The significant role of plants can be observed through the dependency of animals and humans on them. Oxygen, materials, food and the beauty of the world are contributed by plants. Climate change, the decrease in pollinators, and plant diseases are causing a significant decline in both quality and coverage ratio of the plants and crops on a global scale. In developed countries, above 80 percent of rural production is produced by sharecropping. However, due to widespread diseases in plants, yields are reported to have declined by more than a half. These diseases are identified and diagnosed by the agricultural and forestry department. Manual inspection on a large area of fields requires a huge amount of time and effort, thereby reduces the effectiveness significantly. To counter this problem, we propose an automatic disease detection and classification method in radish fields by using a camera attached to an unmanned aerial vehicle (UAV) to capture high quality images from the fields and analyze them by extracting both color and texture features, then we used K-means clustering to filter radish regions and feeds them into a fine-tuned GoogleNet to detect Fusarium wilt of radish efficiently at early stage and allow the authorities to take timely action which ensures the food safety for current and future generations.

通过动物和人类对植物的依赖可以观察到植物的重要作用。氧气，材料，食物和美丽的世界都是由植物贡献的。气候变化，传粉媒介减少和植物病害导致全球范围内植物和农作物的质量和覆盖率均显着下降。在发达国家，超过80％的农村生产是通过农作物生产的。但是，由于植物中普遍存在病害，据报道单产下降了一半以上。这些疾病由农业和林业部门识别和诊断。在大片区域上进行手动检查需要大量的时间和精力，从而大大降低了有效性。为了解决这个问题，