Research

These tools help reduce waste and increase resilience to climate stressors. However, there is a need to train agricultural scientists in using these tools. An interdisciplinary team will train five Ph.D. students to develop solutions for specialty crop systems using precision agriculture and AI. This project aims to advance the Penn State Specialty Crop program and develop sustainable solutions for the US specialty crop industry.

Research Area 1: Advancing specialty crop production by developing integrated solutions using internet of things (IoT) and artificial intelligence (AI) technologies.

Recent advances in AI and IoT have addressed many agricultural challenges, optimizing resource utilization. AI and IoT-enabled solutions are now essential for precision agriculture, such as crop growth monitoring, weed control, pest detection, precision spraying, smart irrigation, and plant phenotyping. Our interdisciplinary team and students will focus on the following tasks:

• Developing AI models for monitoring specialty crops using various sensing systems such as RGBD cameras.
• Creating a low-cost IoT system to connect sensors for monitoring crop stress, soil moisture, and environmental conditions.
• Integrating IoT and AI systems for automatic crop management in controlled environments and open fields, such as water and nutrient management in greenhouse vegetables.

Research Area 2: Unmanned aerial sprayer-based tree fruit crop load management and performance evaluation using hyperspectral imaging.

Precision chemical thinning in tree fruit is crucial for optimizing yield and quality. Unmanned aerial sprayers allow precise application of thinning agents. Understanding plant biology is key to creating algorithms for thinning and improving fruit production. Using hyperspectral imaging to assess fruit health post-thinning helps growers manage crop loads efficiently. This cohort will research:

• UAV sprayer practices for chemical thinning and performance evaluation.
• Large-scale flower stage estimation with aerial and ground-based scanning.
• Trials on drone spraying in high-density apple and peach systems.
• Using hyperspectral imaging to evaluate post-thinning fruit growth for crop load management.

Research Area 3: Precision weed management with targeted spraying.

Weed control, including root sucker management, is vital for tree fruit production. Herbicide use can harm the environment and lead to resistant weeds. This research aims to develop an AI-based precision spraying system to reduce herbicide use. The cohort will work on:

• Identifying optimal herbicide chemicals and timing for effective weed and sucker control in apple orchards.
• Developing an AI-based machine vision system for weed and sucker detection in orchards.
• Conducting field trials to compare precision weed management with conventional methods and disseminate findings through extension activities.

Research Area 4: Crop health and pest management for vegetable crops.

Insects are a major issue in vegetable production, reducing crop value. Early detection and control are critical to preventing infestations. AI-based crop monitoring systems could help manage insect populations. This cohort will research:

• Developing a low-cost system for measuring environmental conditions and predicting insect events.
• Creating an image-based trapping system for real-time insect data collection in vegetable fields.
• Developing a decision support tool for insect management in vegetable fields.

Research Area 5: IoT-based precision monitoring for casing moisture regulation in button mushroom production.

Maintaining the ideal humidity (80-90%) is crucial for mushroom growth. Precision agriculture technologies, such as IoT-based sensing, can help regulate humidity to optimize production. The cohort will focus on:

• Investigating optimal casing moisture levels for mushroom production across different facilities.
• Developing a sensor-based IoT system for real-time moisture and environmental monitoring.
• Evaluating mushroom quality and yield under precision and conventional management, providing guidance for farmers.