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  • Open Access

    REVIEW

    Applications of Microalgae in Five Areas of Biotechnology

    Héctor Alejandro Reza-Solis1, Ofelia Adriana Hernández-Rodríguez1,*, Andrés Francisco Martínez-Rosales2, Dámaris Leopoldina Ojeda-Barrios1

    Phyton-International Journal of Experimental Botany, Vol.92, No.10, pp. 2737-2759, 2023, DOI:10.32604/phyton.2023.029851

    Abstract Microalgae are mostly photoautotrophic microscopic organisms. According to their cellular structure, they are classified into two types, eukaryotes, and prokaryotes, and they are distributed in all types of ecosystems, presenting unique qualities due to the fact that they synthesize high value-added molecules used in various productive and environmental activities, and because their biomass is used as raw material to obtain various products. Therefore, the objective of this review was to collect, organize, and collate current information on the use of microalgae in the development of biotechnology involving the areas of agriculture, health, food, bioremediation, and biofuels. The results show that… More >

  • Open Access

    ARTICLE

    Increasing Crop Quality and Yield with a Machine Learning-Based Crop Monitoring System

    Anas Bilal1,*, Xiaowen Liu1, Haixia Long1,*, Muhammad Shafiq2, Muhammad Waqar3

    CMC-Computers, Materials & Continua, Vol.76, No.2, pp. 2401-2426, 2023, DOI:10.32604/cmc.2023.037857

    Abstract Farming is cultivating the soil, producing crops, and keeping livestock. The agricultural sector plays a crucial role in a country’s economic growth. This research proposes a two-stage machine learning framework for agriculture to improve efficiency and increase crop yield. In the first stage, machine learning algorithms generate data for extensive and far-flung agricultural areas and forecast crops. The recommended crops are based on various factors such as weather conditions, soil analysis, and the amount of fertilizers and pesticides required. In the second stage, a transfer learning-based model for plant seedlings, pests, and plant leaf disease datasets is used to detect… More >

  • Open Access

    ARTICLE

    Towards Sustainable Agricultural Systems: A Lightweight Deep Learning Model for Plant Disease Detection

    Sana Parez1, Naqqash Dilshad2, Turki M. Alanazi3, Jong Weon Lee1,*

    Computer Systems Science and Engineering, Vol.47, No.1, pp. 515-536, 2023, DOI:10.32604/csse.2023.037992

    Abstract A country’s economy heavily depends on agricultural development. However, due to several plant diseases, crop growth rate and quality are highly suffered. Accurate identification of these diseases via a manual procedure is very challenging and time-consuming because of the deficiency of domain experts and low-contrast information. Therefore, the agricultural management system is searching for an automatic early disease detection technique. To this end, an efficient and lightweight Deep Learning (DL)-based framework (E-GreenNet) is proposed to overcome these problems and precisely classify the various diseases. In the end-to-end architecture, a MobileNetV3Small model is utilized as a backbone that generates refined, discriminative,… More >

  • Open Access

    ARTICLE

    AI Method for Improving Crop Yield Prediction Accuracy Using ANN

    T. Sivaranjani1,*, S. P. Vimal2

    Computer Systems Science and Engineering, Vol.47, No.1, pp. 153-170, 2023, DOI:10.32604/csse.2023.036724

    Abstract Crop Yield Prediction (CYP) is critical to world food production. Food safety is a top priority for policymakers. They rely on reliable CYP to make import and export decisions that must be fulfilled before launching an agricultural business. Crop Yield (CY) is a complex variable influenced by multiple factors, including genotype, environment, and their interactions. CYP is a significant agrarian issue. However, CYP is the main task due to many composite factors, such as climatic conditions and soil characteristics. Machine Learning (ML) is a powerful tool for supporting CYP decisions, including decision support on which crops to grow in a… 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

    ARTICLE

    Evaluating the Effects of Sustainable Chemical and Organic Fertilizers with Water Saving Practice on Corn Production and Soil Characteristics

    Xuejun Zhang1,#, Muhammad Amjad Bashir2,#, Qurat-Ul-Ain Raza3, Xiaotong Liu1, Jianhang Luo1, Ying Zhao1, Qiuliang Lei4, Hafiz Muhammad Ali Raza2,3, Abdur Rehim2,3, Yucong Geng4, Hongbin Liu4,*

    Phyton-International Journal of Experimental Botany, Vol.92, No.5, pp. 1349-1360, 2023, DOI:10.32604/phyton.2023.026952

    Abstract

    The rapidly growing world population, water shortage, and food security are promising problems for sustainable agriculture. Farmers adopt higher irrigation and fertilizer applications to increase crop production resulting in environmental pollution. This study aimed to identify the long-term effects of intelligent water and fertilizers used in corn yield and soil nutrient status. A series of field experiments were conducted for six years with treatments as: farmer accustomed to fertilization used as control (CON), fertilizer decrement (KF), fertilizer decrement + water-saving irrigation (BMP1); combined application of organic and inorganic fertilizer + water-saving irrigation (BMP2), and combined application of controlled-release fertilizer (BMP3).… More >

  • Open Access

    ARTICLE

    Assessment of Nutrient Leaching Losses and Crop Uptake with Organic Fertilization, Water Saving Practices and Reduced Inorganic Fertilizer

    Xiaotong Liu1,#, Muhammad Amjad Bashir2,3,#, Yucong Geng4, Qurat-Ul-Ain Raza2, Abdur Rehim2, Muhammad Aon2, Jianhang Luo1, Ying Zhao1, Xuejun Zhang1,*, Hongbin Liu4,*

    Phyton-International Journal of Experimental Botany, Vol.92, No.5, pp. 1555-1570, 2023, DOI:10.32604/phyton.2023.026735

    Abstract

    The increasing world population has forced excessive chemical fertilizer and irrigation to complete the global food demand, deteriorating the water quality and nutrient losses. Short-term studies do not compile the evidences; therefore, the study aimed to identify the effectiveness of reduced doses of inorganic fertilizer and water-saving practices, hence, a six-year experiment (2015–2020) was conducted in China to address the knowledge gap. The experimental treatments were: farmer accustomed fertilization used as control (525:180:30 kg NPK ha−1), fertilizer decrement (450:150:15 kg NPK ha−1), fertilizer decrement + water-saving irrigation (450:150:15 kg NPK ha−1), application of organic and inorganic fertilizer + water-saving irrigation… More >

  • Open Access

    ARTICLE

    Modeling of Sensor Enabled Irrigation Management for Intelligent Agriculture Using Hybrid Deep Belief Network

    Saud Yonbawi1, Sultan Alahmari2, B. R. S. S. Raju3, Chukka Hari Govinda Rao4, Mohamad Khairi Ishak5, Hend Khalid Alkahtani6, José Varela-Aldás7,*, Samih M. Mostafa8

    Computer Systems Science and Engineering, Vol.46, No.2, pp. 2319-2335, 2023, DOI:10.32604/csse.2023.036721

    Abstract Artificial intelligence (AI) technologies and sensors have recently received significant interest in intellectual agriculture. Accelerating the application of AI technologies and agriculture sensors in intellectual agriculture is urgently required for the growth of modern agriculture and will help promote smart agriculture. Automatic irrigation scheduling systems were highly required in the agricultural field due to their capability to manage and save water deficit irrigation techniques. Automatic learning systems devise an alternative to conventional irrigation management through the automatic elaboration of predictions related to the learning of an agronomist. With this motivation, this study develops a modified black widow optimization with a… More >

  • Open Access

    ARTICLE

    Harris Hawks Optimizer with Graph Convolutional Network Based Weed Detection in Precision Agriculture

    Saud Yonbawi1, Sultan Alahmari2, T. Satyanarayana Murthy3, Padmakar Maddala4, E. Laxmi Lydia5, Seifedine Kadry6,7,8,*, Jungeun Kim9

    Computer Systems Science and Engineering, Vol.46, No.2, pp. 1533-1547, 2023, DOI:10.32604/csse.2023.036296

    Abstract Precision agriculture includes the optimum and adequate use of resources depending on several variables that govern crop yield. Precision agriculture offers a novel solution utilizing a systematic technique for current agricultural problems like balancing production and environmental concerns. Weed control has become one of the significant problems in the agricultural sector. In traditional weed control, the entire field is treated uniformly by spraying the soil, a single herbicide dose, weed, and crops in the same way. For more precise farming, robots could accomplish targeted weed treatment if they could specifically find the location of the dispensable plant and identify the… More >

  • Open Access

    ARTICLE

    Toxic and Antifeedant Effects of Different Pesticidal Plant Extracts against Beet Armyworm (Spodoptera exigua)

    Muhammad Asad1, Rashad Rasool Khan2,*, Ahmed B. Aljuboory3, Muhammad Haroon U. Rashid4, Uttam Kumar5, Inzamam Ul Haq6, Aqsa Hafeez7, Ahmed Noureldeen8, Khadiga Alharbi9,*

    Phyton-International Journal of Experimental Botany, Vol.92, No.4, pp. 1161-1172, 2023, DOI:10.32604/phyton.2023.026513

    Abstract The beet armyworm (BAW), Spodoptera exigua (Lepidoptera: Noctuidae) is a highly destructive pest of vegetables and field crops. Management of beet armyworm primarily relies on synthetic pesticides, which is threatening the beneficial community and environment. Most importantly, the BAW developed resistance to synthetic pesticides with making it difficult to manage. Therefore, alternative and environment-friendly pest management tactics are urgently required. The use of pesticidal plant extracts provides an effective way for a sustainable pest management program. To evaluate the use of pesticidal plant extracts against BAW, we selected six plant species (Lantana camara, Aloe vera, Azadirachta indica, Cymbopogon citratus, Nicotiana tabacum ,… More >

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