In order to resolve some of these challenges, this paper presents crop pests/disease datasets sourced from local farms in Ghana. The dataset is presented in two folds; the raw images which consists of 24,881 images (6,549-Cashew, 7,508-Cassava, 5,389-Maize, and 5,435-Tomato) and ...
the selected deep neural network frameworks are used to pre-train the model on the auxiliary domain dataset.Finally,the parameter-based transfer learning method was used to construct the corresponding crop disease recognition model in the target.In the experiments,multiple different datasets and ...
Our final score was a log loss of 0.288 on the test dataset ranking 53rdon thefinal leaderboardamong 839 participants. Over the test sample of 610 images, this translated to approximately 580 correctly classified instances – in other words, the model accuracy is 95%. This is quite good for...
crop-disease-diagnosis-service crop disease diagnosis service application with image-captioning and object-detection(deep learning) paper Lee, D.I.; Lee, J.H.; Jang, S.H.; Oh, S.J.; Doo, I.C. Crop Disease Diagnosis with Deep Learning-Based Image Captioning and Object Detection. Appl....
TABLE 1. Comparison results of different values of loss function parameter B on the corn-rice disease dataset. Parameter BPrecision (%)Recall (%)mAP@0.5 (%) 0.60 91.0 88.7 91.6 0.65 92.0 91.9 93.3 0.70 92.7 90.6 92.6 The detection results of different models on crop diseases and pests in...
Use saved searches to filter your results more quickly Cancel Create saved search Sign in Sign up Reseting focus {{ message }} YOLOv8-YOLOv11-Segmentation-Studio / Crop-Disease-Identification292 Public Notifications You must be signed in to change notification settings Fork 0 ...
Due to the small dataset and the easy influence of complex background such as illumination and clutter, as shown in Fig. 1, the detection accuracy of crop insect pest is low, which is over-detection or under- detection. In this Section, an improved U-Net model namely dilated multi-scale...
To ensure the reliability of the information collected from the papers, we carefully checked and compared all the collected data with the original paper several times. Quality control of the database was conducted based on outlier detection. For each crop, the outliers of crop yield in CT system...
computer-vision agriculture pytorch yolo object-detection crop-detection weed-detection yolov7 Updated Aug 9, 2024 Jupyter Notebook Raymond-ap / disease-detection Star 1 Code Issues Pull requests Crop Disease Classification Training Model. This is a one part of the entire project. The complet...
In the last few years, plant disease monitoring using UAV platforms is one of the most important agriculture applications that have gained increasing interest by researchers. Accurate detection and treatment of plant diseases at early stages is crucial to improving agricultural production. To this end...