Artificial Intelligence: A Promising Tool for Application in Phytopathology

González-Rodríguez, Victoria E.; Izquierdo-Bueno, Inmaculada; Cantoral, Jesús M.; Carbú, María; Garrido, Carlos

doi:10.3390/horticulturae10030197

Cited by 2 publications

(1 citation statement)

References 141 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In addition, AI technology enables automated disease detection and diagnosis through high-precision image recognition, which has been reported to have an accuracy rate of more than 95%, which is significantly better than that of traditional manual visual assessment methods. AI technology can also predict the risk of disease outbreaks and real-time monitoring, among other things, which demonstrates the potential of AI to enhance the process of disease prevention and precision management decision-making [ 14 ]. The amount of crop disease image data is huge, and cannot be processed effectively by traditional methods.…”

Section: Introductionmentioning

confidence: 99%

Identification of Pepper Leaf Diseases Based on TPSAO-AMWNet

Wan,

Zhu,

Dai

et al. 2024

Plants

View full text Add to dashboard Cite

Pepper is a high-economic-value agricultural crop that faces diverse disease challenges such as blight and anthracnose. These diseases not only reduce the yield of pepper but, in severe cases, can also cause significant economic losses and threaten food security. The timely and accurate identification of pepper diseases is crucial. Image recognition technology plays a key role in this aspect by automating and efficiently identifying pepper diseases, helping agricultural workers to adopt and implement effective control strategies, alleviating the impact of diseases, and being of great importance for improving agricultural production efficiency and promoting sustainable agricultural development. In response to issues such as edge-blurring and the extraction of minute features in pepper disease image recognition, as well as the difficulty in determining the optimal learning rate during the training process of traditional pepper disease identification networks, a new pepper disease recognition model based on the TPSAO-AMWNet is proposed. First, an Adaptive Residual Pyramid Convolution (ARPC) structure combined with a Squeeze-and-Excitation (SE) module is proposed to solve the problem of edge-blurring by utilizing adaptivity and channel attention; secondly, to address the issue of micro-feature extraction, Minor Triplet Disease Focus Attention (MTDFA) is proposed to enhance the capture of local details of pepper leaf disease features while maintaining attention to global features, reducing interference from irrelevant regions; then, a mixed loss function combining Weighted Focal Loss and L2 regularization (WfrLoss) is introduced to refine the learning strategy during dataset processing, enhancing the model’s performance and generalization capabilities while preventing overfitting. Subsequently, to tackle the challenge of determining the optimal learning rate, the tent particle snow ablation optimizer (TPSAO) is developed to accurately identify the most effective learning rate. The TPSAO-AMWNet model, trained on our custom datasets, is evaluated against other existing methods. The model attains an average accuracy of 93.52% and an F1 score of 93.15%, demonstrating robust effectiveness and practicality in classifying pepper diseases. These results also offer valuable insights for disease detection in various other crops.

show abstract

Section: Introductionmentioning

confidence: 99%