A Deep-Siamese CNN-based Approach for Identifying Mycosphaerella Coffeeicola Disease in Coffee Foliage

Mycosphaerella Coffeeicola (Black Spot Disease) is the most destructive disease in coffee leaves and affects global production. Diseased coffee leaves can adversely affect coffee quality, discolor, distort, or rot the coffee leaves, and impair the coffee beans’ cherry, taste, and aroma. The disease can also affect the quality of the coffee beans, leading to lower market value. The management of the Coffee Production Department of India controls the disease through a combination of cultural practices, chemical control, and the use of resistant coffee varieties. In the existing work, using single-layer perceptron was trained and validated with 100 epochs to obtain an accuracy of $\mathbf{9 5. 7 1 \%}$. In the piece above of work, an infected leaf is accurately diagnosed using deep learning-based models by employing the Deep-Siamese-based CNN Approach, recognized by a deep Siamese Neural network architecture which is also called a Twin Neural network with high illness recognition precision and simplified hyper-parameters to maximize description capacity while avoiding overfitting concerns, using a network with high sickness detection precision and minimal hyper-parameters. The prototype was tested in the lab on natural plants for known sickness instances, and the results matched the model validation datasets. When compared to several deep-learning models, the suggested structure attained an accuracy of $\mathbf{9 6. 8 9 \%}$.