article · Potato Research
Potato blight, caused by the oomycete Phytophthora infestans, poses a major threat to potato yields and agricultural revenue. Detecting this disease accurately is vital for maintaining food security. An investigation evaluated several pre-trained deep learning architectures, identifying AlexNet as the most effective model for feature extraction. Following extraction, features were filtered using ten binary optimization algorithms, among which the Binary Waterwheel Plant Algorithm Sine Cosine performed best. These selected features were subsequently classified using five machine learning models: Decision Tree, Random Forest, Multilayer Perceptron, Support Vector Machine, and K-Nearest Neighbour. The Multilayer Perceptron delivered the highest performance, and its hyperparameters were further fine-tuned with the Waterwheel Plant Algorithm Sine Cosine. The resulting combined pipeline achieved a classification accuracy of 99.5 percent, demonstrating an effective and reliable approach to classifying late blight.
Late blight caused by Phytophthora infestans devastates potato crops, inflicting severe financial losses on farmers and threatening food security. Delivering highly accurate automated detection methods helps identify the disease early. By combining pre-trained neural networks with nature-inspired optimization algorithms, this methodology improves diagnostic accuracy to 99.5 percent, offering a robust computational framework for crop health monitoring.
The method could enable automated crop diagnostic tools for agricultural software developers, agronomists, or farming enterprises seeking accurate late blight identification. The pipeline is an applied algorithmic model tested on image features, reaching 99.5 percent classification accuracy. However, because the abstract details algorithmic benchmarking rather than operational deployment, field integration, or mobile device testing, it remains at an applied research stage prior to real-world commercial implementation.
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Abstract Potato blight, sometimes referred to as late blight, is a deadly disease that affects Solanaceae plants, including potato. The oomycete Phytophthora infestans is causal agent, and it may seriously damage potato crops, lowering yields and causing financial losses. To ensure food security and reduce economic losses in agriculture, potato diseases must be identified. The approach we have proposed in our study may provide a reliable and efficient solution to improve potato late blight classification accuracy. For this purpose, we used the ResNet-50, GoogLeNet, AlexNet, and VGG19Net pre-trained models. We used the AlexNet model for feature extraction, which produced the best results. After extraction, we selected features using ten optimization algorithms in their binary format. The Binary Waterwheel Plant Algorithm Sine Cosine (WWPASC) achieved the best results amongst the ten algorithms, and we performed statistical analysis on the selected features. Five machine learning models—Decision Tree (DT), Random Forest (RF), Multilayer Perceptron (MLP), Support Vector Machine (SVM), and K -Nearest Neighbour (KNN)—were used to train the chosen features. The most accurate model was the MLP model. The hyperparameters of the MLP model were optimized using the Waterwheel Plant Algorithm Sine Cosine (WWPASC). The results indicate that the suggested methodology (WWPASC-MLP) outperforms four other optimization techniques, with a classification accuracy of 99.5%.
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DOI: 10.1007/s11540-024-09735-y
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