Precision Agriculture: Harnessing ML/DL for Rice Leaf Disease Diagnosis

Abstract

Machine Learning (ML) and Deep Learning (DL), more advanced techniques, are being studied in this particular paper as a method for early recognition and classification of leaf diseases on rice plants only for four common types of leaf diseases, such as Brown Spot, Hispa, Leaf Blast, and Bacterial Blight. The purpose was to devise an efficient and automated approach to detecting these situational diseases in Pakistan. This paper utilizes the YOLO11s model with geometrical transformations and other data augmentations for hyperparameter optimization. For the collection of objects under investigation, such as brown spots and other leaf diseases, such as the leaves of rice, a set of about six thousand (6000) pictures was collected. When these images were grown from those categories, this is made up of four disease types. Each set of images demanded pre-processing such as flipping, rotation, noise addition, HSV adjustments, and mosaic augmentation so that when presented under different conditions, the model is not only memorized but also generalized. The YOLO11s model was intentionally designed to be fast while avoiding diminishing accuracy as too many layers are added, which also brings about complexity and slow computation. New strategies like MixUp, Copy-Paste, and geometric operations were employed to make YOLO11s yield better results. The tasks above had a comprehensive model training a staggering 400 epochs, stopped early at 331, and the epoch 281 model was picked up as the best. The model evaluation phase, on the other hand, featured quantitative evaluation measures such as Mean Average Precision (mAP), precision, recall, f1 score, and runtime efficiency ratios over all stages of the model. The other method folds to 87.7% mAP@0.5, which has been the case for the best of the other tested equations CNNoll, YOLOv5, YOLOv8 Rice. The model in question had a precision of 0.859 and a recall of 0.795 with an inference speed of 3.1 ms per frame of images, making it possible to apply live agricultural systems. Compared to conventional methods like SVM, KNN, Decision Trees, and Random Forests, the YOLO11s model provides superior accuracy and significantly faster inference speed, making it more practical for real-world implementation. This research contributes to precision agriculture by offering a reliable, efficient tool for rice disease detection, helping farmers manage crop diseases more effectively, thereby increasing yield and reducing economic losses. Future work could involve integrating IoT for continuous monitoring, expanding the dataset for broader disease coverage, and developing lightweight model versions for deployment on mobile and edge devices in resource-limited agricultural environments.