A Neural Network-based Approach for Apple Leaf Diseases Detection in Smart Agriculture Application

Plant diseases significantly harm agriculture, which has an impact on nations' economies and levels of food security. Early plant disease detection is essential in smart agriculture. For the diagnosis of plant diseases, a number of methods, including imaging, have been used recently. Some of the existing methods for plant disease detection using imaging have limitations as firstly, high computational cost, some methods require complex image processing algorithms or manual design of features that can increase the time and resources needed for the detection. Secondly, low accuracy, most of the methods rely on simple classifiers or handcrafted features that may not capture the subtle differences between different diseases or healthy leaves. Thirdly, dependency on expert knowledge, some methods need human intervention or prior knowledge of the diseases and pests to perform the detection. These limitations are not suitable for the problem at hand because they can affect the efficiency of the detection system. In this study, three apple tree leaf diseases—apple black spot, Alternaria, and Minoz blight—are detected using a neural network (NN) and a digital image processing technique. The sample images are prepared, processed, and used to extract attributes using a digital image processing approach, and the NN is used to classify the diseases. An evaluation of the proposed system's performance in identifying illnesses in apple trees shows satisfactory accuracy and strong overall performance. Additionally, when compared to other techniques already in use, this strategy is more effective at diagnosing.

@article{Gan2023,
  title     = {A Neural Network-based Approach for Apple Leaf Diseases Detection in Smart Agriculture Application},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {14},
  number    = {11},
  year      = {2023},
  publisher = {The Science and Information Organization},
  author    = {Shengjie Gan and Defeng Zhou and Yuan Cui and Jing Lv},
  doi       = {10.14569/IJACSA.2023.0141158},
  url       = {https://doi.org/10.14569/IJACSA.2023.0141158}
}