A Novel AI Method for Efficient and Sustainable Tomato Farming.

Abstract. Using sensors and artificial intelligence (AI) in agriculture can significantly improve crop yields and the overall efficiency of farming operations. Sensors, such as temperature, moisture, and pH sensors, can be used to gather data on crop conditions in real time. This data can then be analyzed by Artificial Intelligence (AI) algorithms to optimize irrigation systems, fertilization schedules, and pest management plans. For example, a moisture sensor can detect when soil is too dry and trigger an automatic watering system. In contrast, a temperature sensor can alert farmers to potential heat stress in crops. One example of a sensor-AI system in agriculture is precision farming, which uses GPS and other technologies to collect data on soil conditions, weather patterns, and crop growth. This data is then analyzed by AI algorithms to create custom planting, fertilization, and harvesting plans for each individual field. This can lead to increased crop yields, reduced use of resources, and lower costs for farmers. Therefore, we have developed an AI system based on a raspberry pi platform to control the pH, moisture, and temperature in real-time to maximize the yield of tomato plants. Our system was able to increase tomato plant growth by 43% and tomato yield by 18%, while reducing water consumption by 36%. Our AI system can also be used to predict future crop yields based on historical data and current conditions. Our machine-learning algorithm can be trained on the gathered data such as weather patterns, soil conditions, and crop growth to predict future yields. This information can be used to make informed decisions about planting, fertilization, and harvesting. Overall, our proposed AI system can greatly improve the efficiency and yields of farming operations.