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  • Open Access

    ARTICLE

    Adaptive Deep Learning Model to Enhance Smart Greenhouse Agriculture

    Medhat A. Tawfeek1,2, Nacim Yanes3,4, Leila Jamel5,*, Ghadah Aldehim5, Mahmood A. Mahmood1,6

    CMC-Computers, Materials & Continua, Vol.77, No.2, pp. 2545-2564, 2023, DOI:10.32604/cmc.2023.042179

    Abstract The trend towards smart greenhouses stems from various factors, including a lack of agricultural land area owing to population concentration and housing construction on agricultural land, as well as water shortages. This study proposes building a full farming adaptation model that depends on current sensor readings and available datasets from different agricultural research centers. The proposed model uses a one-dimensional convolutional neural network (CNN) deep learning model to control the growth of strategic crops, including cucumber, pepper, tomato, and bean. The proposed model uses the Internet of Things (IoT) to collect data on agricultural operations and then uses this data… More >

  • Open Access

    ARTICLE

    Modified Metaheuristics with Transfer Learning Based Insect Pest Classification for Agricultural Crops

    Saud Yonbawi1, Sultan Alahmari2, T. Satyanarayana murthy3, Ravuri Daniel4, E. Laxmi Lydia5, Mohamad Khairi Ishak6, Hend Khalid Alkahtani7,*, Ayman Aljarbouh8, Samih M. Mostafa9

    Computer Systems Science and Engineering, Vol.46, No.3, pp. 3847-3864, 2023, DOI:10.32604/csse.2023.036552

    Abstract Crop insect detection becomes a tedious process for agronomists because a substantial part of the crops is damaged, and due to the pest attacks, the quality is degraded. They are the major reason behind crop quality degradation and diminished crop productivity. Hence, accurate pest detection is essential to guarantee safety and crop quality. Conventional identification of insects necessitates highly trained taxonomists to detect insects precisely based on morphological features. Lately, some progress has been made in agriculture by employing machine learning (ML) to classify and detect pests. This study introduces a Modified Metaheuristics with Transfer Learning based Insect Pest Classification… More >

  • Open Access

    REVIEW

    The Genetic and Biochemical Mechanisms Underlying Cereal Seed Dormancy

    Sasa Jing1, Yuan Tian1, Heng Zhang2, John T. Hancock3, Ying Zhu2,*, Ping Li1,*

    Phyton-International Journal of Experimental Botany, Vol.92, No.4, pp. 1203-1214, 2023, DOI:10.32604/phyton.2023.026305

    Abstract The crop seeds have been a staple food for humans, and seed yield is important for sustaining agriculture development and enhancing human adaptability to food risks. The phenomenon of pre-harvest sprouting (PHS), caused by seed dormancy deficiency, and the phenomenon of low seedling emergence caused by seed deep dormancy, will lead to a reduction in agricultural production. Therefore, it is particularly important to understand the regulation mechanisms of seed dormancy. There are many studies on the regulation of seed dormancy in rice, but there are few studies on the regulation of seed dormancy in other crops, and the research on… More >

  • Open Access

    ARTICLE

    Crops Leaf Diseases Recognition: A Framework of Optimum Deep Learning Features

    Shafaq Abbas1, Muhammad Attique Khan1, Majed Alhaisoni2, Usman Tariq3, Ammar Armghan4, Fayadh Alenezi4, Arnab Majumdar5, Orawit Thinnukool6,*

    CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 1139-1159, 2023, DOI:10.32604/cmc.2023.028824

    Abstract Manual diagnosis of crops diseases is not an easy process; thus, a computerized method is widely used. From a couple of years, advancements in the domain of machine learning, such as deep learning, have shown substantial success. However, they still faced some challenges such as similarity in disease symptoms and irrelevant features extraction. In this article, we proposed a new deep learning architecture with optimization algorithm for cucumber and potato leaf diseases recognition. The proposed architecture consists of five steps. In the first step, data augmentation is performed to increase the numbers of training samples. In the second step, pre-trained… More >

  • Open Access

    REVIEW

    Heavy Metal/Metalloid Indexing and Balances in Agricultural Soils: Methodological Approach for Research

    Shahid Hussain*

    Phyton-International Journal of Experimental Botany, Vol.91, No.12, pp. 2687-2697, 2022, DOI:10.32604/phyton.2022.021158

    Abstract Heavy metal(loid) accumulation in agricultural soils is a threat to the soil capacity, quality, and productivity. It also increases human exposure to heavy metal(loid)s via consumption of contaminated plant-based foods. The detrimental effects of soil contamination also deteriorate the environment of plants and animals. For sustainable agriculture, therefore, the soil must be protected from toxic levels of heavy metal(loid)s. Studies on heavy metal(loid) balances in agricultural soils are important in predicting future risks to sustainable production from agro-ecological zones and human exposure to heavy metal(loid)s. The latest and continuous indexing of the problem seems a prerequisite for sustainable agriculture. This… More >

  • Open Access

    ARTICLE

    Prediction of Suitable Crops Using Stacked Scaling Conjugant Neural Classifier

    P. Nithya*, A. M. Kalpana

    Intelligent Automation & Soft Computing, Vol.35, No.3, pp. 3743-3755, 2023, DOI:10.32604/iasc.2023.030394

    Abstract Agriculture plays a vital role in economic development. The major problem faced by the farmers are the selection of suitable crops based on environmental conditions such as weather, soil nutrients, etc. The farmers were following ancestral patterns, which could sometimes lead to the wrong selection of crops. In this research work, the feature selection method is adopted to improve the performance of the classification. The most relevant features from the dataset are obtained using a Probabilistic Feature Selection (PFS) approach, and classification is done using a Neural Fuzzy Classifier (NFC). Scaling Conjugate Gradient (SCG) optimization method is used to update… More >

  • Open Access

    REVIEW

    Nanotechnology-Based Advancements in Postharvest Management of Horticultural Crops

    Tarun Kumar Upadhyay1,*, V. S. Varun Kumar2, Amit Baran Sharangi3, Vijay J. Upadhye1, Fahad Khan4, Pratibha Pandey4, Mohammad Amjad Kamal5,6,7, Abrar Yasin Baba8 and Khalid Rehman Hakeem9,*

    Phyton-International Journal of Experimental Botany, Vol.91, No.3, pp. 471-487, 2022, DOI:10.32604/phyton.2022.017258

    Abstract Horticulture is a branch of Agricultural science where it is defined as the science and art of cultivating and handling fruits, vegetables, ornamental plants and several plants having unique medicinal and aromatic values. Horticultural crops provide farmers with high income and have good export quality, but they have a concern about postharvest losses. Hence, increasing productivity and decreasing post-harvest losses by using scientific studies and techniques like biotechnology and nanotechnology could be the simplest possible solution to the above-mentioned problems. Using nanotechnology which is having the characteristics of nanoparticles is proven to be very useful in science and technological applications.… More >

  • Open Access

    ARTICLE

    Amylose Content, Morphology, Crystal Structure, and Thermal Properties of Starch Grains in Main and Ratoon Rice Crops

    Na Kuang, Huabin Zheng, Qiyuan Tang*, Yuanwei Chen, Xiaomin Wang, Youyi Luo

    Phyton-International Journal of Experimental Botany, Vol.90, No.4, pp. 1119-1230, 2021, DOI:10.32604/phyton.2021.014637

    Abstract Rice ratooning, or the production of a second rice crop from stubble after the harvest of the main crop, is considered to be a green and resource-efficient rice production system. The present study was conducted to examine variance in amylose content (AC), grain morphology, crystal structure, and thermal properties of starch between main- and ratoon-season rice of seven varieties. Ratoon-season rice grains had higher ACs and significantly lower transition gelatinization temperatures (To, Tp, and Tc) than did main-season rice grains. The relative crystallinity and lamellar peak intensity of ratoon-season rice starch were 7.89% and 20.38% lower, respectively, than those of… More >

  • Open Access

    ARTICLE

    CO2 Assimilation Rate in Production Systems for Papaya Crops

    R. Ariza-Flores1, D. Trujillo-García2, M. A. Otero-Sánchez2, E. Canales Sosa2, C. H. Avendaño-Arrazate3,*, L. A. Gálvez-Marroquín4, P. Cadena Iñiguez5

    Phyton-International Journal of Experimental Botany, Vol.90, No.3, pp. 933-947, 2021, DOI:10.32604/phyton.2021.013227

    Abstract The aim of this study was to evaluate some physiological aspects of papaya crops in semi conventional and organic production systems. The following factors assessed in this experiment were: 1. Production systems (organic and semi conventional); 2. Genotypes (Maradol and Maradona F1), and 3. Cover crop plants (Canavalia, vegetative cover and no cover). Twelve treatments were obtained -product of factors’ combination- and distributed under a threerepetition experimental design of subdivided parcels. The factors examined in this study, that changed the CO2 assimilation rate, were production system and genotype. It was determined that the greatest gas exchange in papaya crops happened… More >

  • Open Access

    REVIEW

    Applications of Molecular Markers in Fruit Crops for Breeding Programs—A Review

    Riaz Ahmad1, Muhammad Akbar Anjum1,*, Safina Naz1, Rashad Mukhtar Balal2

    Phyton-International Journal of Experimental Botany, Vol.90, No.1, pp. 17-34, 2021, DOI:10.32604/phyton.2020.011680

    Abstract Selection and use of molecular markers for evaluation of DNA polymorphism in plants are couple of the most important approaches in the field of molecular genetics. The assessment of genetic diversity using morphological markers is not sufficient due to little differentiating traits among the species, genera or their individuals. Morphological markers are not only highly influenced by environmental factors but skilled assessment is also prerequisite to find the variations in plant genetic resources. Therefore, molecular markers are considered as efficient tools for detailed DNA based characterization of fruit crops. Molecular markers provide new directions to the efforts of plant breeders… More >

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