Bases for pesticide dose expression and adjustment in 3D crops and comparison of decision support systems.

Authorities around the world have committed to limiting the use of chemical pesticides by reducing doses, among other strategies. Nevertheless, different dose expression models and decision support systems (DSSs) for dose adjustment coexist for high growing crops (3D crops). Among them, leaf wall area (LWA) and tree row volume (TRV) models have recently been proposed by the European and Mediterranean Plant Protection Organization (EPPO) for pre-registration trials. In this paper, the background and technical bases of six dose adjustment DSSs in fruit crops (PACE, AGMET, DOSA3D, OMAX and PULVARBO) and four in grape orchards (AGMET, OPTIDOSE, DOSAVIÑA and DOSA3D) are described and compared. The discussion leads to the conclusion that LWA and TRV represents a substantial improvement compared to the former crop ground area-based dose expression model. However, total leaf area is the most important parameter for dose adjustment, while sprayer efficiency is also a key factor. Additionally, it is suggested that deposition on leaves (mean values and variability) should be reported in pesticide efficacy evaluations in order to establish the required doses independently from the dose expression mode. The DOSA3D system, based on leaf area index estimation, was found to be the most conservative DSS regarding the spraying volume ratio to TRV because low spraying efficiencies are considered. Instead, AGMET was found to be the most effective for dose adjustment. However, despite the differences between the recommendations, all the analysed DSSs are useful tools for rational decision making about spraying volume rate and pesticide doses at national level. Their use should be promoted by the competent authorities.

Discriminating crop, weeds and soil surface with a terrestrial LIDAR sensor.

In this study, the evaluation of the accuracy and performance of a light detection and ranging (LIDAR) sensor for vegetation using distance and reflection measurements aiming to detect and discriminate maize plants and weeds from soil surface was done. The study continues a previous work carried out in a maize field in Spain with a LIDAR sensor using exclusively one index, the height profile. The current system uses a combination of the two mentioned indexes. The experiment was carried out in a maize field at growth stage 12-14, at 16 different locations selected to represent the widest possible density of three weeds: Echinochloa crus-galli (L.) P.Beauv., Lamium purpureum L., Galium aparine L.and Veronica persica Poir.. A terrestrial LIDAR sensor was mounted on a tripod pointing to the inter-row area, with its horizontal axis and the field of view pointing vertically downwards to the ground, scanning a vertical plane with the potential presence of vegetation. Immediately after the LIDAR data acquisition (distances and reflection measurements), actual heights of plants were estimated using an appropriate methodology. For that purpose, digital images were taken of each sampled area. Data showed a high correlation between LIDAR measured height and actual plant heights (R2 = 0.75). Binary logistic regression between weed presence/absence and the sensor readings (LIDAR height and reflection values) was used to validate the accuracy of the sensor. This permitted the discrimination of vegetation from the ground with an accuracy of up to 95%. In addition, a Canonical Discrimination Analysis (CDA) was able to discriminate mostly between soil and vegetation and, to a far lesser extent, between crop and weeds. The studied methodology arises as a good system for weed detection, which in combination with other principles, such as vision-based technologies, could improve the efficiency and accuracy of herbicide spraying.