Ahmed Redha Mahlous^*

CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 4781-4803, 2024, DOI:10.32604/cmc.2024.050821

Abstract Smart agriculture modifies traditional farming practices, and offers innovative approaches to boost production and sustainability by leveraging contemporary technologies. In today’s world where technology is everything, these technologies are utilized to streamline regular tasks and procedures in agriculture, one of the largest and most significant industries in every nation. This research paper stands out from existing literature on smart agriculture security by providing a comprehensive analysis and examination of security issues within smart agriculture systems. Divided into three main sections—security analysis, system architecture and design and risk assessment of Cyber-Physical Systems (CPS) applications—the study delves… More >

Kristamtini¹, Sugeng Widodo², Heni Purwaningsih³, Arlyna Budi Pustika¹, Setyorini Widyayanti¹, Arif Muazam¹, Arini Putri Hanifa^1,*, Joko Triastono², Dewi Sahara², Heni Sulistyawati Purwaning Rahayu², Pandu Laksono², Diah Arina Fahmi², Sutardi¹, Joko Pramono⁴, Rachmiwati Yusuf¹

Phyton-International Journal of Experimental Botany, Vol.93, No.5, pp. 1007-1021, 2024, DOI:10.32604/phyton.2024.048770

Abstract Sorghum (Sorghum bicolor L. Moench) is an essential food crop for more than 750 million people in tropical and sub-tropical dry climates of Africa, India, and Latin America. The domestic sorghum market in Indonesia is still limited to the eastern region (East Nusa Tenggara, West Nusa Tenggara, Java, and South Sulawesi). Therefore, it is crucial to carry out sorghum research on drylands. This research aimed to investigate the effect of sorghum genotype and planting distance and their interaction toward growth and sorghum’s productivity in the Gunungkidul dryland, Yogyakarta, Indonesia. In addition, the farm business analysis, including… More >

Muhammad Farhan^1,#, Manda Sathish², Rafia Kiran¹, Aroosa Mushtaq³, Alaa Baazeem⁴, Ammarah Hasnain⁵, Fahad Hakim¹, Syed Atif Hasan Naqvi^1,#,*, Mustansar Mubeen⁶, Yasir Iftikhar^6,*, Aqleem Abbas⁷, Muhammad Zeeshan Hassan¹, Mahmoud Moustafa⁸

Phyton-International Journal of Experimental Botany, Vol.93, No.3, pp. 581-609, 2024, DOI:10.32604/phyton.2024.046857

Abstract

Plant growth and resilience to abiotic stresses, such as soil salinity and drought, depend intricately on nitrogen metabolism. This review explores nitrogen’s regulatory role in plant responses to these challenges, unveiling a dynamic interplay between nitrogen availability and abiotic stress. In the context of soil salinity, a nuanced relationship emerges, featuring both antagonistic and synergistic interactions between salinity and nitrogen levels. Salinity-induced chlorophyll depletion in plants can be alleviated by optimal nitrogen supplementation; however, excessive nitrogen can exacerbate salinity stress. We delve into the complexities of this interaction and its agricultural implications. Nitrogen, a vital element

… More >

Naeem Ullah¹, Javed Ali Khan^2,*, Sultan Almakdi³, Mohammed S. Alshehri³, Mimonah Al Qathrady⁴, Eman Abdullah Aldakheel^5,*, 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… More >

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… More >

Medhat A. Tawfeek^1,2, Nacim Yanes^3,4, Leila Jamel^5,*, Ghadah Aldehim⁵, Mahmood A. Mahmood^1,6

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… More >

Deepak Kumar¹, Vinay Kukreja¹, Ayush Dogra^1,*, 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 More >

Abdelwahed Berguiga^1,2,*, Ahlem Harchay^1,2, Ayman Massaoudi^1,2, 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… More >

Jigna Patel¹, Anand Ruparelia¹, Sudeep Tanwar^1,*, Fayez Alqahtani², Amr Tolba³, Ravi Sharma⁴, Maria Simona Raboaca^5,6,*, 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… More >

Héctor Alejandro Reza-Solis¹, Ofelia Adriana Hernández-Rodríguez^1,*, 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 More >