V. Nirmala^1,*, B. Gomathy²

Computer Systems Science and Engineering, Vol.44, No.3, pp. 2585-2601, 2023, DOI:10.32604/csse.2023.027512 - 01 August 2022

Abstract In agricultural engineering, the main challenge is on methodologies used for disease detection. The manual methods depend on the experience of the personal. Due to large variation in environmental condition, disease diagnosis and classification becomes a challenging task. Apart from the disease, the leaves are affected by climate changes which is hard for the image processing method to discriminate the disease from the other background. In Cucurbita gourd family, the disease severity examination of leaf samples through computer vision, and deep learning methodologies have gained popularity in recent years. In this paper, a hybrid method More >

Santhana Krishnan Jayapal¹, Sivakumar Poruran^2,*

Intelligent Automation & Soft Computing, Vol.35, No.1, pp. 1261-1275, 2023, DOI:10.32604/iasc.2023.026564 - 06 June 2022

Abstract Tea plant cultivation plays a significant role in the Indian economy. The Tea board of India supports tea farmers to increase tea production by preventing various diseases in Tea Plant. Various climatic factors and other parameters cause these diseases. In this paper, the image retrieval model is developed to identify whether the given input tea leaf image has a disease or is healthy. Automation in image retrieval is a hot topic in the industry as it doesn’t require any form of metadata related to the images for storing or retrieval. Deep Hashing with Integrated Autoencoders… More >

Masayuki Honda¹, Kenichi Tatsumi^2,*, Masaki Nakagawa³

CMES-Computer Modeling in Engineering & Sciences, Vol.133, No.3, pp. 557-577, 2022, DOI:10.32604/cmes.2022.020623 - 03 August 2022

Abstract A model to predict photosynthetic carbon assimilation rate (A) with high accuracy is important for forecasting crop yield and productivity. Long short-term memory (LSTM), a neural network suitable for time-series data, enables prediction with high accuracy but requires mesophyll variables. In addition, for practical use, it is desirable to have a technique that can predict A from easily available information. In this study, we propose a BLSTMaugmented LSTM (BALSTM) model, which utilizes bi-directional LSTM (BLSTM) to indirectly reproduce the mesophyll variables required for LSTM. The most significant feature of the proposed model is that its hybrid… More >

Xiaofang Zeng^1,#, Guangzheng Li^1,#, Nu’an Liu², Yan Li¹, Jianrong Li¹, Xiaozhen Huang¹, Degang Zhao^1,2,,*

Phyton-International Journal of Experimental Botany, Vol.91, No.11, pp. 2419-2437, 2022, DOI:10.32604/phyton.2022.021199 - 12 July 2022

Abstract Leaf-color mutants play an important role in the study of chlorophyll metabolism, chloroplast development, and photosynthesis system. In this study, the yellow leaf 1 (yl1) rice mutant was identified from the ethyl methane sulfonate-treated mutant progeny of Lailong, a glutinous japonica rice landrace cultivated in Guizhou Province, China. Results showed that yl1 exhibited yellow leaves with decreased chlorophyll content throughout the growth period. Chloroplast development in the yl1 mutant was disrupted, and the grana lamellae was loosely packed and disordered. RNA sequencing and real-time quantitative polymerase chain reaction (qRT-PCR) analysis revealed that the chlorophyll synthesis-related genes OsCHLH, OsCHLM, OsCHLG, PORB,… More >

Helong Yu, Xianhe Cheng, Ziqing Li, Qi Cai, Chunguang Bi^*

CMES-Computer Modeling in Engineering & Sciences, Vol.132, No.3, pp. 711-738, 2022, DOI:10.32604/cmes.2022.020263 - 27 June 2022

Abstract To solve the problem of difficulty in identifying apple diseases in the natural environment and the low application rate of deep learning recognition networks, a lightweight ResNet (LW-ResNet) model for apple disease recognition is proposed. Based on the deep residual network (ResNet18), the multi-scale feature extraction layer is constructed by group convolution to realize the compression model and improve the extraction ability of different sizes of lesion features. By improving the identity mapping structure to reduce information loss. By introducing the efficient channel attention module (ECANet) to suppress noise from a complex background. The experimental… More >

M. Ashraf Ahangar¹, Shabir Hussain Wani^1,*, Zahoor A. Dar², Jan Roohi¹, Fayaz Mohiddin¹, Monika Bansal³, Mukesh Choudhary⁴, Sumit K. Aggarwal⁴, S. A. Waza¹, Khursheed Ahmad Dar⁵, Ayman El Sabagh^6,7, Celaleddin Barutcular⁸, Omer Konuşkan⁹, Mohammad Anwar Hossain^10,*

Phyton-International Journal of Experimental Botany, Vol.91, No.10, pp. 2111-2133, 2022, DOI:10.32604/phyton.2022.020721 - 30 May 2022

Abstract Maize is cultivated extensively throughout the world and has the highest production among cereals. However, Northern corn leaf blight (NCLB) disease caused by Exherohilum turcicum, is the most devastating limiting factor of maize production. The disease causes immense losses to corn yield if it develops prior or during the tasseling and silking stages of crop development. It has a worldwide distribution and its development is favoured by cool to moderate temperatures with high relative humidity. The prevalence of the disease has increased in recent years and new races of the pathogen have been reported worldwide. The… More >

K. Anitha¹, S. Srinivasan^2,*

CMC-Computers, Materials & Continua, Vol.73, No.1, pp. 233-247, 2022, DOI:10.32604/cmc.2022.026542 - 18 May 2022

Abstract In India’s economy, agriculture has been the most significant contributor. Despite the fact that agriculture’s contribution is decreasing as the world’s population grows, it continues to be the most important source of employment with a little margin of difference. As a result, there is a pressing need to pick up the pace in order to achieve competitive, productive, diverse, and long-term agriculture. Plant disease misinterpretations can result in the incorrect application of pesticides, causing crop harm. As a result, early detection of infections is critical as well as cost-effective for farmers. To diagnose the disease… More >

Vinay Gautam^1,*, Jyoti Rani²

Intelligent Automation & Soft Computing, Vol.34, No.2, pp. 849-864, 2022, DOI:10.32604/iasc.2022.025113 - 03 May 2022

Abstract Mango is a widely growing and consumable fruit crop. The quantity and quality of production are most important to satisfy the needs of the huge population. Numerous research has been conducted to increase the yield of the crop. But a good number of crop harvests were destroyed due to various factors and leaf stress is one of them. The various types of stresses include biotic and abiotic that impact the mangoes productivity. But here the focus is on biotic stress factors such as fungus and bacteria. The effect of the stress can be reduced in… More >

Md. Abir Ul Islam¹, Juthy Abedin Nupur², Charles T. Hunter³, Abdullah Al Mamun Sohag⁴, Ashaduzzaman Sagar⁵, Md. Sazzad Hossain⁶, Mona F. A. Dawood^7,*, Arafat Abdel Hamed Abdel Latef⁸, Marián Brestič^9,10, Md. Tahjib-UI-Arif^4,*

Phyton-International Journal of Experimental Botany, Vol.91, No.8, pp. 1557-1583, 2022, DOI:10.32604/phyton.2022.021556 - 14 April 2022

Abstract Moringa leaf extract (MLE) has been shown to promote beneficial outcomes in animals and plants. It is rich in amino acids, antioxidants, phytohormones, minerals, and many other bioactive compounds with nutritional and growth-promoting potential. Recent reports indicated that MLE improved abiotic stress tolerance in plants. Our understanding of the mechanisms underlying MLE-mediated abiotic stress tolerance remains limited. This review summarizes the existing literature on the role of MLE in promoting plant abiotic stress acclimation processes. MLE is applied to plants in a variety of ways, including foliar spray, rooting media, and seed priming. Exogenous application More >

Ahmed S. Almasoud¹, Abdelzahir Abdelmaboud², Taiseer Abdalla Elfadil Eisa³, Mesfer Al Duhayyim⁴, Asma Abbas Hassan Elnour⁵, Manar Ahmed Hamza^6,*, Abdelwahed Motwakel⁶, Abu Sarwar Zamani⁶

CMC-Computers, Materials & Continua, Vol.72, No.1, pp. 1391-1407, 2022, DOI:10.32604/cmc.2022.024618 - 24 February 2022

Abstract In agriculture, rice plant disease diagnosis has become a challenging issue, and early identification of this disease can avoid huge loss incurred from less crop productivity. Some of the recently-developed computer vision and Deep Learning (DL) approaches can be commonly employed in designing effective models for rice plant disease detection and classification processes. With this motivation, the current research work devises an Efficient Deep Learning based Fusion Model for Rice Plant Disease (EDLFM-RPD) detection and classification. The aim of the proposed EDLFM-RPD technique is to detect and classify different kinds of rice plant diseases in… More >