The identification and classification of plant diseases is challenging due to the complexity and variability of symptoms across different species, and the need for timely and accurate diagnosis to effective disease management. However, the existing methods of diagnosing plant diseases often require extensive expert knowledge and can be labor-intensive and time-consuming. This paper presents a novel method leveraging a dynamic self-attention mechanism within convolutional neural networks to enhance the classification accuracy of plant diseases. By allowing the mechanism to focus adaptively on the most informative parts of the input images, this method successfully detects the complex patterns and relationships within the disease symptoms. This method is evaluated by incorporating it in a model that specifically leverages the strengths of EfficientNet architecture combined with the Scaled Exponential Linear Unit (SeLU) activation function on grape leaves dataset containing various types of diseases. This model demonstrates superior performance in detecting a variety of plant diseases, surpassing existing baseline convolutional network methods in both speed and accuracy. This research not only advances the field of plant disease management with cutting-edge AI techniques but also offers a scalable and efficient tool for agriculture practitioners to combat plant diseases more effectively.

Exchange of information, financial transactions, and involvement of intermediaries play an important role in agriculture. However, current ICT-based agriculture systems are centralized and fail to address problems such as information asymmetry and reliability. Besides, consumers are keen to know the origin of food (traceability), intermediaries focus on transparency of shareable data and tracking of transactions are the areas to be studied in depth for smart and sustainable farming. Disruptive technology like blockchain is suitable to effectively manage smart farming. Blockchain promises reliability and authenticity of source of information as it can track the origin of a transaction. Several researchers have proposed conceptual frameworks and models. However, there is a limited study on real-time architecture that uses blockchain and decentralized applications to address the issues for smart farming. This research in progress paper presents the current gaps and focuses on incorporating blockchain to a smart and sustainable farming by developing an effective architecture. The research outcomes of the proposed study will benefit the agriculture value chains, intermediaries, farmers, and food suppliers in Australia.