Deep Learning-Based Tomato Disease Detection for Smart Agriculture

Agriculture is critical in global food security but faces challenges like declining arable land and climate change. Machine learning, particularly deep learning with Convolutional Neural Networks (CNN), has shown promise in addressing agricultural problems. Efficient and accurate identification of plant diseases is essential for guaranteeing the long-term viability of food production. This paper investigates the use of CNNs to classify tomato diseases, employing a deep learning approach automatically. We utilize a publicly available dataset consisting of more than 16,000 photos of tomato leaves. These images cover both healthy and affected leaves by nine different diseases. Our proposal involves the development of three different CNN models, each with a different architectural design. In addition, we applied some Natural Language Generation (NLG) to generate reports and possible recommendations for detected diseases. The results of our research emphasize the capacity of deep learning to effectively and precisely detect diseases in tomatoes. This creates opportunities for the development of pragmatic smartphone applications that empower farmers by providing prompt disease diagnosis, empowering them to make well-informed decisions on crop management and pesticide utilization.