Crop Scouting and Surrounding Awareness for Specialty Crops

Abstract

Until the beginning of the twentieth century, farmers typically managed small family farms where decisions used to be made according to the experience accumulated over long working days in their fields. The size of these fields allowed for a precise management of the land. However, with the advent of mechanization, the land managed by every farmer increased significantly and decisions began to be made through sampling, which often led to unrealistic generalizations. With the adoption of precision agriculture, the acquisition of massive data provided a way to systematically scout crops and enhance decision-making at management level. This chapter describes the main features desired for autonomous scouting machines. In particular, it focuses on the mechanical design and dynamic performance of agricultural robots, their reliability and safety needs, and the agile control of actuators. Also, the chapter presents a discussion on how usability and user acceptance would be impacted by the careful development of human-machine interaction through effective graphic user interfaces and ergonomic joysticks. As the principal task of scouting machines is the perception of the surrounding environment that requires the optimal combination of sensors, this chapter also explains the basic principles of nonvisual perception through ultrasonic devices and laser rangefinders, as well as vision systems and the information they retrieve as digital images. Specifically, it covers the implementation of noninvasive crop sensing devices to assess canopy properties, plant stress, and vegetative indices while a vehicle is moving in the field. Finally, it discusses how to deal with crop data through two-dimensional maps, by applying geospatial techniques for data smoothing and clustering, to reach the ideal situation in which a map delineates a few treatment zones directly applicable to the equipment commonly available in the farm. A use case is presented for vineyard scouting with ground robots for water status assessment.