AI-Driven Automation in Solanaceae Farming for Disease and Pest Management

The integration of artificial intelligence automation yields contemporary changes in Solanaceae farming by managing diseases and pests and achieving enhanced output with sustainability objectives. Scientists have evaluated the utilization of artificial intelligence (AI) and machine-learning technologies for targeted agricultural operations concerning the cultivation of tomato, potato, and eggplant crops. Solanaceae cultivation uses two main AI applications through deep learning algorithms for disease detection across images as well as sensor networks and robotic drone operations for field inspection and early warning systems. New technological applications enable fast and precise pest identification in plants, thus enabling better resource management for intervention purposes. Pest Management Systems operated by artificial intelligence systems utilize real-time observation and examination platforms. The implemented analysis methods create positive outcomes that promote efficient environmental protection of agricultural crops. The use of AI for automation enables improvements in agricultural practices when operating farm equipment, irrigation equipment, and fertilization systems. Technology integration encounters various hurdles because farmers with small agricultural lands must spend considerable money on initial funding and maintain data collection platforms. The system requires immediate solutions to combat ethical problems, including privacy breaches in data processing and system-based discrimination. Research needs to build affordable AI tools that farmer communities can access, and develop environmentally sustainable agricultural systems through AI methodology. AI-powered automated farming technology provides Solanaceae farmers with an opportunity to improve their resource management and achieve higher yield production for enhanced environmental sustainability.