformer models while maintaining fewer parameters, making it suitable for broader deployment on edge devices. Our methodology utilizes a publicly available rice disease image dataset from Kaggle, which was validated by a university structural biologist following the guidelines provided by the Philippine Rice Institute (PhilRice). Modifications to the dataset include renaming certain disease categories and augmenting the rice leaf image data through rotation, scaling, and flipping. The enhanced dataset was then used to train the MobileViTV2 model using the Timm library. The results of our approach are as follows: the model achieved notable performance, with 98% accuracy in both training and validation, 6% training and validation loss, and a Receiver Operating Characteristic (ROC) curve ranging from 95% to 100% for each label. Additionally, the F1 score was 97%. These metrics demonstrate a significant improvement compared to a conventional CNN-based approach, which, in a previous 2022 study, achieved only 78% accuracy after using 5 convolutional layers and 2 dense layers. Thus, it can be concluded that MobileViTV2, with its fewer parameters, outperforms traditional CNN models, particularly when applied to Rice Leaf Disease Image Identification. For future work, we recommend extending this model to include datasets validated by international rice experts and broadening the scope to accommodate biotic factors such as rice pest classification, as well as abiotic stressors such as climate, soil quality, and geographic information, which could improve the accuracy of disease prediction.

Keywords: convolutional neural network, MobileViTV2, rice leaf disease, precision agriculture, image classification, vision transformer

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Authors: S. Lecheb, A. Chellil, H. Mechakra, S. Attou, H. Kebir

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Although leaf springs are one of the oldest suspension components they are still frequently used, especially in commercial vehicles. Being able to capture the leaf spring characteristics is of significant importance for vehicle handling dynamics studies. The main function of leaf spring is not only to support vertical load but also to isolate road induced vibrations. It is subjected to millions of load cycles leading to fatigue failure. It needs to have excellent fatigue life. The objective of this work is its use of Abaqus software to locate the most stressed areas and predict the areas in which it occurs in fatigue and crack of leaf spring and calculate the stress and frequencies of this model.

Keywords: leaf spring, crack, stress, natural frequencies

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Authors: Lidia Kowalska, Edward Arseniuk

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Glume and leaf blotch is a disease of wheat caused by necrotrophic fungus Parastagonospora nodorum. It is a serious pathogen in many wheat-growing areas throughout the world. Use of resistant cultivars is the most effective and economical means to control the above-mentioned disease. Plant breeders and pathologists have worked intensively to incorporate resistance to the pathogen in new cultivars. Conventional methods of breeding for resistance can be supported by using the biotechnological ones, i.e., somatic embryogenesis and androgenesis. Therefore, an effort was undertaken to compare genetic variation in P. nodorum resistance among winter wheat somaclones, dihaploids and conventional varieties. For the purpose, a population of 16 somaclonal and 4 dihaploid wheat lines from six crosses were used to assess their resistance to P. nodorum under field conditions. Lines were grown in disease-free (fungicide protected) and inoculated micro plots in 2 replications of a split-plot design in a single environment. The plant leaves were inoculated with a mixture of P. nodorum isolates three times. Spore concentrations were adjusted to 4 x 10⁶ of viable spores per one milliliter. The disease severity was rated on a scale, where > 90% – susceptible, < 10% - resistant. Disease ratings of plant leaves showed statistically significant differences among all lines tested. Higher resistance to P. nodorum was observed more often on leaves of somaclonal lines than on dihaploid ones. On average, disease, severity reached 15% on leaves of somaclones and 30% on leaves of dihaploids. Some of the genotypes were showing low leaf infection, e.g. dihaploid D-33 (disease severity 4%) and a somaclone S-1 (disease severity 2%). The results from this study prove that dihaploid and somaclonal variation might be successfully used as an additional source of wheat resistance to the pathogen and it could be recommended to use in commercial breeding programs. The reported results prove that biotechnological methods may effectively be used in breeding for disease resistance of wheat to fungal necrotrophic pathogens.

Keywords: glume and leaf blotch, somaclonal, androgenic variation, wheat, resistance breeding

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Authors: Matthew Omoniyi Adebola, Jude E Amadi

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Ground nut (Arachis hypogaea) is one of the most popular commercial crops in Nigeria. Its suc-cessful production has been drastically affected by early leaf spot disease caused by Mycosphae-rella arachidis Deighton. In vitro control of the pathogen by six medicinal plants (Entada afri-cana, Vitex doniana, Lawsonia inermis, Azadirachta indica, Acalypha hispida and Nuaclea lati-folia) was assessed in this study. The extracts of the plants were prepared using cold and hot wa-ter and alcohol. The pathogen was isolated from ground nut infected with early leaf spot disease. The results revealed a great significant difference (P<0.05) in yield of extracts between cold water, hot water, and alcohol extracts. A significant difference (P<0.05) was observed in percentage concentrations of the various phytochemical constituents present in the extracts. Flavonoids per-centage concentration was the highest (0.68 - 1.95%) followed by saponnin(0.09-1.53%) in N. latifolia extracts. Steroiods had the least percentage concentrations (0.00- 0.09%)followed by terpenoids(0.02–0.71%) and proanthocyannin (0.05 – 0.86%). N. latifolia extracts produced the highest percentage concentrations (0.07–1.95%) of all the phytochemicals followed by A. indi-ca(0.05–1.64%)and least concentrations were obtained in A. hispidia(0.09 – 0.87%)and V. do-niana (0.00–0.88%). The extracts inhibited spore germination and growth of M. arachidis. The inhibition by alcohol extracts was high and significantly different (P>0.05) from cold and hot water extracts. Alcohol extract of L. inermis gave 100% spore germination inhibition followed by N. latifolia and A.indica with 97.75% and 85.60% inhibition respectively. Therefore, field trials of these six medicinal plants on the control of early leaf spot disease of ground nut are rec-ommended.

Keywords: groundnut, phytochemicals, medicinal plants, extracts, inhibition

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Authors: M. M. Ali, Ahmed Al- Ani, Derek Eamus, Daniel K. Y. Tan

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In this glasshouse study, we developed the new image-based non-destructive technique for detecting leaf P status of different crops such as cotton, tomato and lettuce. Plants were allowed to grow on nutrient media containing different P concentrations, i.e. 0%, 50% and 100% of recommended P concentration (P0 = no P, L; P1 = 2.5 mL 10 L-1 of P and P2 = 5 mL 10 L-1 of P as NaH2PO4). After 10 weeks of growth, plants were harvested and data on leaf P contents were collected using the standard destructive laboratory method and at the same time leaf images were collected by a handheld crop image sensor. We calculated leaf area, leaf perimeter and RGB (red, green and blue) values of these images. This data was further used in the linear discriminant analysis (LDA) to estimate leaf P contents, which successfully classified these plants on the basis of leaf P contents. The data indicated that P deficiency in crop plants can be predicted using the image and morphological data. Our proposed non-destructive imaging method is precise in estimating P requirements of different crop species.

Keywords: image-based techniques, leaf area, leaf P contents, linear discriminant analysis

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Authors: Wanida Wonsawat

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Bioreduction of silver nanoparticles (AgNPs) from silver ions (Ag+) using water extract of Thai basil leaf was successfully carried out. The basil leaf extract provided a reducing agent and stabilizing agent for a synthesis of metal nanoparticles. Silver nanoparticles received from cut and uncut basil leaf was compared. The resulting silver nanoparticles are characterized by UV-Vis spectroscopy. The maximum intensities of silver nanoparticle from cut and uncut basil leaf were 410 and 420, respectively. The techniques involved are simple, eco-friendly and rapid.

Keywords: basil leaves, silver nanoparticles, green synthesis, plant extract

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Authors: Anike Akinrinlade

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The world population is estimated to grow from 7.1 billion to 9.22 billion by 2075, increasing therefore by 23% from the current global population. Much of the demographic changes up to 2075 will take place in the less developed regions. There are currently 54 countries which fall under the bracket of being defined as having ‘low-middle income’ economies and need new ways to generate valuable products from current resources that is available. Artemisia annua L is well used for the extraction of the phytochemical artemisinin, which accounts for around 0.01 to 1.4 % dry weight of the plant. Artemisinin is used in the treatment of malaria, a disease rampart in sub-Saharan Africa and in many other countries. Once artemisinin has been extracted the spent leaf and waste streams are disposed of as waste. A feasibility study was carried out looking at increasing the biomass value of A. annua, by designing a biorefinery where spent leaf and waste streams are utilized for high product generation. Quercetin, ferulic acid, dihydroartemisinic acid, artemisinic acid and artemsinin were screened for in the waste stream samples and the spent leaf. The analytical results showed that artemisinin, artemisinic acid and dihydroartemisinic acid were present in the waste extracts as well as camphor and arteannuin b. Ongoing effects are looking at using more industrially relevant solvents to extract the phytochemicals from the waste fractions and investigate how microwave pyrolysis of spent leaf can be utilized to generate bio-products.

Keywords: high value product generation, bioinformatics, biomedicine, waste streams, spent leaf

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Authors: Asim Abbasi, Amer Habib, Sajid Hussain, Muhammad Sufyan, Iqra, Hasnain Sajjad

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Except for few scattered cases, powdery mildew disease was not a big problem for watermelon in the past but with the outbreaks of its pathotypes, races 1W and 2W, this disease becomes a serious issue all around the globe. The severe outbreak of this disease also increased the rate of fungicide application for its proper management. Twelve varieties of watermelon were screened in Research Area of Department of Plant pathology, University of Agriculture, Faisalabad to check the incidence of powdery mildew disease. Disease inoculum was prepared and applied with the help of foliar spray method. Fungicides and plants extracts were also applied after the disease incidence. Percentage leaf surface area diseased was assessed visually with a modified Horsfall-Barratt scale. The results of the experiment revealed that among all varieties, WT2257 and Zcugma F1 were highly resistant showing less than 5% disease incidence while Anar Kali and Sugar baby were highly susceptible with disease incidence of more than 65%. Among botanicals neem extract gave best results with disease incidence of less than 20%. Besides neem, all other botanicals also gave significant control of powdery mildew disease than the untreated check. In case of fungicides, Gemstar showed least disease incidence i.e. < 10%, however besides control maximum disease incidence was observed in Curzate (> 30%).

Keywords: botanicals, fungicides, pathotypes, powdery mildew

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Authors: Parisa Alizadeh Oskuie, Shahram Baghban Ciruse

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The influence of arbuscular mycorrhizal (AM) fungus(Glomus mossea) on plant growth and leaf-water potential of tomato (lycopersicum esculentum L.cv.super star) were studied in potted culture water stress stress period of 3 months in greenhouse conditions with the soil matric potential maintained at Fc1, Fc2, Fc3, and Fc4 respectively (0.8,0.7,0.6,0.5 Fc). Seven-day-old seedlings of tomato were transferred to pots containing Glomus mossea or non-AMF. AM colonization significantly stimulated shoot dry matter and leaf-water potential but water stress significantly decreased leaf area, shoot dry matter colonization and leaf-water potential.

Keywords: leaf-water potential, plant growth, tomato, VA mycorrhiza, water-stress

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Authors: Kamal KC, Zhendong Yin, Dasen Li, Zhilu Wu

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Intra-plant disease classification based on leaf images is a challenging computer vision task due to similarities in texture, color, and shape of leaves with a slight variation of leaf spot; and external environmental changes such as lighting and background noises. Deep convolutional neural network (DCNN) has proven to be an effective tool for binary classification. In this paper, two methods for binary classification of diseased plant leaves using DCNN are presented; model created from scratch and transfer learning. Our main contribution is a thorough evaluation of 4 networks created from scratch and transfer learning of 5 pre-trained models. Training and testing of these models were performed on a plant leaf images dataset belonging to 16 distinct classes, containing a total of 22,265 images from 8 different plants, consisting of a pair of healthy and diseased leaves. We introduce a deep CNN model, Optimized MobileNet. This model with depthwise separable CNN as a building block attained an average test accuracy of 99.77%. We also present a fine-tuning method by introducing the concept of a convolutional block, which is a collection of different deep neural layers. Fine-tuned models proved to be efficient in terms of accuracy and computational cost. Fine-tuned MobileNet achieved an average test accuracy of 99.89% on 8 pairs of [healthy, diseased] leaf ImageSet.

Keywords: deep convolution neural network, depthwise separable convolution, fine-grained classification, MobileNet, plant disease, transfer learning

Procedia PDF Downloads 188

Authors: H. Parameshwar Naik, Shripad Kulkarni

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Powdery mildew is a serious disease among the fungal in high humid areas with varied temperature conditions. In recent days disease becomes very severe due to uncertain weather conditions and unique character of the disease is, it produces white mycelia growth on upper and lower leaf surfaces and in severe conditions it leads to defoliation. Results of the experiment revealed that sowing of crop in the I fortnight (FN) of July recorded the minimum mean disease severity (7.96 %) followed by crop sown in II FN of July (13.19 %) as against the crop sown in II FN of August (41.44 %) and I FN of September (33.78 %) and the I fortnight of October (33.77 %). In the first date of sowing infection started at 45 DAS and progressed till 73 DAS and it was up to 14.66 Percent and in second date of sowing disease progressed up to 22.66 percent and in the third date of sowing, it was up to 59.35 percent. Afterward, the disease started earlier and progressed up to 66.15 percent and in sixth and seventh date of sowing disease progressed up to 43.15 percent and 59.85 percent respectively. Disease progress is very fast after 45 days after sowing and highest disease incidence was noticed at 73 DAS irrespective of dates of sowing. From the results of the present study, it is very clear that disease development will be very high if crop sown in between 1st fortnight of August and the 1st fortnight of September.

Keywords: cucumber, India, Karnataka, powdery mildew

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Authors: Pawandeep Kaur, Prashant Garg

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Red mud is a byproduct of aluminum extraction from Bauxite industry. It is dumped in a pond which not only uses thousands of acres of land but having very high pH, it pollutes the ground water and the soil also. Leaves are yet another big waste especially during autumn when they contribute immensely to the blockage of drains and can easily catch fire, among other risks hence also needs to be utilized effectively. The use of leaf ash and red mud in highway construction as a filling material may be an efficient way to dispose of leaf ash and red mud. In this study, leaf ash and lime were used as admixtures to improve the geotechnical engineering properties of red mud. The red mud was taken from National Aluminum Company Limited, Odisha, and leaf ash was locally collected. The aim of present study is to investigate the effect of lime and leaf ash on compaction characteristics and strength characteristics of red mud. California Bearing Ratio and Unconfined Compression Strength tests were performed on red mud by varying different percentages of lime and leaf ash. Leaf ash was added in proportion 2%,4%,6%,8% and 10% whereas lime was added in proportions of 5% to 15%. Optimized value of lime was decided with respect to maximum CBR (California Bearing Ratio) of red mud mixed with different proportions of lime. An increase of 300% in California Bearing ratio of red mud and an increase of 125% in Unconfined Compression Strength values were observed. It may, therefore, be concluded that red mud may be effectively utilized in the highway industry as a filler material.

Keywords: stabilization, lime, red mud, leaf ash

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Authors: Jacintha Menezes, Ramalingam Dharmalingam, Palaiahnakote Shivakumara

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In the recent years, Omani acid lime cultivation and production has been affected by Citrus greening or Huanglongbing (HLB) disease. HLB disease is one of the most destructive diseases for citrus, with no remedies or countermeasures to stop the disease. Currently used Polymerase chain reaction (PCR) and enzyme-linked immunosorbent assay (ELISA) HLB detection tests require lengthy and labor-intensive laboratory procedures. Furthermore, the equipment and staff needed to carry out the laboratory procedures are frequently specialized hence making them a less optimal solution for the detection of the disease. The current research uses hyperspectral imaging technology for automatic detection of citrus trees with HLB disease. Omani citrus tree leaf images were captured through portable Specim IQ hyperspectral camera. The research considered healthy, nutrition deficient, and HLB infected leaf samples based on the Polymerase chain reaction (PCR) test. The highresolution image samples were sliced to into sub cubes. The sub cubes were further processed to obtain RGB images with spatial features. Similarly, RGB spectral slices were obtained through a moving window on the wavelength. The resized spectral-Spatial RGB images were given to Convolution Neural Networks for deep features extraction. The current research was able to classify a given sample to the appropriate class with 92.86% accuracy indicating the effectiveness of the proposed techniques. The significant bands with a difference in three types of leaves are found to be 560nm, 678nm, 726 nm and 750nm.

Keywords: huanglongbing (HLB), hyperspectral imaging (HSI), · omani citrus, CNN

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Authors: Olofintoye Lawrence Kayode, Olorunniyi Omojola Felix

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Schistosomiasis is a vector-borne water-based disease transmitted by Bulinus globosus, causing haematuria in the urine of man, while fascioliasis is a trematode zoonosis infectious transmitted by Lymnaea natalensis causing liver disease in man and animals. Adult Bulinus globosus and Lymnaea natalensis were used for the experiment. Aqueous leaf extract of Ageratum conyzoides and Datura stramonium were prepared into 25, 50, 75, 100, 200 and 400 ppm concentrations. Ten snails of each species were exposed to different concentrations in triplicates, and dechlorinated water was used as control at 24h, 48h, and 72h exposure. The results revealed that 100 ppm of both plants leaves extracts indicated mortality rates between 76.7% and 100% at 24h, 48h, and 72h for both snail species. (P<0.05). In conclusion, the extract exercised molluscicidal activity to control the snail vector at lethal doses LC₅₀ (66.611- 72.021 ppm), CI = 63.083-77.90ppm and LC₉₀ (92.623-102.350), CI = 87.715 -110.12 ppm.

Keywords: snail, plant leaf, aqueous extract, mortality

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Authors: Nazmi Mat Nawi, Muhamad Najib Mohamad Nor, Che Dini Maryani Ishkandar

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This study was conducted to evaluate the capability of spectroscopic methods to detect the presence of pesticide residues on leaf mustard. A total of 105 leaf mustard used were divided into five batches, four batches were treated with four different types of pesticides whereas one batch with no pesticide applied. Spectral data were obtained using visible shortwave near infrared spectrometer (VSWNIRS) which is Ocean Optics HR4000 High-resolution Miniature Fiber Optic Spectrometer. Reflectance value was collected to determine the difference between one pesticide to the other. The obtained spectral data were pre-processed for optimum performance. The effective wavelength of approximate 880 nm, 675-710 nm also 550 and 700 nm indicates the overtones -CH stretching vibration, tannin, also chlorophyll content present in the leaf mustard respectively. This study has successfully demonstrated that the spectroscopic method was able to differentiate between leaf mustard sample with and without pesticide residue.

Keywords: detect, leaf mustard, non-destructive, pesticide residue

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Authors: Zawawi Daud

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Agro waste can be defined as waste from agricultural plant. Oil palm leaf and corn stalk can be categorized as ago waste material. At first, the comparison between oil palm leaf and corn stalk by mechanical properties from soda pulping process. After that, focusing on surface characterization by Scanning Electron Microscopy (SEM). Both material have a potential due to mechanical properties (tensile, tear, burst and fold) and surface characterization but corn stalk shows more in strength and compactness due to fiber characterization compared to oil palm leaf. This study promoting the green technology in develop a friendly product and suitable to be used as an alternative pulp in paper making industry.

Keywords: fiber, oil palm leaf, corn stalk, green technology

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Authors: Nathenal Thomas Lambamo

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Based on the fact that more than 85% of the labor force and 90% of the export earnings are taken by agriculture in Ethiopia and it can be said that it is the backbone of the overall socio-economic activities in the country. Among the cereal crops that the agriculture sector provides for the country, wheat is the third-ranking one preceding teff and maize. In the present day, wheat is in higher demand related to the expansion of industries that use them as the main ingredient for their products. The local supply of wheat for these companies covers only 35 to 40% and the rest 60 to 65% percent is imported on behalf of potential customers that exhaust the country’s foreign currency reserves. The above facts show that the need for this crop in the country is too high and in reverse, the productivity of the crop is very less because of these reasons. Wheat disease is the most devastating disease that contributes a lot to this unbalance in the demand and supply status of the crop. It reduces both the yield and quality of the crop by 27% on average and up to 37% when it is severe. This study aims to detect the most frequent and degrading wheat diseases, Septoria and Leaf rust, using the most efficiently used subset of machine learning technology, deep learning. As a state of the art, a deep learning class classification technique called Convolutional Neural Network (CNN) has been used to detect diseases and has an accuracy of 99.01% is achieved.

Keywords: septoria, leaf rust, deep learning, CNN

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Authors: M. Ebrahimi, E. Maroufyan, M. Shakeri, E. Oskoueian, A. F Soleimani, Y. M. Goh

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Lipid peroxidation is a main reason of low quality in meat and meat products. The free radical chain reaction is the major process of lipid peroxidation and reactive oxygen species (ROS) such as hydroxyl radical and hydroperoxyl radical are the main starter of the chain reaction. Papaya leaf contains several secondary metabolites which can be used as a potential antioxidant in broiler feed. Hence, this research was carried out to evaluate the potential of papaya leaf to prevent lipid peroxidation and enhance the antioxidant activity of breast meat of broiler chicken. The results showed that supplementation of papaya leaf at 5%, significantly (p < 0.05) reduced the lipid peroxidation compared to control group. The supplementation of papaya leaf prevented from lipid peroxidation and enhanced the antioxidant activity of the broiler breast meat significantly (p < 0.05) after different storage periods. Papaya leaf reduced the lipid oxidation of meat during storage with strong free radical-scavenging ability. In conclusion, supplementation of papaya leaf in broiler diet to have high quality meat is recommended.

Keywords: antioxidant activity, papaya leaf, breast meat, lipid peroxidation

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Authors: Keziah Bazar

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The effect of Brassica rapa leaf extracts on the growth of upland Ipomoea aquatica was investigated. One hundred grams Brassica rapa leaf were blended using a heavy duty blender. These were diluted with water to have final concentrations of 75% (T1), 50% (T2) and 25% (T3) that served as treatments of the study. Pure water (T0) that served as control was also included Upland Ipomoea aquatic were grown in pots. A 3-4 in water level was maintained during the whole duration of the study. Plant height, leaf area, fruit size and shoot height, were taken after 6 months. Results showed that plant height and shoot height was highest in T1 while T0 was the lowest. On the other hand, T2 had the highest leaf area and fruit size. The study suggests that T1 and T2 can be a good fertilizer for Ipomoea aquatica.

Keywords: Ipomoea aquatica, leaf extracts, growth, Brassica rapa

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Authors: Omolola Soji-Omoniwa, Babasoji Omoniwa

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The hypolipidemic effect of leaf essential oil of Citrus sinensis in alloxan–induced diabetic rats was evaluated. Forty albino rats (150–200 g) were randomly selected into 4 groups of 10 rats each, representing Normal Control, Diabetic Control, Diabetic treated with 14.2 mg/kg body weight Metformin and Diabetic treated with 110 mg/kg body weight leaf essential oil of Citrus sinensis. Diabetes was induced in the animals by intraperitoneal administration of single dose alloxan monohydrate (150 mg/kg body weight). The leaf essential oil of Citrus sinensis was administered every other day to the Diabetic rats for a period of 15 days. The effects of leaf essential oil on High Density Lipoprotein (HDL), Low Density Lipoprotein (LDL), Trigylcerides and Cholesterol were evaluated. A significant reduction (p <0.05) in LDL, Triglycerides and cholesterol levels and a significant increase (p<0 .05) in HDL was observed. Leaf essential oil of Citrus sinensis possesses hypolipidemic properties.

Keywords: Citrus sinensis, Diabetes mellitus, hypolipidemic, leaf essential oil

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Authors: Nathenal Thomas

Abstract:

In the food chain and in many other agricultural products, corn, also known as maize, which goes by the scientific name Zea mays subsp, is a widely produced agricultural product. Corn has the highest adaptability. It comes in many different types, is employed in many different industrial processes, and is more adaptable to different agro-climatic situations. In Ethiopia, maize is among the most widely grown crop. Small-scale corn farming may be a household's only source of food in developing nations like Ethiopia. The aforementioned data demonstrates that the country's requirement for this crop is excessively high, and conversely, the crop's productivity is very low for a variety of reasons. The most damaging disease that greatly contributes to this imbalance between the crop's supply and demand is the corn disease. The failure to diagnose diseases in maize plant until they are too late is one of the most important factors influencing crop output in Ethiopia. This study will aid in the early detection of such diseases and support farmers during the cultivation process, directly affecting the amount of maize produced. The diseases in maize plants, such as northern leaf blight and cercospora leaf spot, have distinct symptoms that are visible. This study aims to detect the most frequent and degrading maize diseases using the most efficiently used subset of machine learning technology, deep learning so, called Image Processing. Deep learning uses networks that can be trained from unlabeled data without supervision (unsupervised). It is a feature that simulates the exercises the human brain goes through when digesting data. Its applications include speech recognition, language translation, object classification, and decision-making. Convolutional Neural Network (CNN) for Image Processing, also known as convent, is a deep learning class that is widely used for image classification, image detection, face recognition, and other problems. it will also use this algorithm as the state-of-the-art for my research to detect maize diseases by photographing maize leaves using a mobile phone.

Keywords: CNN, zea mays subsp, leaf blight, cercospora leaf spot

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Authors: Hairin Taha, P. Narrima, M. A. Hapipah, A. M. Mustafa

Abstract:

The chemical constituents and antioxidant activity of the leaf essential oils of Pseuduvaria monticola and Pseuduvaria macrophylla from the Annonaceae family were investigated. GC-TOFMS analyses identified 46 compounds from Pseuduvaria monticola and 11 compounds from Pseuduvaria macrophylla. The major constituents in the leaf essential oil of Pseuduvaria monticola were a-cadinol (13.0%), calamenene-cis (6.9%), alfa copaene (4%), and epizonarene (3.8%), while in the leaf essential oil of Pseuduvaria macrophylla were caryophyllene oxide (29.7%) and elimicin (28%). The antioxidant activity of both the essential oils were determined using the 2,2'-diphenyl-1-picrylhydrazyl assay (DPPH). The present study suggests that both essential oils demonstrated good antioxidant activity.

Keywords: Pseuduvaria monticola, Pseuduvaria macrophylla, leaf essential oils, GC-MSTOF, antioxidant activity

Procedia PDF Downloads 381

Authors: Zunnu Raen Akhtar

Abstract:

Cotton Leaf Curl Disease (CLCuD) is from Gemini virus and is transmitted through whiteflies in cotton. Transgenic cotton containing Antisense Coat Protein (ACP) has been found to show better results against CLCuD in cotton. In current research, Antisense Coat Protein was inserted in cotton plants to observe resistance developed in the cotton plants against CLCuD. T1 generation of plants were observed for its expression in plants. Tests were carried out to observe the expression of Antisense Coat Protein using Polymerase Chain Reaction (PCR) technique and by southern blotting. Whiteflies showing positive Cotton Leaf Curl Virus (CLCV) were reared and released in bioassay on ACP expressing cotton plants under laboratory as well as confined semi-field conditions. Results confirmed the expression of AC protein in PCR and southern blotting. Further laboratory results showed that cotton plants expressing AC protein showed rare incidence of CLCuD infection as compared to control. In the confined semi-field, similar results were observed in AC protein expressing cotton as compared to control. These results explicitly show that ACP can help to tackle the CLCuD issue in the future and further studies on biochemical processes involved in these plants and effects of ACP induction on non-target organisms should also be studied for eco-system.

Keywords: cotton, white flies, antisense coat protein, CLCV

Procedia PDF Downloads 185

Authors: Panpan Fan, Bo Ming, Niels P. R. Anten, Jochem B. Evers, Yaoyao Li, Shaokun Li, Ruizhi Xie

Abstract:

The reallocation of dry matter (DM) and nitrogen (N) from vegetative tissues to the grain sinks are critical for grain yield. The objective of this study was to quantify the DM and N accumulation, partition, and reallocation at the single-leaf, different-organ, and individual-plant scales and clarify the responses to different levels of N availabilities. A two-year field experiment was conducted in Jinlin province, Northeast China, with three N fertilizer rates to create the different N availability levels: N0 (N deficiency), N1(low supply), and N2 (high supply). The results showed that grain N depends more on reallocations of vegetative organs compared with grain DM. Besides, vegetative organs reallocated more DM and N to grain under lower N availability, whereas more grain DM and grain N were derived from post-silking leaf photosynthesis and post-silking N uptake from the soil under high N availability. Furthermore, the reallocation amount and reallocation efficiency of leaf DM and leaf N content differed among leaf ranks and were regulated by N availability; specifically, the DM reallocation occurs mainly on senesced leaves, whereas the leaf N reallocation was in live leaves. These results provide a theoretical basis for deriving parameters in crop models for the simulation of the demand, uptake, partition, and reallocation processes of DM and N.

Keywords: dry matter, leaf N content, leaf rank, N availability, reallocation efficiency

Procedia PDF Downloads 129

Authors: Kavyadevi N., Kaviya G., Gowsalya P., Janani M., Mohanraj S.

Abstract:

Hevea brasiliensis, also known as the rubber tree, is one of the foremost assets of crops in the world. One of the most significant advantages of the Rubber Plant in terms of air oxygenation is its capacity to reduce the likelihood of an individual developing respiratory allergies like asthma. To construct such a system that can properly identify crop diseases and pests and then create a database of insecticides for each pest and disease, we must first give treatment for the illness that has been detected. We shall primarily examine three major leaf diseases since they are economically deficient in this article, which is Bird's eye spot, algal spot and powdery mildew. And the recommended work focuses on disease identification on rubber tree leaves. It will be accomplished by employing one of the superior algorithms. Input, Preprocessing, Image Segmentation, Extraction Feature, and Classification will be followed by the processing technique. We will use time-consuming procedures that they use to detect the sickness. As a consequence, the main ailments, underlying causes, and signs and symptoms of diseases that harm the rubber tree are covered in this study.

Keywords: image processing, python, convolution neural network (CNN), machine learning

Procedia PDF Downloads 77

Authors: Enikő Bene

Abstract:

Our sowing date experiment took place in the Demonstration Garden of Institution of Plant Sciences, Agricultural Center of University of Debrecen, in 2012-2014. The thesis contains data of test year 2014. Our purpose, besides several other examinations, was to observe how sowing date influences leaf area index and activity of photosynthesis of maize hybrids, and how those factors affect fruiting. In the experiment we monitored the change of the leaf area index and the photosynthesis of hybrids with four different growing seasons. The results obtained confirm that not only the environmental and agricultural factors in the growing season have effect on the yield, but also other factors like the leaf area index and the photosynthesis are determinative parameters, and all those factors together, modifying effects of each other, develop average yields

Keywords: sowing date, hybrid, leaf area index, photosynthetic capacity

Procedia PDF Downloads 334