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  • Open Access

    ARTICLE

    A Lightweight Deep Learning-Based Model for Tomato Leaf Disease Classification

    Naeem Ullah1, Javed Ali Khan2,*, Sultan Almakdi3, Mohammed S. Alshehri3, Mimonah Al Qathrady4, Eman Abdullah Aldakheel5,*, Doaa Sami Khafaga5

    CMC-Computers, Materials & Continua, Vol.77, No.3, pp. 3969-3992, 2023, DOI:10.32604/cmc.2023.041819

    Abstract Tomato leaf diseases significantly impact crop production, necessitating early detection for sustainable farming. Deep Learning (DL) has recently shown excellent results in identifying and classifying tomato leaf diseases. However, current DL methods often require substantial computational resources, hindering their application on resource-constrained devices. We propose the Deep Tomato Detection Network (DTomatoDNet), a lightweight DL-based framework comprising 19 learnable layers for efficient tomato leaf disease classification to overcome this. The Convn kernels used in the proposed (DTomatoDNet) framework is 1 × 1, which reduces the number of parameters and helps in more detailed and descriptive feature extraction for classification. The proposed DTomatoDNet model… More >

  • Open Access

    ARTICLE

    Internet of Things Based Smart Irrigation System Using ESP WROOM 32

    Krish R. Mehta, K. Jayant Naidu, Madhav Baheti, Dev Parmar, A. Sharmila*

    Journal on Internet of Things, Vol.5, pp. 45-55, 2023, DOI:10.32604/jiot.2023.043102

    Abstract Farming has been the most prominent and fundamental activity for generations. As the population has been multiplying exponentially, the demand for agricultural yield is growing relentlessly. Such high demand in production through traditional farming methodologies often falls short in terms of efficiency due to the limitations of manual labour. In the era of digitization, smart agricultural solutions have been emerging through the windows of Internet of Things and Artificial Intelligence to improve resource management, optimize the process of farming and enhance the yield of crops, hence, ensuring sustainable growth of the increasing production. By implementing modern technologies in the field… More >

  • 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

    Fusion of Region Extraction and Cross-Entropy SVM Models for Wheat Rust Diseases Classification

    Deepak Kumar1, Vinay Kukreja1, Ayush Dogra1,*, Bhawna Goyal2, Talal Taha Ali3

    CMC-Computers, Materials & Continua, Vol.77, No.2, pp. 2097-2121, 2023, DOI:10.32604/cmc.2023.044287

    Abstract Wheat rust diseases are one of the major types of fungal diseases that cause substantial yield quality losses of 15%–20% every year. The wheat rust diseases are identified either through experienced evaluators or computerassisted techniques. The experienced evaluators take time to identify the disease which is highly laborious and too costly. If wheat rust diseases are predicted at the development stages, then fungicides are sprayed earlier which helps to increase wheat yield quality. To solve the experienced evaluator issues, a combined region extraction and cross-entropy support vector machine (CE-SVM) model is proposed for wheat rust disease identification. In the proposed… More >

  • Open Access

    ARTICLE

    GMLP-IDS: A Novel Deep Learning-Based Intrusion Detection System for Smart Agriculture

    Abdelwahed Berguiga1,2,*, Ahlem Harchay1,2, Ayman Massaoudi1,2, Mossaad Ben Ayed3, Hafedh Belmabrouk4

    CMC-Computers, Materials & Continua, Vol.77, No.1, pp. 379-402, 2023, DOI:10.32604/cmc.2023.041667

    Abstract Smart Agriculture, also known as Agricultural 5.0, is expected to be an integral part of our human lives to reduce the cost of agricultural inputs, increasing productivity and improving the quality of the final product. Indeed, the safety and ongoing maintenance of Smart Agriculture from cyber-attacks are vitally important. To provide more comprehensive protection against potential cyber-attacks, this paper proposes a new deep learning-based intrusion detection system for securing Smart Agriculture. The proposed Intrusion Detection System IDS, namely GMLP-IDS, combines the feedforward neural network Multilayer Perceptron (MLP) and the Gaussian Mixture Model (GMM) that can better protect the Smart Agriculture… More >

  • Open Access

    ARTICLE

    Deep Learning-Based Model for Detection of Brinjal Weed in the Era of Precision Agriculture

    Jigna Patel1, Anand Ruparelia1, Sudeep Tanwar1,*, Fayez Alqahtani2, Amr Tolba3, Ravi Sharma4, Maria Simona Raboaca5,6,*, Bogdan Constantin Neagu7

    CMC-Computers, Materials & Continua, Vol.77, No.1, pp. 1281-1301, 2023, DOI:10.32604/cmc.2023.038796

    Abstract The overgrowth of weeds growing along with the primary crop in the fields reduces crop production. Conventional solutions like hand weeding are labor-intensive, costly, and time-consuming; farmers have used herbicides. The application of herbicide is effective but causes environmental and health concerns. Hence, Precision Agriculture (PA) suggests the variable spraying of herbicides so that herbicide chemicals do not affect the primary plants. Motivated by the gap above, we proposed a Deep Learning (DL) based model for detecting Eggplant (Brinjal) weed in this paper. The key objective of this study is to detect plant and non-plant (weed) parts from crop images.… More >

  • 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 >

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