Potato plants are plants that are beneficial to humans. Like other plants in general, potato plants also have diseases; if this disease is not treated immediately, there will be a significant decrease in food production. Therefore, it is necessary to detect diseases quickly and precisely so that disease control can be carried out effectively and efficiently. Classification of potato leaf disease can be done directly. Still, the symptoms cannot always explain the type of disease that attacks potato leaves because there are many types of diseases with symptoms that look the same. Humans also have deficiencies in determining the results of identification of potato leaf disease, so sometimes the results of identification between individuals can be different. Therefore, the use of Deep Learning for the classification process of potato leaf disease is expected to shorten the time and have a high classification accuracy. This study uses a deep learning method with the DenseNet201 architecture. The choice to use the DenseNet201 algorithm in this study is because the model can identify important features of potato leaves and recognize early signs of emerging diseases. This study aimed to evaluate the effectiveness of the transfer learning method with the DenseNet201 architecture in increasing the classification accuracy of potato leaf disease compared to traditional classification methods. This study uses two types of scenarios, namely, comparing the number of dropouts and comparing the three optimizers. This test produces the best model using dropout 0.1 and Adam optimizer with an accuracy of 99.5% for training, 95.2% for validation, and 96% for the confusion matrix. In this study, using data testing, as many as 40 images were tested into the model that has been built. The test results on this model resulted in a new accuracy for classifying potato leaf disease, namely 92.5%.
PotatoGANs: Utilizing Generative Adversarial Networks, Instance Segmentation, and Explainable AI for Enhanced Potato Disease Identification and Classification
Ahsanullah University of Science and Technology（阿沙努拉科学与技术大学）；The University of Western Australia（西澳大学）
Deep Learning-Based Approach for Identification of Potato Leaf Diseases Using Wrapper Feature Selection and Feature Concatenation
School of Information and Software Engineering, University of Electronic Science and Technology of China（电子科技大学信息与软件工程学院）；Faculty of Computing, Riphah International University, Faisalabad Campus（里法国际大学费萨拉巴德校区计算学院）；COMSATS University Islamabad, Wah Campus（COMSATS伊斯兰堡大学瓦赫校区）；Washington University of Science and Technology（华盛顿科技大学）；State University of New York Polytechnic Institute（纽约州立大学理工学院）；University of Alaska Anchorage（阿拉斯加安克雷奇大学）
In-field early disease recognition of potato late blight based on deep learning and proximal hyperspectral imaging
Lincoln Agri-Robotics, Lincoln Institute for Agri-Food Technology, University of Lincoln（林肯大学林肯农业食品技术研究所林肯农业机器人）；College of Engineering, Nanjing Agricultural University（南京农业大学工学院）；Department of Plant Protection Biology, Swedish University of Agricultural Sciences（瑞典农业科学大学植物保护生物学系）；Department of Plant and Environmental Sciences, University of Copenhagen（哥本哈根大学植物与环境科学系）；Danespo Breeding Company（Danespo育种公司）；Lincoln Centre for Autonomous System, University of Lincoln（林肯大学林肯自主系统中心）
LeafNet: A Large-Scale Dataset and Comprehensive Benchmark for Foundational Vision-Language Understanding of Plant Diseases
School of Electrical Engineering, Korea University（韩国大学电气工程学院）；Department of Integrative Biology, The University of Texas at Austin（德克萨斯大学奥斯汀分校整合生物学系）；Department of Software Engineering, FPT University（FPT大学软件工程系）
Improving Fine-Grained Rice Leaf Disease Detection via Angular-Compactness Dual Loss Learning
Begum Rokeya University, Rangpur（贝古姆·罗基亚大学，朗布尔）；Wilmington University（威尔明顿大学）；Washington University of Science and Technology（华盛顿科学与技术大学）；Central Michigan University（中密歇根大学）
Neural Network-based Study for Rice Leaf Disease Recognition and Classification: A Comparative Analysis Between Feature-based Model and Direct Imaging Model
Department of Computer Science & Engineering, Netrokona University（计算机科学与工程系，纳托克纳大学）；Department of Computer and Communication Engineering, International Islamic University Chittagong（计算机与通信工程系，国际伊斯兰大学钦德加角分校）；School of Computing Sciences and Computer Engineering, University of Southern Mississippi（计算科学与计算机工程学院，密西根州立大学）；Department of CSE, Daffodil International University（计算机科学与工程系，达福尔国际大学）
AgroVisNet: A lightweight Convolutional Network and the BD-PlantDX Expert-Validated Benchmark for Radish, Potato and Pointed Gourd Disease Classification
Bangladesh Army University of Science & Technology（孟加拉国陆军科技大学）；Rajshahi University of Engineering & Technology（拉杰沙希工程技术大学）
Artificial Intelligence Lab, Department of Computer Systems Engineering, University of Engineering and Applied Sciences (UEAS), Swat（斯瓦特工程与应用科学大学计算机系统工程系人工智能实验室）；Interdisciplinary Research Center for Smart Mobility and Logistics (IRC-SML), King Fahd University of Petroleum and Minerals (KFUPM), Dhahran（法赫德国王石油矿产大学智能移动与物流跨学科研究中心）
Mam-App: A Novel Parameter-Efficient Mamba Model for Apple Leaf Disease Classification
Computer Science and Engineering, Begum Rokeya University（计算机科学与工程，贝 Gum 罗基亚大学）；Computer Science, Kumamoto University（计算机科学，熊本大学）；Information and Communication Technologies, Asian Institute of Technology（信息与通信技术，亚洲理工学院）；Computer Science, Texas State University（计算机科学，德克萨斯州立大学）
An Efficient Transfer Learning-based Approach for Apple Leaf Disease Classification
Islamic University of Technology（伊斯兰技术大学）；Bangabandhu Sheikh Mujibur Rahman Agricultural University（班加班杜·谢赫·穆吉布尔·拉赫曼农业大学）；King Fahd University of Petroleum & Minerals（法赫德国王石油与矿产大学）
Dynamic Meta-Ensemble Framework for Efficient and Accurate Deep Learning in Plant Leaf Disease Detection on Resource-Constrained Edge Devices
South West University Science and Technology, School of Computer Science and Technology（西南科技大学计算机科学与技术学院）；South West University Science and Technology, School of Control and Information Engineering Technology（西南科技大学控制与信息工程学院）
CT-CLIP: A Multi-modal Fusion Framework for Robust Apple Leaf Disease Recognition in Complex Environments
College of Information Science and Engineering, Shandong Agricultural University（山东农业大学信息科学与工程学院）；Shandong Provincial Smart Agriculture Characteristic Laboratory（山东省智慧农业特色实验室）；Centre for Vision Speech and Signal Processing (CVSSP), University of Surrey（萨里大学视觉语音与信号处理中心）；School of Mechanical and Electronic Engineering, Shandong Agricultural Engineering College（山东农业工程学院机械电子工程学院）；Australian Institute for Machine Learning, University of Adelaide（阿德莱德大学澳大利亚机器学习研究所）
Evaluating Data Augmentation Techniques for Coffee Leaf Disease Classification
National University of Science and Technology POLITEHNICA Bucharest（布加勒斯特理工国立科学技术大学）；National Institute for Research and Development in Informatics - ICI Bucharest（布加勒斯特国家信息学研发研究所（ICI布加勒斯特））；Academy of Romanian Scientists（罗马尼亚科学家学院）
