Plant diseases pose significant threats to agriculture, impacting both food safety and public health. Traditional plant disease detection systems are typically limited to recognizing disease categories included in the training dataset, rendering them ine
The classification and recognition of foliar diseases is an increasingly developing field of research, where the concepts of machine and deep learning are used to support agricultural stakeholders. Datasets are the fuel for the development of these technologies. In this paper, we release and make pub...
Plant diseases and pests detection is a very important research content in the field of machine vision. It is a technology that uses machine vision equipment to acquire images to judge whether there are diseases and pests in the collected plant images [1]. At present, machine vision-based plan...
Plant diseases and pests are important factors determining the yield and quality of plants. Plant diseases and pests identification can be carried out by means of digital image processing. In recent years, deep learning has made breakthroughs in the fiel
The authors of [29] have used the dataset “PlantVillage” to depict four bacterial infections, two viral diseases, two mold diseases, and one mite-related ailment. Images of unaffected leaves were also shown for a total of 12 crop species. For the development of prediction models, ML approa...
Shrub encroachment into grasslands affects species biodiversity and ecosystem functioning, but its impact on herbaceous diseases and the role of climatic factors remain unclear. This study finds that shrubs reduce pathogen load in colder regions but may increase it in warmer regions, with temperature be...
Using pre-trained CNNs like GoogleNet and AlexNet could classify twenty-six pests and diseases within fourteen plant species [7]; 99.34% was obtained through GoogleNet. AlexNet, GoogleNet, VGG, feat, and AlexNetOWTBn could recognize 58 leaf diseases [9]. A nine-layered deep convolutional network...
Additionally, we employ a transfer learning and finetuningapproach to enhance the prediction accuracy of tomato leaf diseases. To evaluate the performanceof MX-MLF2, we compare it with other pre-trained deep learning models such as MobileNet V1, MobileNetV2, and Inception V3." 展开 关键词: ...
We also present a visualization of the topics that provides plant scientists an intuitive tool for hyperspectral imaging. In short, our analysis and visualization of characteristic topics found during symptom development and disease progress reveal the hyperspectral language of plant diseases....
In future work, more data at different stages of different diseases will be collected with versatile sensors, like infrared camera and multispectral camera. The deep learning model can be associated with treatment recommendation, yield prediction, and so on. Conflicts of Interest The authors declare ...