DETECTION OF NUTRIENT DEFICIENCY IN CROP PLANTS USING DEEP LEARNING MODEL

Nutrient deficiency significantly affects plants growth and crop productivity. Hence, early detection is essential for effective agricultural management. This study aimed to develop a deep learning model for automated detection of healthy, potassium deficient (K) and nitrogen-potassium deficient (N-K) status in crop plants using leaf images. EfficientNetB0 architecture was used to build the model which was then trained and tested. The final model achieved 85.80% training accuracy and 76.61% test accuracy. Precision, recall and F1 score for test set was 76.03%, 76.49% and 75.93%%, respectively, indicating highly reliable prediction performance. Confusion matrix analysis for test set showed correct detection of 91 healthy, 72 potassium deficient and 122 nitrogen-potassium deficient crop leaves. Overall, the findings of the study indicate that the proposed model is effective for identifying nutrient deficiency in crop plants from leaf images.