COMPARISON OF RESNET18, EFFICIENTNETB0, AND VGG12 MODELS FOR CASSAVA PLANT GROWTH CLASSIFICATION

Monitoring the growth condition of cassava plants is a crucial aspect of agriculture to ensure optimal yields. The manual identification process is often subjective, time-consuming, and requires special expertise. This research aims to develop an automatic classification model using the Convolutional Neural Network (CNN) method to identify the growth conditions of cassava plants based on leaf imagery. A dataset of cassava leaf images with three categories Healthy, Stressed, and Diseased was used to train and test the CNN architecture. The research methodology includes data collection, image augmentation to enrich data variance, model architecture design, training, and evaluation. The results indicate that the developed CNN model achieves good accuracy in distinguishing among the three leaf condition classes. This system has the potential to assist farmers in detecting plant health issues early and accurately, thereby supporting more effective decision-making in agricultural management.