Although Convolutional neural networks (CNNs) are widely used for plant disease detection, they require a large number of training samples when dealing with wide variety of heterogeneous background. In this work, a CNN based dual phase method has been proposed which can work effectively on small rice grain disease dataset with heterogeneity. At the first phase, Faster RCNN method is applied for cropping out the significant portion (rice grain) from the image. This initial phase results in a secondary dataset of rice grains devoid of heterogeneous background. Disease classification is performed on such derived and simplified samples using CNN architecture. Comparison of the dual phase approach with straight forward application of CNN on the small grain dataset shows the effectiveness of the proposed method which provides a 5 fold cross validation accuracy of 88.07%.
Exploring Convolutional Neural Networks for Rice Grain Classification: An Explainable AI Approach
National Centre for Physics (NCP)（国家物理中心（NCP））；University of Central Punjab (UCP)（中旁遮普大学（UCP））；Wenn ASA School of Informatics and Robotics（Wenn ASA信息学与机器人学院）；Shanghai JiaoTong University（上海交通大学）；Institute of Arts and Culture (IAC)（艺术与文化学院（IAC））
An Improved Pure Fully Connected Neural Network for Rice Grain Classification
College of Information（信息学院）；Electrical Engineering China Agricultural University Beijing, China（电子工程中国农业大学北京）；College of Land Science（土地科学学院）；Technology China Agricultural University Beijing, China（技术中国农业大学北京）；Electrical Engineering China Agricultural University Beijing, China Corresponding author（电子工程中国农业大学北京）；School of Computer Science（计算机科学学院）；Engineering Huizhou University Huizhou, China（工程惠州市大学）
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）
MT-CYP-Net: Multi-Task Network for Pixel-Level Crop Yield Prediction Under Very Few Samples
State Key Laboratory of Water Resources Engineering and Management, Wuhan University（水利水电工程与管理国家重点实验室，武汉大学）；School of Computer Science, Wuhan University（武汉大学计算机学院）；State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University（测绘遥感信息工程国家重点实验室，武汉大学）；College of Agricultural Science and Engineering, Hohai University（河海大学农业科学与工程学院）
