N. Vasudevan^*, T. Karthick

Computer Systems Science and Engineering, Vol.46, No.1, pp. 337-356, 2023, DOI:10.32604/csse.2023.034242 - 20 January 2023

Abstract Crop protection is a great obstacle to food safety, with crop diseases being one of the most serious issues. Plant diseases diminish the quality of crop yield. To detect disease spots on grape leaves, deep learning technology might be employed. On the other hand, the precision and efficiency of identification remain issues. The quantity of images of ill leaves taken from plants is often uneven. With an uneven collection and few images, spotting disease is hard. The plant leaves dataset needs to be expanded to detect illness accurately. A novel hybrid technique employing segmentation, augmentation,… More >

S. Poornima^1,*, N. Sripriya¹, Adel Fahad Alrasheedi², S. S. Askar², Mohamed Abouhawwash^3,4

Intelligent Automation & Soft Computing, Vol.36, No.2, pp. 2393-2409, 2023, DOI:10.32604/iasc.2023.024820 - 05 January 2023

Abstract Plant diseases prediction is the essential technique to prevent the yield loss and gain high production of agricultural products. The monitoring of plant health continuously and detecting the diseases is a significant for sustainable agriculture. Manual system to monitor the diseases in plant is time consuming and report a lot of errors. There is high demand for technology to detect the plant diseases automatically. Recently image processing approach and deep learning approach are highly invited in detection of plant diseases. The diseases like late blight, bacterial spots, spots on Septoria leaf and yellow leaf curved… More >

Javed Rashid^1,2, Imran Khan¹, Ghulam Ali³, Shafiq ur Rehman⁴, Fahad Alturise⁵, Tamim Alkhalifah^5,*

CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 1235-1257, 2023, DOI:10.32604/cmc.2023.032005 - 22 September 2022

Abstract The guava plant has achieved viable significance in subtropics and tropics owing to its flexibility to climatic environments, soil conditions and higher human consumption. It is cultivated in vast areas of Asian and Non-Asian countries, including Pakistan. The guava plant is vulnerable to diseases, specifically the leaves and fruit, which result in massive crop and profitability losses. The existing plant leaf disease detection techniques can detect only one disease from a leaf. However, a single leaf may contain symptoms of multiple diseases. This study has proposed a hybrid deep learning-based framework for the real-time detection… More >

Mazen Mushabab Alqahtani¹, Ashit Kumar Dutta², Sultan Almotairi³, M. Ilayaraja⁴, Amani Abdulrahman Albraikan⁵, Fahd N. Al-Wesabi^6,7,*, Mesfer Al Duhayyim⁸

CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 217-233, 2023, DOI:10.32604/cmc.2023.025280 - 22 September 2022

Abstract Recent developments in digital cameras and electronic gadgets coupled with Machine Learning (ML) and Deep Learning (DL)-based automated apple leaf disease detection models are commonly employed as reasonable alternatives to traditional visual inspection models. In this background, the current paper devises an Effective Sailfish Optimizer with EfficientNet-based Apple Leaf disease detection (ESFO-EALD) model. The goal of the proposed ESFO-EALD technique is to identify the occurrence of plant leaf diseases automatically. In this scenario, Median Filtering (MF) approach is utilized to boost the quality of apple plant leaf images. Moreover, SFO with Kapur's entropy-based segmentation technique More >

Sandhya Venu Vasantha¹, Shirina Samreen^2,*, Yelganamoni Lakshmi Aparna³

CMC-Computers, Materials & Continua, Vol.73, No.1, pp. 1895-1914, 2022, DOI:10.32604/cmc.2022.028504 - 18 May 2022

Abstract Hitherto, Rice (Oryza Sativa) has been one of the most demanding food crops in the world, cultivated in larger quantities, but loss in both quality and quantity of yield due to abiotic and biotic stresses has become a major concern. During cultivation, the crops are most prone to biotic stresses such as bacterial, viral, fungal diseases and pests. These stresses can drastically damage the crop. Lately and erroneously recognized crop diseases can increase fertilizers costs and major yield loss which results in high financial loss and adverse impact on nation’s economy. The proven methods of… 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 >

Bobbinpreet¹, Sultan Aljahdali^2,*, Tripti Sharma¹, Bhawna Goyal¹, Ayush Dogra³, Shubham Mahajan⁴, Amit Kant Pandit⁴

CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 4771-4787, 2022, DOI:10.32604/cmc.2022.023150 - 21 April 2022

Abstract In recent times, the images and videos have emerged as one of the most important information source depicting the real time scenarios. Digital images nowadays serve as input for many applications and replacing the manual methods due to their capabilities of 3D scene representation in 2D plane. The capabilities of digital images along with utilization of machine learning methodologies are showing promising accuracies in many applications of prediction and pattern recognition. One of the application fields pertains to detection of diseases occurring in the plants, which are destroying the widespread fields. Traditionally the disease detection… 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 >

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 >

Ahmed Elaraby^1,*, Walid Hamdy², Madallah Alruwaili³

CMC-Computers, Materials & Continua, Vol.71, No.2, pp. 4019-4031, 2022, DOI:10.32604/cmc.2022.022161 - 07 December 2021

Abstract Plant diseases are a major impendence to food security, and due to a lack of key infrastructure in many regions of the world, quick identification is still challenging. Harvest losses owing to illnesses are a severe problem for both large farming structures and rural communities, motivating our mission. Because of the large range of diseases, identifying and classifying diseases with human eyes is not only time-consuming and labor intensive, but also prone to being mistaken with a high error rate. Deep learning-enabled breakthroughs in computer vision have cleared the road for smartphone-assisted plant disease and… More >