Towards Precision Agriculture: Harnessing Deep Learning for Accurate Plant Disease Diagnosis

To a large extent, food plants are responsible for satisfying the dietary requirements of the world’s population. Nevertheless, the danger posed by plant illnesses and diseases is significant, despite the fact that diagnoses are frequently based on the symptoms that may be observed. Studies are currently being conducted to investigate novel ways with the interest of developing more efficient strategies for protecting plants. Recent developments in technology have resulted in the creation of more convenient alternatives to the labor-intensive procedures that have been used in the past. The application of deep learning techniques, which are particularly useful in solving picture categorization issues, has emerged as a potentially fruitful route. The attention-based few-shot learning technique was utilized in this research project to accurately diagnose plant illnesses based on photographs of leaves. The photos were then classified into two distinct groups: healthy and sick. After being trained on a database that contains images of plant leaves, the suggested network model exhibits an impressively high level of accuracy, which is 95.96%.