Robertas Damasevicius^1,*, Faheem Mahmood², Yaseen Zaman³, Sobia Dastgeer², Sajid Khan²

Computer Systems Science and Engineering, Vol.48, No.5, pp. 1349-1366, 2024, DOI:10.32604/csse.2024.050359 - 13 September 2024

Abstract Plant diseases must be identified as soon as possible since they have an impact on the growth of the corresponding species. Consequently, the identification of leaf diseases is essential in this field of agriculture. Diseases brought on by bacteria, viruses, and fungi are a significant factor in reduced crop yields. Numerous machine learning models have been applied in the identification of plant diseases, however, with the recent developments in deep learning, this field of study seems to hold huge potential for improved accuracy. This study presents an effective method that uses image processing and deep… More >

Oumaima Ameslek^1,*, Hafida Zahir², Soukaina Mitro², El Mostafa Bachaoui¹

Revue Internationale de Géomatique, Vol.33, pp. 321-340, 2024, DOI:10.32604/rig.2024.054838 - 03 September 2024

Abstract L’agriculture de précision (AP) est une stratégie de gestion agricole fondée sur l’observation, la mesure et la réponse à la variabilité des cultures inter/intra-champ. Il comprend des avancées en matière de collecte, d’analyse et de gestion des données, ainsi que des développements technologiques en matière de stockage et de récupération de données, de positionnement précis, de surveillance des rendements et de télédétection. Cette dernière offre une résolution spatiale, spectrale et temporelle sans précédent, mais peut également fournir des informations détaillées sur la hauteur de la végétation et diverses observations. Aujourd’hui, le succès des nouvelles technologies… More >

Md. Mosiur Rahman^1,2, Md. Ruhul Quddus³, Quanle Xu⁴, Muhammad Malek Hossain², Rong Liu¹, Mengwei Li¹, Xin Yan¹, Guan Li¹, Yishan Ji¹, Chenyu Wang¹, Ashutosh Sarker⁵, Tao Yang¹, Xuxiao Zong¹, Md. Monoar Hossain⁶, Saleh Alfarraj⁷, Mohammad Javed Ansari⁸, Sagar Maitra^9,*, Akbar Hossain^10,*

Phyton-International Journal of Experimental Botany, Vol.93, No.8, pp. 1781-1804, 2024, DOI:10.32604/phyton.2024.048992 - 30 August 2024

Abstract Grass pea (Lathyrus sativus L.) is an imperative food crop cultured in dryland agricultural ecology. It is a vital source of dietary protein to millions of populaces living in low-income countries in South-East Asia and Africa. This study highlights the improvement of genomic properties and their application in marker-trait relationships for 17 yield-related characters in 400 grass pea genotypes from China and Bangladesh. These characters were assessed via 56 polymorphic markers using general linear model (GLM) (P+G+Q) and mixed linear model (MLM) (P+G+Q+K) in the tassel software based on the linkage disequilibrium and population structure analysis.… More >

Sumit Sow^1,#, Shivani Ranjan^1,#,*, Mahmoud F. Seleiman^2,3, Hiba M. Alkharabsheh^4,*, Mukesh Kumar¹, Navnit Kumar¹, Smruti Ranjan Padhan⁵, Dhirendra Kumar Roy¹, Dibyajyoti Nath⁶, Harun Gitari⁷, Daniel O. Wasonga⁸

Phyton-International Journal of Experimental Botany, Vol.93, No.7, pp. 1569-1598, 2024, DOI:10.32604/phyton.2024.052241 - 30 July 2024

Abstract Agriculture plays a crucial role in the economy, and there is an increasing global emphasis on automating agricultural processes. With the tremendous increase in population, the demand for food and employment has also increased significantly. Agricultural methods traditionally used to meet these requirements are no longer adequate, requiring solutions to issues such as excessive herbicide use and the use of chemical fertilizers. Integration of technologies such as the Internet of Things, wireless communication, machine learning, artificial intelligence (AI), and deep learning shows promise in addressing these challenges. However, there is a lack of comprehensive documentation… More >

Ahmed Redha Mahlous^*

CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 4781-4803, 2024, DOI:10.32604/cmc.2024.050821 - 20 June 2024

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 - 28 May 2024

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 - 28 March 2024

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 - 26 December 2023

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 - 26 December 2023

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 - 29 November 2023

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 >