Plant disease prevention and management have become increasingly important due to population growth and the consequent intensification of crops, which drive the development of plant protection products. However, monitoring tools have also been integrated to improve food security while avoiding crop losses. Nowadays, early detection of pathogens like Phytophthora infestans in potatoes, Xylella fastidiosa in olives, and Fusarium species in cereals is sometimes the only viable alternative to developing targeted interventions. Implementing advanced technologies such as biosensors and artificial intelligence (AI) in agriculture can solve this problem, ensuring food security while protecting environmental health. Recent innovations in biosensor technology include smart sensors for real-time monitoring of soil conditions (pH, moisture, or total nutrient uptake), weather patterns, and crop/plant health, including the early detection of plant pathogens, herbicides, pesticides, heavy metals, and toxins.

This systematic review explores biosensors under the scope of precision agriculture (Agriculture 4.0) by integrating them with AI and the Internet of Things (IoT) to develop improved disease management strategies, increase crop yield, and optimize resources. Moreover, smartphone-based biosensors and machine learning (ML) algorithms further enhance the practicality of in-field applications through rapid data analysis and integration with precision agriculture systems.

The advantages, challenges, and knowledge gaps regarding the adoption of AI in biosensors and precision agriculture are also discussed. Future research should assess the effectiveness of these technologies in enhancing efficiency, productivity, and sustainability to enhance real-time decision making in agriculture.