Artificial Intelligence Based Integrated Technological Advancements for Automated Crops Diseases Identification in Smart Farming

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.