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Article
Engineering, Chemical
Shihui Liu et al.
Summary: The development of agricultural robots and the promotion of agricultural production automation are important means to alleviate the shortage of agricultural labor. Fruit and vegetable detection is a prerequisite for accurate harvesting by robots. This paper studies tomato recognition technology based on the YOLOv3 convolutional neural network algorithm and presents the tomato detection process of the YOLOv3 model. The test results show that the model based on the YOLOv3 convolutional neural network has a better detection effect.
JOURNAL OF FOOD PROCESS ENGINEERING
(2023)
Article
Agronomy
Muhamad Faisal et al.
Summary: Early identification of plant diseases is crucial for preventing crop loss. Advancements in deep learning have greatly improved the identification of plant leaf diseases. This paper introduces a novel CNN model, DFNet, that uses multiple automated feature extractors to classify plant leaf diseases more effectively.
Article
Agriculture, Multidisciplinary
Jiaping Jing et al.
Summary: A BC-YOLOv5 tomato disease recognition method is proposed to identify healthy growth and nine types of diseased tomato leaves, which shows excellent image annotation effect, high accuracy in disease identification, and balanced identification effect on various diseases.
JOURNAL OF THE SCIENCE OF FOOD AND AGRICULTURE
(2023)
Review
Agriculture, Multidisciplinary
Ruben Chin et al.
Summary: Plant diseases have adverse effects on agricultural products and food safety. Automated plant disease detection using drones is a feasible solution to prevent yield losses.
PRECISION AGRICULTURE
(2023)
Article
Agriculture, Multidisciplinary
Yun Zhao et al.
Summary: In this paper, a convolutional neural network model based on Inception and residual structure with an embedded modified convolutional block attention module (CBAM) was proposed to enhance classification accuracy of plant leaf diseases. The model achieved a high overall accuracy of 99.55% for the identification of corn, potato, and tomato diseases. Additionally, individual accuracy rates of 98.44%, 99.43%, and 95.20% were achieved for corn, potato, and tomato, respectively. The model demonstrated fewer parameters, shorter training time, and higher recognition accuracy compared to existing image classification models.
COMPUTERS AND ELECTRONICS IN AGRICULTURE
(2022)
Review
Computer Science, Artificial Intelligence
Poornima Singh Thakur et al.
Summary: Globally, major crops are significantly affected by diseases every year, leading to significant crop loss. Existing machine learning solutions are mainly focused on controlled environments and have not been widely applied for in-field plant disease detection.
EXPERT SYSTEMS WITH APPLICATIONS
(2022)
Article
Computer Science, Information Systems
Zhipeng Cao et al.
Summary: This paper proposes a mango recognition method based on an improved YOLOv4 network structure, which achieves better accuracy and detection speed than the traditional YOLOv4 method.
Article
Agronomy
Mariam Moussafir et al.
Summary: This paper introduces a new technology in agriculture 4.0 - a tomato disease detection model based on image data. By applying transfer learning and fine-tuning strategies to improve the performance of pre-trained models, more accurate disease diagnosis can be achieved. The results show that the proposed hybrid model achieves a high accuracy in tomato disease identification.
Article
Chemistry, Multidisciplinary
T. Anandhakrishnan et al.
Summary: Plant leaf diseases pose a significant risk to food security and the economy. This study proposes an automatic system for leaf disease identification using Deep Convolutional Neural Network (CNN), specifically focusing on tomato leaves. The model achieved a high accuracy of 98.40% on the testing set, after training on a large dataset of tomato leaf disease images.
SUSTAINABLE CHEMISTRY AND PHARMACY
(2022)
Article
Plant Sciences
Ramadan A. Arafa et al.
Summary: Late blight-resistant wild tomato produces a higher number of volatile organic compounds (VOCs) compared to susceptible genotypes. This study provides promising insights for the management of late blight and suggests that metabolic profiling can contribute to a better understanding of resistance mechanisms.
Article
Plant Sciences
Wufeng Liu et al.
Summary: In this study, a new attention-enhanced DenseNet neural network model is proposed, which includes a lesion feature extractor by region of interest (ROI) extraction algorithm. The model improves the recognition accuracy of rice leaf disease identification by highlighting the lesion area.
FRONTIERS IN PLANT SCIENCE
(2022)
Article
Agriculture, Multidisciplinary
Ananda S. Paymode et al.
Summary: The use of AI in agriculture, especially in disease detection and classification of different crops, has become increasingly important. This research proposes a model using Convolutional Neural Network (CNN) methods to detect Multi-Crops Leaf Disease (MCLD) and achieve higher accuracy. With the use of deep learning-based models, the proposed research successfully extracts image features and classifies diseased and healthy leaves. The experiment achieved an accuracy of 98.40% for grapes and 95.71% for tomatoes, directly contributing to increased food production in agriculture.
ARTIFICIAL INTELLIGENCE IN AGRICULTURE
(2022)
Article
Computer Science, Information Systems
Fahd N. Al-Wesabi et al.
Summary: Precision agriculture utilizes recent technological advancements to observe, measure, and analyze the requirements of individual fields and crops. This paper presents a novel AI enabled apple leaf disease classification technique and demonstrates its superior performance through a wide range of experiments.
CMC-COMPUTERS MATERIALS & CONTINUA
(2022)
Article
Agronomy
Shengyi Zhao et al.
Summary: Crop disease diagnosis is crucial for crop yield and agricultural production. This study proposed a deep convolutional neural network with an attention mechanism for accurate diagnosis of tomato leaf diseases. Experimental results on both tomato leaf diseases dataset and grape leaf diseases dataset demonstrated the model's high performance in extracting complex disease features.
Review
Agriculture, Multidisciplinary
Vibhor Kumar Vishnoi et al.
Summary: This article discusses the use of computer vision and soft computing techniques in plant disease detection, focusing on the application of leaf images. The study summarizes various aspects of this field, including the identification of diseases and different stages of detection systems, and analyzes the application of modern feature extraction techniques.
JOURNAL OF PLANT DISEASES AND PROTECTION
(2021)
Review
Agriculture, Multidisciplinary
Daoliang Li et al.
Summary: This article discusses the importance of crop and livestock monitoring in agricultural production, as well as the application of image fusion technology in improving monitoring methods. It reviews the specific applications of image fusion in areas such as crop recognition, disease detection, and livestock health assessment, while also highlighting the challenges and future research directions in the field.
COMPUTERS AND ELECTRONICS IN AGRICULTURE
(2021)
Article
Environmental Sciences
Long Chen et al.
Summary: The embedding of a convolutional block attention module (CBAM) in between the convolution blocks of P-Net to construct CBAM-P-Net significantly enhances the feature extraction efficiency of the model. Testing in different sample windows shows that CBAM-P-Net has increased accuracy and kappa coefficient.
Review
Engineering, Chemical
Nikita S. Bhatkar et al.
Summary: Tomatoes, as a valuable agricultural product, can be dried on a large scale for various commercial products, functional foods, and direct consumption. The challenge lies in producing dried forms with quality attributes equivalent to those of fresh tomatoes. Various drying techniques and pretreatments are employed to produce dried tomato products with different qualities.
Article
Telecommunications
Nisar Ahmad et al.
Summary: The study proposes an automated plant disease identification approach using complex deep learning models and feature extraction techniques, achieving an accuracy of 98.79% and suitable for mobile and handheld devices. The proposed model shows superior performance in disease identification and can be extended by adding more disease categories.
WIRELESS PERSONAL COMMUNICATIONS
(2021)
Article
Computer Science, Information Systems
Changjian Zhou et al.
Summary: The restructured residual dense network model proposed in this paper can efficiently identify tomato leaf diseases with high accuracy and performance, providing an effective method for crop disease recognition.
Article
Plant Sciences
Bin Liu et al.
FRONTIERS IN PLANT SCIENCE
(2020)
Review
Agriculture, Multidisciplinary
R. Manavalan
COMPUTERS AND ELECTRONICS IN AGRICULTURE
(2020)
Article
Computer Science, Information Systems
Rupali Sandip Kute et al.
INFORMATION SCIENCES
(2019)
Review
Plant Sciences
Muhammad Hammad Saleem et al.
Article
Multidisciplinary Sciences
Liming Wang et al.
SCIENTIFIC REPORTS
(2017)
Article
Computer Science, Artificial Intelligence
Jing Rui Tang et al.
APPLIED SOFT COMPUTING
(2017)
