Jomer Allan G. Barcenilla, Christian V. Maderazo
Some of the most crucial factors that can influence the quality and quantity of harvested produce are soil fertility, water availability, climate, pests, and diseases. Pests and diseases have been a major problem for farmers in the production of crops, where they have caused up to 40% of the total global crop production. The model aims to determine the wellness of lettuce plants by identifying common pests and diseases through the use of image recognition techniques with deep learning called the Convolutional Neural Network (CNN). The model detects pests and diseases of lettuce plants such as anthracnose, leaf drop, powdery mildew, septoria leaf spot, big vein, bottom rot, and downy mildew based on their physical and observable characteristics. The training and testing of the model will be done over a dataset created specifically for the model. A 10-fold crossvalidation will then be performed to verify the models' accuracy. Categorical cross-entropy is utilized to ensure the model has no outlier predictions with huge errors against the observed data, making the system accurate and effective. The CNN model garnered the highest accuracy of 95.72% with 97.03% precision, 95.12% recall, and a 95.84% F1 score. © 2023 IEEE.
Information Sciences, and Mathematics, University of San Carlos, Department of Computer, Cebu City, Philippines
