Formalized mathematical framework for CNN-based on leaf disease detection leveraging block chain technology

Leaf diseases are a major threat to the productivity and quality of crops, affecting food security and economic development. Traditional methods of disease detection, such as manual inspection and laboratory testing, are time-consuming, labor-intensive, and limited in their ability to cover large areas. In recent years, machine learning has shown great potential for disease detection, providing a fast and accurate solution. However, the accuracy of machine learning models is dependent on the quality and integrity of the data used for training and inference. CNN will trained to identify acute and sharp points to detect whether the crop will suffer from disease or not. In similar way CNN converted numerical data need to reconvert in image format for which Deconvolutional networks are specifically designed and can help reconstruct the original image from the numerical data using TensorFlow library. In this context, block chain technology can play a key role in ensuring the security, reliability, and transparency of the data used for machine learning-based disease detection. In this paper, we propose a novel approach for leaf disease detection using deep learning in combination with Ethereum with Consortium type of block chain technology. Ethereum is a public block chain platform that allows the development of decentralized applications (DApps) and the execution of smart contract as well Ethereum also supports private or consortium block chain setups and the beauty of Consortium type block chain network is operated and controlled by a group of organizations rather than being completely public. The system collects data from leaf images, which is processed using machine learning algorithms to identify and diagnose the disease. The results are then securely stored on a block chain network, ensuring the reliability and transparency of the data. This approach provides a valuable contribution to the advancement of precision agriculture and food security.