Plant Health Assessment Based on Precision IoT Leaf Phytometry Techniques

• Main Authors: Dolawatta, Ravindu, Kirindigoda, Janinda, Rathnayake, Kavindu, Alexander, Shenita, Jayakody, Anuradha, Gamage, Narmada
• Format: Conference Proceeding
• Language: English
• Subjects: Agricultural IoT; Cabbage (Brassica sp.) Plant Leaf Quality Assessment; Capacitance; Capacitance measurement; Leaf Phytometry; Magnetic field measurement; Plant Stress; Plants (biology); Precision Agriculture; Temperature measurement; Temperature sensors; Thickness measurement
• ISSN: 2837-5424
• DOI: 10.1109/ICAC60630.2023.10417528

Leaf Phytometry supersedes conventional Agri-IoT techniques because of its ability to monitor plant data from leaves, more directly, accurately, and extensively, compared to typical indirect methods that only target monitoring environment variables of a plant. It belongs to precision agriculture which is a fundamental method for plant quality and health assessment, optimization, and resource savings and has been the impetus behind agricultural success despite the extreme climate conditions of Middle Eastern countries. Four leaf phytometric techniques were researched and implemented that measure; leaf thickness via magnetic field measurement, chlorophyll level, and nitrogen level estimation via spectrophotometry, leaf capacitance via measuring capacitance, and leaf surface temperature compared to environment temperature, on the experiment plant i.e., Cabbage (Brassica sp.) to obtain highly accurate relative measurements and, rote-experiment and estimation-based absolute measurements. Four effective and low-cost leaf clips were developed for the above cases based on external research and utilizing alternative resources. Finally, a Neural Network based ML model was trained to identify plant stress from the output of leaf clips.