Enhancing Precision of Crop Farming towards Smart Cities: An Application of Artificial Intelligence
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5.2. Data Collection and Training
Historical information about the farm, such as its weather patterns (temperature, humidity, and precipitation), soil moisture, and crop yield records, was gathered before the experiment began. Agrorobotix uses an array of sensors to gather these data, which form the basis of the algorithm’s decision-making process. Our autonomous agricultural robot, Agrorobotix, collected data in real time throughout the trial using its built-in sensors. Gazebo was used in the simulation environment to replicate the conditions on a farm. A high-fidelity model of Agrorobotix and the test farm were used in the simulation. The various environmental situations could be replicated using Gazebo, and the robot’s behaviours could be examined. Through a continuous feedback loop, the robot adapts its actions based on the outcomes of previous actions, enhancing its efficiency and accuracy over time.
The collected data were pre-processed to be fed into the DRL model. The state space vector is defined as described in Equation (13):
where represents the soil moisture, represents weather information, and represents the robot’s position, which was obtained from GPS data.
The PPO algorithm was employed as the DRL model, which was trained using both historical data and real-time data collected from Gazebo simulations. The objective was to maximize the cumulative reward .
The learning curves for these tasks are given in Figure 7. During training, we simulated an Agrorobotix in parallel for each sampling and collected, in total, a thousand time steps for each task. The policy was modified iteratively through epochs, and the training continued until convergence.
Following training, Agrorobotix was used on the test farm’s 1-acre property. Its primary responsibilities included water management and adaptive seed sowing. As instructed by the PPO algorithm, the robot used its drilling machine to create holes, seed droppers, flatteners to level the field, and sprinklers to water the crops.
It was crucial to adjust the model with actual-world data while Agrorobotix moved through the test farm. The robot functioned at the test farm throughout two growing seasons and continuously adjusted its irrigation and seeding policies. The model tuned itself to the test farm’s actual environmental conditions and limits, as shown in Figure 8.
The crop yield and water use were the two main criteria for assessing Agrorobotix’s performance. Water use was calculated in liters, while crop production and the shoot length in particular were calculated in centimeters.
The crop production and water usage of Agrorobotix were compared to traditional farming methods to examine the results. The effectiveness and advantages of using a Deep Reinforcement Learning-based strategy in agriculture were established through this comparison.
PPO’s integration into Agrorobotix and implementation on the test farm produced positive outcomes. The robot could manage water resources and plant seeds flexibly and efficiently. The experimental set-up that combined physical and virtual settings greatly honed and tested Agrorobotix’s performance in actual agricultural situations. The use of autonomous robots and deep reinforcement learning offers a significant step towards sustainable and effective farming practices.
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