article · Smart Agricultural Technology
Mango is a valuable fruit crop rich in nutrients, yet diseases and pests create major barriers to productivity and fruit quality. To address this, a disease detection system was developed using an ensemble convolutional neural network that combines GoogLeNet and VGG16 architectures. The model was trained and evaluated on healthy and diseased leaf photographs collected from cultivation sites in the Amhara Region. Pre-processing incorporated image resizing, noise reduction, and augmentation, alongside k-means and Mask R-CNN segmentation techniques. Important visual features extracted through convolutional layers were categorised using fully-connected classifiers. Across testing on these datasets, the ensemble approach reached 99.87 percent training accuracy, 99.72 percent validation accuracy, and 99.21 percent testing accuracy, showing strong classification capability for mango leaf health.
Mango is a globally significant, nutrient-rich crop, but diseases and pest infestations constrain farm yields. Automating the identification of leaf diseases through highly accurate image recognition offers a path towards timely interventions, helping to minimise crop damage, sustain harvest yields, and protect farmer livelihoods.
The method could enable automated plant pathology tools for agricultural extension workers, commercial growers, or agritech mobile applications. At present, this represents applied research tested on image datasets from the Amhara Region. Real-world deployment would require integration into field-ready digital tools, as the abstract does not indicate whether testing occurred on live farms or outside photographic evaluation.
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Mango is a highly significant fruit crop that thrives in a variety of agro-ecologies around the world. Mangoes are rich in vitamins and minerals. However, its yield is currently severely constrained due to disease and pest infestations. Thus, in order to improve mango fruit quality and productivity, illnesses and insect pests must be detected early on. In this study, we conceived and constructed a mango leaf disease detection mechanism utilizing an ensemble convolutional neural network approach. Healthy and diseased mango leaf images were manually obtained from main producing locations in Amhara Region for Merawi fruit and vegetable research identification. To improve the datasets, several pre-processing procedures (such as image resizing, noise reduction, and image augmentation) were used. To improve classification performance and meet the study's purpose, various segmentation approaches such as k means and Mask R-CNN were applied. Furthermore, following pre-processing and segmentation, features of mango leaf images were retrieved using CNN to obtain important features. The classification model was then constructed using fully-connected layer classifiers on the retrieved features of mango leaf images. The ensemble proposed GoogLeNet and VGG16 based CNN model in the study encompasses various operations, including dataset collection, image preprocessing, noise removal, segmentation, data augmentation, feature extraction, and classification. Upon testing, the model demonstrated impressive performance with 99.87% training classification accuracy, 99.72% validation accuracy, and 99.21% testing accuracy. This indicates the effectiveness of the ensemble approach in achieving high accuracy in image classification tasks.
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DOI: 10.1016/j.atech.2024.100476
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