A Lightweight CNN For High-Accuracy Potato Blight Detection with Minimal Computational Overhead

Abstract

Potato, a crucial global crop, contributes significantly to economic growth, job creation, and food security. Rich in carbohydrates, fiber, magnesium, potassium, and vitamin C, it remains vulnerable to devastating blight diseases like early and late blight. Traditional methods for detection prove ineffective. Early and automated detection is paramount to minimize potato yield and quality losses, protecting farmer livelihoods. While Techniques used in DL and ML have been explored for potato blight detection, accuracy and computation time require improvement. This paper proposes a CNN designed to achieve high accuracy using reduced number of parameters and shorter processing time. Utilizing the PlantVillage dataset of 9485 images, to measure the effectiveness of the model, four evaluation metrics were used: precision, recall, F1-score, and accuracy, achieving 99.506%, 99.527%, and 99.515%, 99.65%, respectively. Compared to DL models that were pre-trained and previous work, the presented model outperformed all compared models, attaining an accuracy of 99.65% while boasting only 347,971 trainable parameters and an image processing time of 0.71 seconds.