Abstract:
The rapid growth of agricultural technology has prompted the investigation of novel crop disease detection
approaches. This paper presents an integrated method for the autonomous detection of agricultural diseases that
combines the capabilities of a quadcopter with deep learning methods. The quadcopter is an aerial platform outfitted
with high-resolution cameras to gather detailed field photographs effectively. Create a reliable and precise disease
identification system using deep learning methods, specifically Convolutional Neural Networks (CNNs). The steps in
our method are as follows: employing a quadcopter to capture photographs, pre-processing the images, feature
extraction using a pre-trained CNN, and disease classification using a specially trained deep neural network. This work
with agricultural specialists ensures the precise annotation of disease labels to make it easier to create a trustworthy
dataset. Test the proposed system on several crops and agricultural settings, showcasing its capacity to identify and
categorize various illnesses in real time precisely. Evaluate the model's precision, recall, and F1-score performance
through extensive experimentation and contrast it with conventional manual disease detection techniques. The outcomes
demonstrate the effectiveness and efficiency of our automated strategy and highlight its potential to transform disease
management in agriculture completely. This study makes a contribution to the field of robotics, computer vision, and
agriculture by providing a cutting-edge solution that reduces the negative effects of crop diseases on the economy and
the environment through prompt and accurate diagnosis.
