New strategies for row-crop management based on cost-effective remote sensors

Agricultural technology can be an excellent antidote to resource scarcity. Its growth has led to the extensive study of spatial and temporal in-field variability. The challenge of accurate management has been addressed in recent years through the use of accurate high-cost measurement instruments by researchers. However, low rates of technological adoption by farmers motivate the development of alternative technologies based on affordable sensors, in order to improve the sustainability of agricultural biosystems. This doctoral thesis has as main objective the development and evaluation of systems based on affordable sensors, in order to address two of the main aspects affecting the producers: the need of an accurate plant water status characterization to perform a proper irrigation management and the precise weed control. To address the first objective, two data acquisition methodologies based on aerial platforms have been developed, seeking to compare the use of infrared thermometry and thermal imaging to determine the water status of two most relevant row-crops in the region, sugar beet and super high-density olive orchards. From the data obtained, the use of an airborne low-cost infrared sensor to determine the canopy temperature has been validated. Also the reliability of sugar beet canopy temperature as an indicator its of water status has been confirmed. The empirical development of the Crop Water Stress Index (CWSI) has also been carried out from aerial thermal imaging combined with infrared temperature sensors and ground measurements of factors such as water potential or stomatal conductance, validating its usefulness as an indicator of water status in super high-density olive orchards. To contribute to the development of precise weed control systems, a system for detecting tomato plants and measuring the space between them has been developed, aiming to perform intra-row treatments in a localized and precise way. To this end, low cost optical sensors have been used and compared with a commercial LiDAR laser scanner. Correct detection results close to 95% show that the implementation of these sensors can lead to promising advances in the automation of weed control. The micro-level field data collected from the evaluated affordable sensors can help farmers to target operations precisely before plant stress sets in or weeds infestation occurs, paving the path to increase the adoption of Precision Agriculture techniques

A Mixed Data-Based Deep Neural Network to Estimate Leaf Area Index in Wheat Breeding Trials

Remote and non-destructive estimation of leaf area index (LAI) has been a challenge in the last few decades as the direct and indirect methods available are laborious and time-consuming. The recent emergence of high-throughput plant phenotyping platforms has increased the need to develop new phenotyping tools for better decision-making by breeders. In this paper, a novel model based on artificial intelligence algorithms and nadir-view red green blue (RGB) images taken from a terrestrial high throughput phenotyping platform is presented. The model mixes numerical data collected in a wheat breeding field and visual features extracted from the images to make rapid and accurate LAI estimations. Model-based LAI estimations were validated against LAI measurements determined non-destructively using an allometric relationship obtained in this study. The model performance was also compared with LAI estimates obtained by other classical indirect methods based on bottom-up hemispherical images and gaps fraction theory. Model-based LAI estimations were highly correlated with ground-truth LAI. The model performance was slightly better than that of the hemispherical image-based method, which tended to underestimate LAI. These results show the great potential of the developed model for near real-time LAI estimation, which can be further improved in the future by increasing the dataset used to train the model

Developing New Tools to Determine Plant Spacing for Precise Agrochemical Application

CIGR - AgEng 2016 Aarhus, Denmark 26 - 29 JuneAdvances in the usage of computer imaging, communication technologies and the successful development of new techniques for precision agriculture have facilitated a smart-digital revolution in row crop agriculture in recent years. The use of a yield monitor, variable rate application (VRA) for fertilizer and herbicides, soil property maps and Global Navigation Satellite System (GNSS) technology has enabled the development of computer generated prescription maps for farm management. All these technologies are changing agricultural practices from simple mechanical operations to automated operations implemented by robotic-based systems. The automation of individual crop plant care in vegetable crop fields has increased its practical feasibility and improved efficiency and economic benefit. A systems-based approach is a key feature in the mechanization engineering design via the incorporation of precision sensing techniques. The objective of this study was to design sensing capabilities for implementation to measure plant spacing under different test conditions (California, USA and Andalucía, Spain). Three different optical sensors were used: an optical light curtain transmitter and receiver (880nm), a LiDAR sensor (905 nm), and an RGB camera. An active photoelectric transmission sensor, which contained 3 pairs of optical light curtain transmitters and receivers, evaluated the interruption by the tomato stem of the light curtain between the two devices, and was recorded simultaneously in real-time by a high-speed embedded control system. The LiDAR (model LMS 211 in California and LMS 111 in Spain, from SICK AG) was installed in a vertical orientation in the middle of the mobile platform. Additionally, a RGB spatial mosaicked image was used to adjust the data from the light beam and LiDAR sensor and obtain combined information (RGBD where D is for distance). These sensors were used to properly detect, localize, and discriminate between weed and tomato plants. The use of this detection system may result in a new technique that allows for the automatic control of aggressive weeds and the automation of weeding tools.Ministerio de Economía y Competitividad AGL2013-46343-RJunta de Andalucía P12-AGR-122

Development and preliminary results of a mobile application to perform pre-inspection of sprayer equipment

Por todos es conocido que el presente y futuro de las aplicaciones de productos agroquímicos pasa por una buena regulación, calibración y mantenimiento de los equipos que se utilizan. Tanto Europa como los países miembros y sus comunidades autónomas, en el caso de España, a través de sus instituciones (Universidades, Consejerías, etc.), están trabajando concienzudamente en el protocolo de inspección. Es de prever que en muchos equipos la primera inspección será desfavorable, y además supondrá un coste económico alto para el propietario, independientemente de que el equipo pase la inspección de forma favorable o no. Este hecho hace que el agricultor no conciba este control con la “percepción beneficiosa” que pueda tener la administración. Por todo ello, el Laboratorio de Agricultura de Precisión de la Universidad de Sevilla ha decidido centrarse en el término “pre-inspección” y desarrollar una aplicación gratuita de autocontrol para dispositivos móviles que permita al propietario o su técnico agrícola conocer el estado de su equipo de aplicación antes de acudir a una inspección “oficial” y de esta forma solventar los posibles problemas detectados. En los primeros equipos que han formado parte de este estudio se ha conseguido, por una parte poner en conocimiento y mostrar los elementos y aspectos a controlar y por otra, generar de forma gratuita y fácil un informe donde se indica los elementos y aspectos a solventar antes de ir a una inspección. Esta aplicación de pre-inspección ha sido una herramienta muy valorada por los usuarios como mantenimiento preventivo.It is widely acknowledged that the present and future practice of pesticide applications requires good regulation, calibration and maintenance of the equipment used. European countries and their regional governments, in the case of Spain, through its institutions (Universities, Councils, etc.), are working conscientiously towards inspection protocol. It is expected that many sprayers will not pass the first inspection, and there will be a significant cost to the owner, whether the equipment passes the inspection or not. This fact makes this inspection protocol is not conceived with the “beneficial perception” as the administration may have. Therefore, the Precision Agriculture Laboratory at University of Sevilla has decided to focus on the term “pre-inspection” and develop a free mobile application for self-assessment that allows the owner or agricultural technician check the status of the sprayer before inspection. This would address the possible problem idenfified. For the first spayers considered in this study there have been several achievements: firstly informing about and showing those elements and aspects of the sprayer which need to be controlled and secondly, generating simply and without cost a report describing those elements and aspects which must be resolved before going for an inspection. This approach to pre-inspection has proved to be highly very valued by users as a tool for preventive maintenance

Optical Sensing to Determine Tomato Plant Spacing for Precise Agrochemical Application: Two Scenarios

The feasibility of automated individual crop plant care in vegetable crop fields has increased, resulting in improved efficiency and economic benefits. A systems-based approach is a key feature in the engineering design of mechanization that incorporates precision sensing techniques. The objective of this study was to design new sensing capabilities to measure crop plant spacing under different test conditions (California, USA and Andalucía, Spain). For this study, three different types of optical sensors were used: an optical light-beam sensor (880 nm), a Light Detection and Ranging (LiDAR) sensor (905 nm), and an RGB camera. Field trials were conducted on newly transplanted tomato plants, using an encoder as a local reference system. Test results achieved a 98% accuracy in detection using light-beam sensors while a 96% accuracy on plant detections was achieved in the best of replications using LiDAR. These results can contribute to the decision-making regarding the use of these sensors by machinery manufacturers. This could lead to an advance in the physical or chemical weed control on row crops, allowing significant reductions or even elimination of hand-weeding tasks

Estimación de producción en cítricos usando técnicas de aprendizaje automático

Estimar con exactitud la cosecha de un cultivo representa una información muy relevante para agricultores y cooperativas/agentes encargados de gestionar y vender el producto. De esta estimación depende la organización y logística necesarias para la recolección, planificación del almacenaje, stock y abastecimiento de los mercados. Actualmente la estimación de la cosecha se realiza en campo con personal experimentado realizando una inspección visual y en base a datos históricos, proceso que tiene riesgo de presentar errores humanos. Las redes neuronales convolucionales (CNN) basadas en el Deep Learning (DL), constituyen una herramienta prometedora para hacer estimaciones de rendimiento basadas en el reconocimiento y conteo de frutos. El objetivo del este trabajo ha sido crear un modelo basado en CNNs a partir de una arquitectura de red existente y entrenada para contar el número de frutos y estimar la producción de la parcela. Una vez entrenados los modelos, se testaron sobre imágenes tomadas con un dron multirrotor sobre 20 árboles seleccionados al azar de una parcela de cítricos Citrus sinensis (L.) cv. Navelina. Durante las tres campañas anuales en las que se han realizado las estimaciones, el error medio absoluto obtenido con DL fue entre 4-6% y el del técnico especialista en el aforo entorno al 8-11% frente a la producción real de la parcela. Estos resultados vislumbran un gran potencial de la metodología para la predicción del rendimiento de árboles de cítricos

Estimación de parámetros biofísicos de interés para la mejora de trigo usando inteligencia artificial

Los mejoradores vegetales demandan gran cantidad de información de los cultivos que les ayude a tomar decisiones a la hora de seleccionar variedades. El fenotipado de alto rendimiento tanto en plataformas terrestres como áreas, que permite la evaluación precisa con sensores remotos de las respuestas a estreses abióticos y bióticos de los cultivos, han emergido como una herramienta prometedora. Entre la gran cantidad de parámetros fisiológicos y estructurales que se pueden monitorear o estimar a nivel de planta, el Índice de Área Foliar (IAF) es un parámetro biofísico del cultivo que tiene gran importancia agronómica y es un indicador de la capacidad fotosintética del cultivo, estando estrechamente relacionado con la producción final. Su valor, se puede obtener a partir de dividir el área de las hojas de un cultivo expresado en m2 y el área de suelo sobre el cual se encuentra establecido. Para el cálculo del IAF, tradicionalmente se han utilizado métodos directos (destructivos) e indirectos (no destructivos). Los métodos directos, además de ser destructivos requieren mucho tiempo y son costosos. Es por lo que el IAF se suele estimar más frecuentemente mediante métodos indirectos (fotografías hemisféricas). Sin embargo, a pesar de que los métodos indirectos son bastante precisos, estos necesitan un post-procesamiento que consume mucho tiempo. El desarrollo de redes neuronales basadas en aprendizaje profundo o Deep Learning (DL), un subconjunto del aprendizaje automático o Machine Learning (ML), y concretamente las redes convolucionales (CNN o ConvNet) se presentan como alternativa para hacer estimaciones de IAF. Este tipo de redes permiten transferir lo aprendido por la red en un escenario en el que dispone de datasets o conjuntos datos de gran tamaño a otros escenarios con menores datos con el propósito de resolver un problema concreto, técnica conocida como transfer learning. El objetivo del presente trabajo ha sido crear un modelo basado en CNNs a partir de una arquitectura de red existente y entrenada para estimar el IAF a partir de imágenes RGB. Para crear los datasets necesarios para entrenar el modelo, se tomaron fotografías desde un trípode a una altura de 1 m sobre un ensayo de trigo de 10 variedades con tres repeticiones durante 9 fechas distintas. Al mismo tiempo, se realizaron medidas no destructivas, utilizando una relación alométrica, para calcular el IAF de cada unidad experimental. Una vez entrenado el modelo se testeó sobre uno de los días de ensayo. Los resultados preliminares muestran que el uso de DL es una herramienta prometedora para el cálculo del IAF

Low-Cost Three-Dimensional Modeling of Crop Plants

Plant modeling can provide a more detailed overview regarding the basis of plant development throughout the life cycle. Three-dimensional processing algorithms are rapidly expanding in plant phenotyping programmes and in decision-making for agronomic management. Several methods have already been tested, but for practical implementations the trade-off between equipment cost, computational resources needed and the fidelity and accuracy in the reconstruction of the end-details needs to be assessed and quantified. This study examined the suitability of two low-cost systems for plant reconstruction. A low-cost Structure from Motion (SfM) technique was used to create 3D models for plant crop reconstruction. In the second method, an acquisition and reconstruction algorithm using an RGB-Depth Kinect v2 sensor was tested following a similar image acquisition procedure. The information was processed to create a dense point cloud, which allowed the creation of a 3D-polygon mesh representing every scanned plant. The selected crop plants corresponded to three different crops (maize, sugar beet and sunflower) that have structural and biological differences. The parameters measured from the model were validated with ground truth data of plant height, leaf area index and plant dry biomass using regression methods. The results showed strong consistency with good correlations between the calculated values in the models and the ground truth information. Although, the values obtained were always accurately estimated, differences between the methods and among the crops were found. The SfM method showed a slightly better result with regard to the reconstruction the end-details and the accuracy of the height estimation. Although the use of the processing algorithm is relatively fast, the use of RGB-D information is faster during the creation of the 3D models. Thus, both methods demonstrated robust results and provided great potential for use in both for indoor and outdoor scenarios. Consequently, these low-cost systems for 3D modeling are suitable for several situations where there is a need for model generation and also provide a favourable time-cost relationship

Diseño y primeros resultados de una plataforma móvil eléctrica de registro de datos para agricultura de precisión

La monitorización cercana requiere de equipamiento montado a bordo de vehículos agrícolas, implementos o cualquier plataforma que permitan obtener rasgos agronómicamente relevantes. Las plataformas móviles para el fenotipado de características biofísicas de los cultivos permiten obtener una alta repetibilidad de las mediciones, al tiempo de no ser invasivas sobre las labores normales de cultivo. De la misma forma que el diseño de estas plataformas está condicionado por la variedad de sensores que deben alojar, deberá ser adecuado al tipo de cultivo del que se pretende obtener información precisa, y que su uso no requiera de una intensiva participación humana. Los objetivos se centraron en (i) desarrollar una plataforma móvil eléctrica, sencilla y modular, diseñada para alojar una gran variedad de sensores que permitan la caracterización fenotípica de cultivos, y (ii) probar su rendimiento en campo, la reproducibilidad de los resultados y su capacidad para obtener mediciones sobre el volumen de copa de los naranjos de una parcela comercial. Para este segundo objetivo, y tras unas primeras comprobaciones en laboratorio sobre aspectos como la velocidad, la carga óptima, y la estabilidad, se llevaron a cabo pruebas de campo empleando un sensor LiDAR escaneando de forma lateral y una cámara de profundidad Kinect de Microsoft. En este trabajo se presentan los primeros resultados obtenidos del desarrollo técnico de la plataforma para la caracterización electrónica del volumen de copa de cítricos, con los que se pretende en el futuro generar información de alto valor para el agricultor que le permitan ajustar aspectos como las aplicaciones de agroquímicos o labores como la poda