VineInspector: The Vineyard Assistant

Proximity sensing approaches with a wide array of sensors available for use in precision viticulture contexts can nowadays be considered both well-know and mature technologies. Still, several in-field practices performed throughout different crops rely on direct visual observation supported gained experience to assess aspects plants’ phenological development, as well indicators relating the onset common plagues diseases. Aiming mimic observation, this paper presents VineInspector: low-cost, self-contained easy-to-install system, which is able measure microclimatic parameters, also acquire images using multiple cameras. It built upon stake structure, rendering it suitable deployment across vineyard. The approach through distinguishable attributes are detected, classified tallied periodically acquired images, makes artificial intelligence approaches. Furthermore, made an IoT cloud-based support system. VineInspector was field-tested under real operating conditions not only robustness functionality hardware solution, but AI approaches’ accuracy. Two applications were developed evaluate VineInspector’s consistency while viticulturist’ assistant everyday practices. One intended determine size very first grapevines’ shoots, one required parameters known 3–10 rule predict primary downy mildew infection. other tally grapevine moth males captured sex traps. Results show that logical step smart proximity monitoring by mimicking from experienced viticulturists. While latter traditionally responsible set field, these time resource consuming. proven effective two practices, performing them automatically. Therefore, enables continuous assessment vineyard’s development more efficient manner, making way assertive timely against pests