Optimal Dynamic Motion Sequence Generation for Multiple Harvesters

Rosana G. Moreira, Editor-in-Chief; Texas A&M UniversityThis is a paper from International Commission of Agricultural Engineering (CIGR, Commission Internationale du Genie Rural) E-Journal Volume 9 (2007): Optimal Dynamic Motion Sequence Generation for Multiple Harvesters. Manuscript ATOE 07 001. Vol. IX. July, 2007

Field Operation Planning for Agricultural Vehicles: A Hierarchical Modeling Framework

Rosana G. Moreira, Editor-in-Chief; Texas A&M UniversityThis is a paper from International Commission of Agricultural Engineering (CIGR, Commission Internationale du Genie Rural) E-Journal Volume 9 (2007): Field Operation Planning for Agricultural Vehicles: A Hierarchical Modeling Framework. Manuscript PM 06 021. Vol. IX. February, 2007

Detection of grapevine yellows symptoms in Vitis vinifera L. with artificial intelligence

Grapevine yellows (GY) are a significant threat to grapes due to the severe symptoms and lack of treatments. Conventional diagnosis of the phytoplasmas associated to GYs relies on symptom identification, due to sensitivity limits of diagnostic tools (e.g. real time PCR) in asymptomatic vines, where the low concentration of the pathogen or its erratic distribution can lead to a high rate of false-negatives. GY's primary symptoms are leaf discoloration and irregular wood ripening, which can be easily confused for symptoms of other diseases making recognition a difficult task. Herein, we present a novel system, utilizing convolutional neural networks, for end-to-end detection of GY in red grape vine (cv. Sangiovese), using color images of leaf clippings. The diagnostic test detailed in this work does not require the user to be an expert at identifying GY. Data augmentation strategies make the system robust to alignment errors during data capture. When applied to the task of recognizing GY from digital images of leaf clippings—amongst many other diseases and a healthy control—the system has a sensitivity of 98.96% and a specificity of 99.40%. Deep learning has 35.97% and 9.88% better predictive value (PPV) when recognizing GY from sight, than a baseline system without deep learning and trained humans respectively. We evaluate six neural network architectures: AlexNet, GoogLeNet, Inception v3, ResNet-50, ResNet-101 and SqueezeNet. We find ResNet-50 to be the best compromise of accuracy and training cost. The trained neural networks, code to reproduce the experiments, and data of leaf clipping images are available on the internet. This work will advance the frontier of GY detection by improving detection speed, enabling a more effective response to the disease

Prediction of temperature dependent wave dispersion and interaction properties in composite structures

Composite structures are widely used for aerospace and automotive applications. These operate within a broad temperature range varying between -100_C to 200_C for launch vehicles and -60_C to +50_C for aircraft and automotive vehicles. Hereby, the sensitivity of the wave propagation and interaction properties of a composite structure to the ambient flight temperature is investigated. A wave finite element (WFE) and finite element (FE) based computational method is presented by which the temperature dependent wave dispersion characteristics and interaction phenomenon in a composite structures can be predicted. Initially, the temperature dependent mechanical properties of the panel in the range of -100_C to 150_C are measured experimentally using the Thermal Mechanical Analysis (TMA). Temperature dependent wave dispersion characteristics of each waveguide of the structural system, which is discretised as a system of a number of waveguides joined by a coupling element, is calculated using the WFE approach. The wave scattering properties, as a function of temperature, is determined by coupling the WFE wave characteristics models of the waveguides with the full FE modelling of the coupling element on which defect is included. Numerical case studies are exhibited for two waveguides coupled through a coupling element

Yield sensing technologies for perennial and annual horticultural crops: a review

Yield maps provide a detailed account of crop production and potential revenue of a farm. This level of details enables a range of possibilities from improving input management, conducting on-farm experimentation, or generating profitability map, thus creating value for farmers. While this technology is widely available for field crops such as maize, soybean and grain, few yield sensing systems exist for horticultural crops such as berries, field vegetable or orchards. Nevertheless, a wide range of techniques and technologies have been investigated as potential means of sensing crop yield for horticultural crops. This paper reviews yield monitoring approaches that can be divided into proximal, either direct or indirect, and remote measurement principles. It reviews remote sensing as a way to estimate and forecast yield prior to harvest. For each approach, basic principles are explained as well as examples of application in horticultural crops and success rate. The different approaches provide whether a deterministic (direct measurement of weight for instance) or an empirical (capacitance measurements correlated to weight for instance) result, which may impact transferability. The discussion also covers the level of precision required for different tasks and the trend and future perspectives. This review demonstrated the need for more commercial solutions to map yield of horticultural crops. It also showed that several approaches have demonstrated high success rate and that combining technologies may be the best way to provide enough accuracy and robustness for future commercial systems

Particle swarm optimization for solving a class of type-1 and type-2 fuzzy nonlinear equations

This paper proposes a modified particle swarm optimization (PSO) algorithm that can be used to solve a variety of fuzzy nonlinear equations, i.e. fuzzy polynomials and exponential equations. Fuzzy nonlinear equations are reduced to a number of interval nonlinear equations using alpha cuts. These equations are then sequentially solved using the proposed methodology. Finally, the membership functions of the fuzzy solutions are constructed using the interval results at each alpha cut. Unlike existing methods, the proposed algorithm does not impose any restriction on the fuzzy variables in the problem. It is designed to work for equations containing both positive and negative fuzzy sets and even for the cases when the support of the fuzzy sets extends across 0, which is a particularly problematic case

Vision-based system for detecting grapevine yellow diseases using artificial intelligence

Grapevine yellows (GY) of grapes, a critical threat to grapevines because of the severe symptoms and the lack of healing treatments, has been detected worldwide. The detection of GY diseases is a very difficult and time consuming task, and relies on symptoms identification, which are very similar with other diseases. Additionally, the analysis of asymptomatic GY-infected grapes could lead to high rates of false-negative due of the low concentration of the pathogen in the host. Herein, we present a supporting vision-based tool for GY disease detection using artificial intelligence (AI) and machine learning (ML). Leaves of bois noir-infected plants (previously tested by qPCR) were collected in July-October, 2017. Grapevine yellow was detected in a data set of 322 images and six diseases. Other than grapevine yellow, the diseases include downy mildew, esca disease, grapevine leafroll, powdery mildew and Stictocephala bisonia. A linear support vector machine (SVM) classified features from a pre-trained convolutional neural network - AlexNet trained on ImageNet. The system obtains a 95.23% accuracy and a Matthew's correlation coefficient of 0.832. For reference, a baseline system with local binary patterns (LBP) and color histogram with a SVM obtains only 26.7% and -0.124, respectively. Our work shows promise for automatic detection of grapevine yellow by computers. Future work will focus on improving the sensitivity of the system and implementation on drones with Nvidia Jetson. This system could reduce the rate of false positive/negative in large-scale vineyard monitoring