AI-Powered Disease Detection and Remedy Recommendation System for the Colocasia Plant

The ever-present need for super-efficient and nutrition-packed food has always challenged the agricultural industry. Diseases and pests lead the charge towards making this issue worse with their ever-growing impact. Crop quality and yield both suffer immensely. One such crop struggling with this condition is Taro, better known by its scientific name, Colocasia esculenta. It is a tropical vegetable grown in several countries in Asia and Africa. Like many others, Taro suffers from pestilence and diseases, and the damage can be severe due to its versatility and ingredients, as well as its nutritional profile, which consists of a ton of carbohydrates and fibre while having very low calories. Cultivating taro is not economically viable for most due to Phytophthora colocasiae, which causes "leaf blight." This condition reduces the yield by approximately 25%-50%. The plant is also affected by a mosaic virus, which is transmitted mainly by aphids. This virus weakens the plant and also destroys leaves. To ensure the impacts are not overly excessive, prompt action for disease control is imperative. AI solutions such as MobileNetV2, EfficientNetB3, and ConvNeXt, which assist in recognizing blight leaves and marking the infection of taro leaves, will be implemented in this study. Alongside image classification, actionable suggestion systems built using the Hugging Face library's Transformer-based models will provide immediate and useful suggestions, helping taro leaf disease management be effortless and efficient.