| Autor: |
Redwan Ahmed Rizvee, Tasnim Hossain Orpa, Adil Ahnaf, Md Ahsan Kabir, Mohammad Rifat Ahmmad Rashid, Mohammad Manzurul Islam, Maheen Islam, Taskeed Jabid, Md Sawkat Ali |
| Jazyk: |
angličtina |
| Rok vydání: |
2023 |
| Předmět: |
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| Zdroj: |
Journal of Agriculture and Food Research, Vol 14, Iss , Pp 100787- (2023) |
| Druh dokumentu: |
article |
| ISSN: |
2666-1543 |
| DOI: |
10.1016/j.jafr.2023.100787 |
| Popis: |
Fruit production plays a significant role in meeting nutritional needs and contributing to the lessening of the global food crisis. Plant diseases are quite a common phenomenon that hampers gross production and causes huge losses for farmers in tropical South Asian weather conditions. In context, early-stage detection of plant disease is essential for healthy production. This research develops LeafNet, a convolutional neural network (CNN)-based approach to detect seven of the most common diseases of mango using images of the leaves. This model is trained specially for the pattern of mango diseases in Bangladesh using a novel dataset of region-specific images and is classified for almost all highly available mango diseases. The performance of LeafNet is evaluated with an average accuracy, precision, recall, F-score, and specificity of 98.55%, 99.508%, 99.45%, 99.47%, and 99.878%, respectively, in a 5-fold cross-validation that is higher than the state-of-the-art models like AlexNet and VGG16. LeafNet can be helpful in the detection of early symptoms of diseases, ultimately leading to a higher production of mangoes and contributing to the national economy. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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