Image-Based Potato Phoma Blight Severity Analysis Through Deep Learning

Mandal, Satyendra Nath; Mukherjee, Kaushik; Dan, Sanket; Ghosh, Pritam; Das, Shubhajyoti; Mustafi, Subhranil; Roy, Kunal; Chakraborty, Ashis Kumar

doi:10.1007/s40031-022-00820-9

Cited by 4 publications

(1 citation statement)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A proper pest and disease management of potato at an early stage significantly impacts yield and quality of produce [3,4]. From the literature, it is observed that 30 − 40% loss in potato production is due to disease and pests [5], which are caused by fungi, bacteria and viruses adversely affecting the productivity and quality of the produce. Therefore there is an urgent need for timely detection of pests and pathogens attacks for the effective management of diseases and thus preventing loss of yield and also assuring the quality of produce.…”

Section: Introductionmentioning

confidence: 99%

Leveraging Feature Fusion Ensemble of VGG16 and ResNet-50 for Automated Potato Leaf Abnormality Detection in Precision Agriculture

Trivedi,

Mahajan,

Maheshwari

et al. 2023

Preprint

View full text Add to dashboard Cite

In the era of advancement in technology and modern agriculture, early disease detection of potato leaves will improve crop yield. Various researchers have focused on disease due to different types of microbial infection in potato leaves using computer vision and machine learning approaches. In this paper, a data science approach for multiclass classification of potato normal and abnormal leaves due to fungal infection like early blight and late blight is performed using the ensembling of deep learning (DL) CNN models. Firstly, the performance of classification on potato disease is verified separately on VGG16 and ResNet−50 CNN models after pre-processing of the leaf dataset. The pre-processing includes noise removal and normalization. Further improvement in classification accuracy is achieved by the ensembling of VGG16 and ResNet−50 CNN models. The ensembling of CNN models is performed on the feature level by fusing features extracted using VGG16 and ResNet−50. From the experimental results, performed on publicly available datasets consisting of 2152 number of normal and abnormal images it is observed that the average classification accuracy of 98.22%, 96.16% and 95.68% is achieved using the proposed ensemble, VGG16 and ResNet−50 models respectively. The efficacy of the proposed approach (ensemble technique at feature level fusion) is verified in comparison with recently reported DL model-based approaches.

show abstract

Section: Introductionmentioning

confidence: 99%