Due to the inconsistency of rice variety,agricultural industry faces an important challenge of rice grading and classification by the traditional grading system.The existing grading system is manual,which introduces s...Due to the inconsistency of rice variety,agricultural industry faces an important challenge of rice grading and classification by the traditional grading system.The existing grading system is manual,which introduces stress and strain to humans due to visual inspection.Automated rice grading system development has been proposed as a promising research area in computer vision.In this study,an accurate deep learning-based non-contact and cost-effective rice grading system was developed by rice appearance and characteristics.The proposed system provided real-time processing by using a NI-myRIO with a high-resolution camera and user interface.We firstly trained the network by a rice public dataset to extract rice discriminative features.Secondly,by using transfer learning,the pre-trained network was used to locate the region by extracting a feature map.The proposed deep learning model was tested using two public standard datasets and a prototype real-time scanning system.Using AlexNet architecture,we obtained an average accuracy of 98.2%with 97.6%sensitivity and 96.4%specificity.To validate the real-time performance of proposed rice grading classification system,various performance indices were calculated and compared with the existing classifier.Both simulation and real-time experiment evaluations confirmed the robustness and reliability of the proposed rice grading system.展开更多
Rice is an essential food crop that is cultivated in many countries.Rice leaf diseases can cause significant damage to crop cultivation,leading to reduced yields and economic losses.Traditional disease detection appro...Rice is an essential food crop that is cultivated in many countries.Rice leaf diseases can cause significant damage to crop cultivation,leading to reduced yields and economic losses.Traditional disease detection approaches are often time-consuming,labor-intensive,and require expertise.Automatic leaf disease detection approaches help farmers detect diseases without or with less human interference.Most of the earlier studies on rice leaf disease detection depended on image processing and machine learning techniques.Image processing techniques are used to extract features from diseased leaf images,such as the color,texture,vein patterns,and shape of lesions.Machine learning techniques are used to detect diseases based on the extracted features.In contrast,deep learning techniques learn complex patterns from large datasets without explicit feature extraction techniques and are well-suited for disease detection tasks.This systematic review explores various deep learning approaches used in the literature for rice leaf disease detection,such as Transfer Learning,Ensemble Learning,and Hybrid approaches.This review also discusses the effectiveness of these approaches in addressing various challenges.This review discusses the details of various models and hyperparameter settings used,model fine-tuning techniques followed,and performance evaluation metrics utilized in various studies.This review also discusses the limitations of existing studies and presents future directions for further developing more robust and efficient rice leaf disease detection techniques.展开更多
基金the Indian National Academy of Science, New Delhi for providing research fellowship in the Department of Electrical Engineering, Indian Institute of Technology, New Delhi and Department of Electrical and Electronics Engineering, Mepco Schlenk Engineering College, Sivakasi, India for providing the necessary research facilities
文摘Due to the inconsistency of rice variety,agricultural industry faces an important challenge of rice grading and classification by the traditional grading system.The existing grading system is manual,which introduces stress and strain to humans due to visual inspection.Automated rice grading system development has been proposed as a promising research area in computer vision.In this study,an accurate deep learning-based non-contact and cost-effective rice grading system was developed by rice appearance and characteristics.The proposed system provided real-time processing by using a NI-myRIO with a high-resolution camera and user interface.We firstly trained the network by a rice public dataset to extract rice discriminative features.Secondly,by using transfer learning,the pre-trained network was used to locate the region by extracting a feature map.The proposed deep learning model was tested using two public standard datasets and a prototype real-time scanning system.Using AlexNet architecture,we obtained an average accuracy of 98.2%with 97.6%sensitivity and 96.4%specificity.To validate the real-time performance of proposed rice grading classification system,various performance indices were calculated and compared with the existing classifier.Both simulation and real-time experiment evaluations confirmed the robustness and reliability of the proposed rice grading system.
文摘Rice is an essential food crop that is cultivated in many countries.Rice leaf diseases can cause significant damage to crop cultivation,leading to reduced yields and economic losses.Traditional disease detection approaches are often time-consuming,labor-intensive,and require expertise.Automatic leaf disease detection approaches help farmers detect diseases without or with less human interference.Most of the earlier studies on rice leaf disease detection depended on image processing and machine learning techniques.Image processing techniques are used to extract features from diseased leaf images,such as the color,texture,vein patterns,and shape of lesions.Machine learning techniques are used to detect diseases based on the extracted features.In contrast,deep learning techniques learn complex patterns from large datasets without explicit feature extraction techniques and are well-suited for disease detection tasks.This systematic review explores various deep learning approaches used in the literature for rice leaf disease detection,such as Transfer Learning,Ensemble Learning,and Hybrid approaches.This review also discusses the effectiveness of these approaches in addressing various challenges.This review discusses the details of various models and hyperparameter settings used,model fine-tuning techniques followed,and performance evaluation metrics utilized in various studies.This review also discusses the limitations of existing studies and presents future directions for further developing more robust and efficient rice leaf disease detection techniques.
