Nouh Sabri Elmitwally^1,2, Maria Tariq^3,4, Muhammad Adnan Khan^5,*, Munir Ahmad³, Sagheer Abbas³, Fahad Mazaed Alotaibi⁶

Computer Systems Science and Engineering, Vol.42, No.3, pp. 1001-1014, 2022, DOI:10.32604/csse.2022.022017 - 08 February 2022

Abstract In the agricultural industry, rice infections have resulted in significant productivity and economic losses. The infections must be recognized early on to regulate and mitigate the effects of the attacks. Early diagnosis of disease severity effects or incidence can preserve production from quantitative and qualitative losses, reduce pesticide use, and boost ta country’s economy. Assessing the health of a rice plant through its leaves is usually done as a manual ocular exercise. In this manuscript, three rice plant diseases: Bacterial leaf blight, Brown spot, and Leaf smut, were identified using the Alexnet Model. Our research More >

Mengjiao Chen^1,2, Jinhui Zhai³, Jiajia Zhang^1,4,5,6, Hui Li^1,2, Xinjiang Niu^1,2, Yaxin Liu^1,2, Yue Ren^1,2, Hongyan Du^1,4,5,6, Jingle Zhu^1,4,5,6,*

Phyton-International Journal of Experimental Botany, Vol.91, No.5, pp. 1027-1044, 2022, DOI:10.32604/phyton.2022.019106 - 24 January 2022

Abstract Eucommia ulmoides ‘Hongye’ is a new ornamental variety of E. ulmoides with excellent red or purple foliage. We found that E. ulmoides ‘Hongye’ exhibited a gradual change from green to red colour under light conditions. However, the colouring mechanism in the leaves of E. ulmoides ‘Hongye’ remains unclear. In this study, we compared the pigment content and leaf colour index of E. ulmoides ‘Hongye’ at five stages with those of E. ulmoides ‘Xiaoye’, which was used as the control variety. The transcriptome sequencing data of the first-period (H1, green) and fifth-period (H5, red) leaves were also analysed and compared. The corresponding… More >

Sukanta Ghosh¹, Amar Singh¹, Kavita^2,*, N. Z. Jhanjhi³, Mehedi Masud⁴, Sultan Aljahdali⁴

CMC-Computers, Materials & Continua, Vol.71, No.3, pp. 4257-4274, 2022, DOI:10.32604/cmc.2022.023414 - 14 January 2022

Abstract Automatic plant classification through plant leaf is a classical problem in Computer Vision. Plants classification is challenging due to the introduction of new species with a similar pattern and look-a-like. Many efforts are made to automate plant classification using plant leaf, plant flower, bark, or stem. After much effort, it has been proven that leaf is the most reliable source for plant classification. But it is challenging to identify a plant with the help of leaf structure because plant leaf shows similarity in morphological variations, like sizes, textures, shapes, and venation. Therefore, it is required… More >

Ayman Altameem¹, Sandeep Kumar², Ramesh Chandra Poonia³, Abdul Khader Jilani Saudagar^4,*

CMC-Computers, Materials & Continua, Vol.71, No.3, pp. 4719-4736, 2022, DOI:10.32604/cmc.2022.022177 - 14 January 2022

Abstract Since the beginning of time, humans have relied on plants for food, energy, and medicine. Plants are recognized by leaf, flower, or fruit and linked to their suitable cluster. Classification methods are used to extract and select traits that are helpful in identifying a plant. In plant leaf image categorization, each plant is assigned a label according to its classification. The purpose of classifying plant leaf images is to enable farmers to recognize plants, leading to the management of plants in several aspects. This study aims to present a modified whale optimization algorithm and categorizes More >

Komal Bashir^1,*, Mariam Rehman², Afnan Bashir³, Faria Kanwal¹

Computer Systems Science and Engineering, Vol.42, No.2, pp. 751-777, 2022, DOI:10.32604/csse.2022.022247 - 04 January 2022

Abstract Knowledge Management (KM) has become a dynamic concept for inquiry in research. The management of knowledge from multiple sources requires a systematic approach that can facilitate capturing all important aspects related to a particular discipline, several KM frameworks have been designed to serve this purpose. This research aims to propose a Collaborative Knowledge Management (CKM) Framework that bridges gaps and overcomes weaknesses in existing frameworks. The paper also validates the framework by evaluating its effectiveness for the agriculture sector of Pakistan. A software LCWU aKMS was developed which serves as a practical implementation of the… More >

Ke Shi^1,2, Luyang Wang^1,2, Ruige Qi^1,2, Chunxia He^1,2,*

Journal of Renewable Materials, Vol.10, No.5, pp. 1257-1268, 2022, DOI:10.32604/jrm.2022.017891 - 22 December 2021

Abstract This work was aim to prepare a packing material from natural resources to reduce the environment pollution caused by plastics. Four bio-adhesives (guar gum, sodium alginate, agar and chitosan) were combined with lotus leaf fibers to prepare degradable composites, respectively. The mechanical properties, moisture absorption profiles and the thermal conductivity of the composites were studied and the cross section morphology and the thermal properties of the composites were analyzed. The Fourier-transform infrared spectroscopy (FTIR) results showed that the polar groups such as –OH and –COO^– in bio-adhesives can form hydrogen bond with –OH in lotus leaf More >

Fen Zhang¹, Weili Tian¹, Lu Cen¹, Litang Lv², Xiaofang Zeng¹, Yulu Chen¹, Yichen Zhao^2,*, Yan Li^1,*

Phyton-International Journal of Experimental Botany, Vol.91, No.4, pp. 791-809, 2022, DOI:10.32604/phyton.2022.018104 - 09 December 2021

Abstract Duyun Maojian tea is a famous tea in China. In this study, the specific-locus amplified fragment (SLAF) sequencing method was used to analyze the population structure and conduct a genome-wide association study (GWAS) of 2 leaf traits of 123 tea plants in Qiannan, China. A total of 462,019 SLAF tags and 11,362,041 single-nucleotide polymorphism (SNP) loci were obtained. The results of phylogenetic tree analysis, cluster analysis, and principal component analysis showed that 123 tea germplasms were clustered into three groups, and the heterozygosity rates for Groups I, II, and II were 0.206, 0.224, and 0.34,… More >

Swati Singh¹, Isha Gupta², Sheifali Gupta², Deepika Koundal^3,*, Sultan Aljahdali⁴, Shubham Mahajan⁵, Amit Kant Pandit⁵

CMC-Computers, Materials & Continua, Vol.71, No.1, pp. 1849-1866, 2022, DOI:10.32604/cmc.2022.021875 - 03 November 2021

Abstract In Agriculture Sciences, detection of diseases is one of the most challenging tasks. The mis-interpretations of plant diseases often lead to wrong pesticide selection, resulting in damage of crops. Hence, the automatic recognition of the diseases at earlier stages is important as well as economical for better quality and quantity of fruits. Computer aided detection (CAD) has proven as a supportive tool for disease detection and classification, thus allowing the identification of diseases and reducing the rate of degradation of fruit quality. In this research work, a model based on convolutional neural network with 19… More >

Fahd N. Al-Wesabi^1,2,*, Amani Abdulrahman Albraikan³, Anwer Mustafa Hilal⁴, Majdy M. Eltahir¹, Manar Ahmed Hamza⁴, Abu Sarwar Zamani⁴

CMC-Computers, Materials & Continua, Vol.70, No.3, pp. 6223-6238, 2022, DOI:10.32604/cmc.2022.021299 - 11 October 2021

Abstract Precision agriculture enables the recent technological advancements in farming sector to observe, measure, and analyze the requirements of individual fields and crops. The recent developments of computer vision and artificial intelligence (AI) techniques find a way for effective detection of plants, diseases, weeds, pests, etc. On the other hand, the detection of plant diseases, particularly apple leaf diseases using AI techniques can improve productivity and reduce crop loss. Besides, earlier and precise apple leaf disease detection can minimize the spread of the disease. Earlier works make use of traditional image processing techniques which cannot assure… More >

Nazar Hussain¹, Muhammad Attique Khan¹, Usman Tariq², Seifedine Kadry^3,*, MuhammadAsfand E. Yar⁴, Almetwally M. Mostafa⁵, Abeer Ali Alnuaim⁶, Shafiq Ahmad⁷

CMC-Computers, Materials & Continua, Vol.70, No.2, pp. 3281-3294, 2022, DOI:10.32604/cmc.2022.019036 - 27 September 2021

Abstract Agriculture is an important research area in the field of visual recognition by computers. Plant diseases affect the quality and yields of agriculture. Early-stage identification of crop disease decreases financial losses and positively impacts crop quality. The manual identification of crop diseases, which are mostly visible on leaves, is a very time-consuming and costly process. In this work, we propose a new framework for the recognition of cucumber leaf diseases. The proposed framework is based on deep learning and involves the fusion and selection of the best features. In the feature extraction phase, VGG (Visual… More >