César Fernández-Quintanilla

Modelling the population dynamics of Avena sterilis under dry-land cereal cropping systems

(1) A mathematical model for simulating the population dynamics of Avena sterilis ssp. ludoviciana (Dur.) Nyman has been constructed using previously reported data. The model considers the age structure of the population of seedlings as well as the effects of density on plant survivorship and reproduction. (2) The model is used to describe the behaviour of the population in the absence of control practices and to predict the effects of various control strategies. In the absence of control, and under continuous winter cereal cropping, the population grows hyperbolically, reaching equilibrium at a density of 535 plants m-2. Annual application of herbicides with 90%. Fallowing the land for 1 in every 2-3 years gave a practical method of containing the populations of A. sterilis. However, to eradicate this weed it was necessary to combine crop rotation with application of herbicides. (3) The effects of changing the values of the parameters on the output of the model were generally minor. The two processes most sensitive to parameter variation were dispersal and mortality of seeds after reproduction and the fecundity of the first cohort of plants. The contribution of late emerging plants to the overall dynamics of the population was rather small and could be disregarded. (4) The model was validated by comparing simulation results with those from longterm field studies. Model predictions closely matched experimental results from herbicide trials, but gave only a crude description of the population dynamics under various crop rotations.

Analysis of natural images processing for the extraction of agricultural elements

This work presents several developed computer-vision-based methods for the estimation of percentages of weed, crop and soil present in an image showing a region of interest of the crop field. The visual detection of weed, crop and soil is an arduous task due to physical similarities between weeds and crop and to the natural and therefore complex environments (with non-controlled illumination) encountered. The image processing was divided in three different stages at which each different agricultural element is extracted: (1) segmentation of vegetation against non-vegetation (soil), (2) crop row elimination (crop) and (3) weed extraction (weed). For each stage, different and interchangeable methods are proposed, each one using a series of input parameters which value can be changed for further refining the processing. A genetic algorithm was then used to find the best value of parameters and method combination for different sets of images. The whole system was tested on several images from different years and fields, resulting in an average correlation coefficient with real data (bio-mass) of 84%, with up to 96% correlation using the best methods on winter cereal images and of up to 84% on maize images. Moreover, the methods low computational complexity leads to the possibility, as future work, of adapting them to real-time processing.

Accuracy and Feasibility of Optoelectronic Sensors for Weed Mapping in Wide Row Crops

The main objectives of this study were to assess the accuracy of a ground-based weed mapping system that included optoelectronic sensors for weed detection, and to determine the sampling resolution required for accurate weed maps in maize crops. The optoelectronic sensors were located in the inter-row area of maize to distinguish weeds against soil background. The system was evaluated in three maize fields in the early spring. System verification was performed with highly reliable data from digital images obtained in a regular 12 m × 12 m grid throughout the three fields. The comparison in all these sample points showed a good relationship (83% agreement on average) between the data of weed presence/absence obtained from the optoelectronic mapping system and the values derived from image processing software (“ground truth”). Regarding the optimization of sampling resolution, the comparison between the detailed maps (all crop rows with sensors separated 0.75 m) with maps obtained with various simulated distances between sensors (from 1.5 m to 6.0 m) indicated that a 4.5 m distance (equivalent to one in six crop rows) would be acceptable to construct accurate weed maps. This spatial resolution makes the system cheap and robust enough to generate maps of inter-row weeds.

Highlights and preliminary results for autonomous crop protection

Intelligent pest control remains a mayor challenge to agriculture.The autonomous tractor used in this work was able to track each straight line with high degree of accuracy.The new design concept was able to autonomously adjust spray application according tree sizes and orchard structure.The intelligent spray boom responded satisfactorily to variation in the level of weed infestation in the field. New technologies are required for safe, site-specific and efficient control of weeds, pathogens and insects in agricultural crops and in forestry. The development and use of autonomous tractors equipped with innovative sensor systems, data processing techniques and actuation tools can be highly beneficial because this technology allows pest control measures to be applied only if, when, and where they are genuinely needed, thus reducing costs, environmental damage and risks to farmers. RHEA (Robotics and associated High-technologies and Equipment for Agriculture) is an EC-funded research project conducted by a consortium composed of 15 research partners from eight European countries. The focus of the project is the design, development and testing of a new generation of automatic and robotic systems for both chemical and physical pest management. A heterogeneous fleet of small, cooperative ground and aerial robots equipped with advanced sensors, enhanced end effectors and improved decision control algorithms will be used. Initially, we are investigating three major scenarios: (a) chemical weed control in winter wheat, (b) thermal weed control (i.e., flaming) in maize and (c) variable applications of pesticides in olive crops. A preliminary system evaluation demonstrated that the intelligent sprayer boom applied the control agent to over 95% of the target area and that the response time, 10s, of the direct-injection system was anticipated in the sprayer system to ensure the accuracy of herbicide spraying. Field trial results showed that the estimated cost for site-specific flame weeding was approximately 24?ha-1, whereas approximately 52?ha-1 was needed to perform a conventional broadcast treatment. Thus, the use of VRA (Variable Rate Application) flaming reduces the use of liquid petroleum gas (cost savings of 28?ha-1). The results also indicated that the control system, mounted on a prototype, air-blast sprayer design, produced a precise system response to variation in the target features, an approximate accuracy of 0.1m in horizontal resolution and a rapid actuation response of approximately 100ms. Workshop and field experiments provide convincing evidence that autonomous agricultural vehicles equipped with intelligent implements represent an important step forward for optimizing pest control applications in sustainable row crop, orchard and cereal crop production systems.

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.

Strategies for the control of Avena sterilis in winter wheat production systems in central Spain

A bioeconomic model is described and used to investigate the agronomic and economic consequences of using a range of management strategies for the control of winter wild oats (Avena sterilis L.) in cereal cropping systems representative of central Spain. The results of simulations indicated that growing winter wheat continuously with the annual application of herbicides may be the optimum strategy, resulting in acceptable wild oat populations and maximum economic benefits. However, the practice of wheat monoculture was only a valid option as long as herbicides were applied annually: spraying herbicides in alternate years failed to control wild oats adequately and resulted in major economic losses. The rotation of wheat with a fallow year, with no herbicides applied in either of the two years, may be a satisfactory low-cost alternative when wild oat infestation levels are low, but it is not valid when infestation levels are high. The strategy that combines the use of a fallow year with herbicide application in the wheat year resulted in optimum wild oat control and moderate profitability under all conditions. However, the net returns obtained were substantially lower than in the continuous wheat plus herbicide strategy. The sensitivity of the model to variation in various key parameters was tested: wheat yield level and fixed costs were the two parameters that had the largest effect on model output. In general, the effect of changing parameter values was more pronounced in continuous wheat systems than in wheat-fallow rotations

An Approach to the Use of Depth Cameras for Weed Volume Estimation.

The use of depth cameras in precision agriculture is increasing day by day. This type of sensor has been used for the plant structure characterization of several crops. However, the discrimination of small plants, such as weeds, is still a challenge within agricultural fields. Improvements in the new Microsoft Kinect v2 sensor can capture the details of plants. The use of a dual methodology using height selection and RGB (Red, Green, Blue) segmentation can separate crops, weeds, and soil. This paper explores the possibilities of this sensor by using Kinect Fusion algorithms to reconstruct 3D point clouds of weed-infested maize crops under real field conditions. The processed models showed good consistency among the 3D depth images and soil measurements obtained from the actual structural parameters. Maize plants were identified in the samples by height selection of the connected faces and showed a correlation of 0.77 with maize biomass. The lower height of the weeds made RGB recognition necessary to separate them from the soil microrelief of the samples, achieving a good correlation of 0.83 with weed biomass. In addition, weed density showed good correlation with volumetric measurements. The canonical discriminant analysis showed promising results for classification into monocots and dictos. These results suggest that estimating volume using the Kinect methodology can be a highly accurate method for crop status determination and weed detection. It offers several possibilities for the automation of agricultural processes by the construction of a new system integrating these sensors and the development of algorithms to properly process the information provided by them.

Spatial Distribution Patterns of Johnsongrass (Sorghum halepense) in Corn Fields in Spain

This study describes the distribution patterns of Johnsongrass populations present in 38 commercial corn fields located in three major corn growing regions of Spain. A total of 232 ha were visually assessed from the cabin of a combine during harvesting using a three-category ranking (high density, low density, no presence) and recording the georeferenced data in a tablet personal computer. On average, 10.3 and 3.9% of the surveyed area were infested with high and low density of Johnsongrass, respectively. Most of the infested area was concentrated in a few large patches with irregular shape. Small patches (less than 1,000 m2) represented only 27% of the infested area. Management factors could explain much of the spatial distribution of this weed in the studied fields. Tillage direction was the main factor explaining patch shape: the length width−1 ratio of the patches was greater than two in the tillage direction. In sprinkler irrigated fields, higher levels of infestation were generally observed close to the sprinkler lines. Areas close to the edges of the field had a higher risk of infestation than the areas in the middle of the fields: a negative relationship between distance from the edge and weed abundance was established. Because a few patches, located in some predictable parts of the field, such as field edges, represent most of the seriously infested area, site-specific treatments of these areas could reduce herbicide inputs, until more reliable, spatially precise and practical detection, mapping, and spraying systems are developed. Nomenclature: Johnsongrass, Sorghum halepense (L.) Pers. SORHA; corn, Zea mays L.

Fleets of robots for environmentally-safe pest control in agriculture

Abstract Feeding the growing global population requires an annual increase in food production. This requirement suggests an increase in the use of pesticides, which represents an unsustainable chemical load for the environment. To reduce pesticide input and preserve the environment while maintaining the necessary level of food production, the efficiency of relevant processes must be drastically improved. Within this context, this research strived to design, develop, test and assess a new generation of automatic and robotic systems for effective weed and pest control aimed at diminishing the use of agricultural chemical inputs, increasing crop quality and improving the health and safety of production operators. To achieve this overall objective, a fleet of heterogeneous ground and aerial robots was developed and equipped with innovative sensors, enhanced end-effectors and improved decision control algorithms to cover a large variety of agricultural situations. This article describes the scientific and technical objectives, challenges and outcomes achieved in three common crops.

Development and reproduction of Myzus persicae and Aphis fabae (Hom., Aphididae) on selected weed species surrounding sugar beet fields

Abstract:  Myzus persicae (Sulzer) and Aphis fabae (Scop.) are two major aphid species colonizing sugar beet. They have a very wide host range adapting to a large number of plant families and species. A series of cage experiments, conducted under ‘winter’ and ‘summer’ growth chamber conditions, showed that both species have the potential to use winter and summer weeds that are usually present in the vicinity of and within sugar beet fields as secondary hosts. Among all the weeds tested, Veronica hederifolia L. and Solanum nigrum L. were the most suitable for M. persicae. Although Brassica kaber (DC.) Wheeler was the weed species associated to the highest reproduction rate of this aphid, V. hederifolia showed a higher intrinsic rate of population increase due to the shorter prereproductive period of the aphid. Amsynckia intermedia Fish. and Mey and Amaranthus retroflexus L. were the most suitable weed hosts for A. fabae.