Precision Agriculture-Based Pest Detection Solutions Using AI Algorithms Simulated with Complex Logic Gates Integration and 3D Bioprinting

Algorithm or a computer blueprint code is a set of instructions and commands that are used to resolve issues or complete tasks in a computer system, programming language structure, or related datasets. A detailed analysis of various machine and deep learning algorithms, particularly focusing on their application in artificial intelligence (AI) and integration into real-world systems further explaining how they can be used in detecting diseases is highlighted. This whole process helps in discovering new disease-causing pests in turn resulting in the farmers detecting pests faster in their crops. It explores AI algorithms such as CNNs, SIFT, and Random Forests (RF), comparing their performance metrics using key factors like accuracy, precision, recall, and F1-score. Further, the focus shifts on the optimization of AI for mobile applications, specifically through the development of lightweight AI models suited for mobile devices. It addresses challenges such as limited computational resources and data connectivity, along with methods for optimizing inference speed and ensuring smooth integration with mobile platforms including their platforms and architecture. Therefore, the present review gives state of art advanced technologies in existing agricultural pest detection for effective control and improved productivity. The usage of 3D bioprinting enables the precise fabrication of biological structures that include tissues and organs. This mechanism can be used with 3D-printed plant tissues that are engineered with enhanced resistance to pests and diseases. A combination of different complex logic gates that provide the foundation for complex decision-making processes can be used to control pests in agricultural aspects. These logic gates can regulate gene expression for optimal growth and development. Logic gates can also control their release based on specific environmental cues. By combining these technologies, one can picture a future where agriculture is more sustainable, efficient, and resilient.