Preliminary Evaluation of a Blast Sprayer Controlled by Pulse-Width-Modulated Nozzles.

Precision spraying relies on the response of the spraying equipment to the features of the targeted canopy. PWM technology manages the flow rate using a set of electronically actuated solenoid valves to regulate flow rate at the nozzle level. Previous studies have found that PWM systems may deliver incorrect flow rates. The objective of the present study was to characterize the performance of a commercial blast sprayer modified with pulse-width-modulated nozzles under laboratory conditions, as a preliminary step before its further field validation. Four different duty cycles (25 percent, 50 percent, 75 percent and 100 percent) and four different pressures (400 kPa, 500 kPa, 600 kPa and 700 kPa) were combined to experimentally measure the flow rate of each nozzle. Results showed that the PWM nozzles mounted in the commercial blast sprayer, under static conditions, were capable of modulating flow rate according to the duty cycle. However, the reduction of flow rates for the tested duty cycles according to pressure was lower than the percentage expected. A good linear relation was found between the pressure registered by the control system feedback sensor and the pressure measured by a reference conventional manometer located after the pump. High-speed video recordings confirmed the accurate opening and closing of the nozzles according to the duty cycle; however, substantial pressure variations were found at nozzle level. Further research to establish the general suitability of PWM systems for regulating nozzle flow rates in blast sprayers without modifying the system pressure still remains to be addressed.

Sensing Architecture for Terrestrial Crop Monitoring: Harvesting Data as an Asset.

Very often, the root of problems found to produce food sustainably, as well as the origin of many environmental issues, derive from making decisions with unreliable or inexistent data. Data-driven agriculture has emerged as a way to palliate the lack of meaningful information when taking critical steps in the field. However, many decisive parameters still require manual measurements and proximity to the target, which results in the typical undersampling that impedes statistical significance and the application of AI techniques that rely on massive data. To invert this trend, and simultaneously combine crop proximity with massive sampling, a sensing architecture for automating crop scouting from ground vehicles is proposed. At present, there are no clear guidelines of how monitoring vehicles must be configured for optimally tracking crop parameters at high resolution. This paper structures the architecture for such vehicles in four subsystems, examines the most common components for each subsystem, and delves into their interactions for an efficient delivery of high-density field data from initial acquisition to final recommendation. Its main advantages rest on the real time generation of crop maps that blend the global positioning of canopy location, some of their agronomical traits, and the precise monitoring of the ambient conditions surrounding such canopies. As a use case, the envisioned architecture was embodied in an autonomous robot to automatically sort two harvesting zones of a commercial vineyard to produce two wines of dissimilar characteristics. The information contained in the maps delivered by the robot may help growers systematically apply differential harvesting, evidencing the suitability of the proposed architecture for massive monitoring and subsequent data-driven actuation. While many crop parameters still cannot be measured non-invasively, the availability of novel sensors is continually growing; to benefit from them, an efficient and trustable sensing architecture becomes indispensable.

Association of fruit, pericarp, and epidermis traits with surface autofluorescence in green peppers.

We investigated the association of blue fluorescence (excitation at 365 nm) with the traits of the fruit, pericarp, and epidermis in green peppers. The fruits were manually classified into two groups based on fluorescence brightness. The dark fluorescence group showed the accumulation of blue-absorbing pigments and a thicker cuticular structure, suggesting epidermal development.

Crop biometric maps: the key to prediction.

The sustainability of agricultural production in the twenty-first century, both in industrialized and developing countries, benefits from the integration of farm management with information technology such that individual plants, rows, or subfields may be endowed with a singular "identity." This approach approximates the nature of agricultural processes to the engineering of industrial processes. In order to cope with the vast variability of nature and the uncertainties of agricultural production, the concept of crop biometrics is defined as the scientific analysis of agricultural observations confined to spaces of reduced dimensions and known position with the purpose of building prediction models. This article develops the idea of crop biometrics by setting its principles, discussing the selection and quantization of biometric traits, and analyzing the mathematical relationships among measured and predicted traits. Crop biometric maps were applied to the case of a wine-production vineyard, in which vegetation amount, relative altitude in the field, soil compaction, berry size, grape yield, juice pH, and grape sugar content were selected as biometric traits. The enological potential of grapes was assessed with a quality-index map defined as a combination of titratable acidity, sugar content, and must pH. Prediction models for yield and quality were developed for high and low resolution maps, showing the great potential of crop biometric maps as a strategic tool for vineyard growers as well as for crop managers in general, due to the wide versatility of the methodology proposed.

Sensor architecture and task classification for agricultural vehicles and environments.

The long time wish of endowing agricultural vehicles with an increasing degree of autonomy is becoming a reality thanks to two crucial facts: the broad diffusion of global positioning satellite systems and the inexorable progress of computers and electronics. Agricultural vehicles are currently the only self-propelled ground machines commonly integrating commercial automatic navigation systems. Farm equipment manufacturers and satellite-based navigation system providers, in a joint effort, have pushed this technology to unprecedented heights; yet there are many unresolved issues and an unlimited potential still to uncover. The complexity inherent to intelligent vehicles is rooted in the selection and coordination of the optimum sensors, the computer reasoning techniques to process the acquired data, and the resulting control strategies for automatic actuators. The advantageous design of the network of onboard sensors is necessary for the future deployment of advanced agricultural vehicles. This article analyzes a variety of typical environments and situations encountered in agricultural fields, and proposes a sensor architecture especially adapted to cope with them. The strategy proposed groups sensors into four specific subsystems: global localization, feedback control and vehicle pose, non-visual monitoring, and local perception. The designed architecture responds to vital vehicle tasks classified within three layers devoted to safety, operative information, and automatic actuation. The success of this architecture, implemented and tested in various agricultural vehicles over the last decade, rests on its capacity to integrate redundancy and incorporate new technologies in a practical way.