Yiannis Ampatzidis

A yield mapping system for hand-harvested fruits based on RFID and GPS location technologies: field testing

It is proposed that radio frequency identification (RFID) technology be used to overcome the limitations of existing yield mapping systems for manual fresh fruit harvesting. Two methods are proposed for matching bins—containing harvested fruits—with corresponding pairs of trees. In the first method, a long-range RFID reader and a DGPS are mounted on an orchard tractor and passive low-cost RFID tags are attached to the bins. In the second method, the DGPS is not used and RFID tags are attached to individual trees as well as bins. An experimental evaluation of the accuracy and reliability of both methods was performed in an orchard. The first method failed in half of the trials because the tree canopies interfered with the GPS signal. The RFID reader miss ratio for the detection of the bins was 0.32% for both methods. However, the attachment of RFID tags on suitable tree branches (to achieve 100% detection), in the second method, is not a well-defined procedure; some trial is demanded to determine the best positions and orientations of the tree tags in order for the RFID reader to successfully detect them. The first method seems more promising if robust tractor location under foliage can be achieved.

An integrated cloud-based platform for labor monitoring and data analysis in precision agriculture

Harvest labor has became a prevailing cost in cherry and other Specialty Crops industry. We developed an integrated solution that provided real-time labor monitoring, payroll accrual, and labor-data-based analysis. At the core of our solution is a cloud-based information system that collects labor data from purposely designed labor monitoring devices, and visualizes real-time labor productivity data through a mobile-friendly user interface. Our solution used a proprietary process [1] to accurately associate labor data with its related worker and position under a many-to-many employment relation. We also describe our communication API and protocol, which are specifically designed to improve the reliability of data communication within an orchard. Besides its immediate benefits in improving the efficiency and accuracy of labor monitoring, our solution also enables data analysis and visualization based on harvest labor data. As an example, we discuss our approach of yield mapping based on harvest labor data. We implemented the platform and deployed the system on a cloud-based computing platform for better scalability. An early version of the system has been tested during the 2012 harvest season in cherry orchards in the U.S. Pacific Northwest Region.

X-FIDO: An Effective Application for Detecting Olive Quick Decline Syndrome with Deep Learning and Data Fusion.

We have developed a vision-based program to detect symptoms of Olive Quick Decline Syndrome (OQDS) on leaves of Olea europaea L. infected by Xylella fastidiosa, named X-FIDO (Xylella FastIdiosa Detector for O. europaea L.). Previous work predicted disease from leaf images with deep learning but required a vast amount of data which was obtained via crowd sourcing such as the PlantVillage project. This approach has limited applicability when samples need to be tested with traditional methods (i.e., PCR) to avoid incorrect training input or for quarantine pests which manipulation is restricted. In this paper, we demonstrate that transfer learning can be leveraged when it is not possible to collect thousands of new leaf images. Transfer learning is the re-application of an already trained deep learner to a new problem. We present a novel algorithm for fusing data at different levels of abstraction to improve performance of the system. The algorithm discovers low-level features from raw data to automatically detect veins and colors that lead to symptomatic leaves. The experiment included images of 100 healthy leaves, 99 X. fastidiosa-positive leaves and 100 X. fastidiosa-negative leaves with symptoms related to other stress factors (i.e., abiotic factors such as water stress or others diseases). The program detects OQDS with a true positive rate of 98.60 ± 1.47% in testing, showing great potential for image analysis for this disease. Results were obtained with a convolutional neural network trained with the stochastic gradient descent method, and ten trials with a 75/25 split of training and testing data. This work shows potential for massive screening of plants with reduced diagnosis time and cost.

Particle Swarm Optimization for Solving a Class of Type-1 and Type-2 Fuzzy Nonlinear Equations

Abstract 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.

Preliminary Testing of a System for Evaluating Picker Efficiency in Tree Fruit

Herein we present a real-time monitoring system that can track and record individual picker efficiency during harvest of tree crops. It integrates a digital weighing scale, RFID reader/writer, RFID tags, computational unit, and a wearable datalogger housed in a protective enclosure attached to the picker’s belt. As harvested fruit is dumped into a standard collection bin situated on the scale, the system reads simultaneously the picker’s ID (RFID tag) and records the incremental weight of fruit. Weight data are transmitted wirelessly to the picker’s datalogger which displays, via LCD, the total weight of harvested fruit. The performance of the prototype system was evaluated during harvest of sweet cherries (Prunus avium L.) and apples (Malus domestica Borkh.). The mean harvest rate for manual harvest of ‘Cowiche’ sweet cherry trees trained to a planar, compact architecture was 0.80 kg/person/min. In addition, preliminary tests showed that harvesting with a mechanical-assist harvest system improved harvest rate in a ‘Skeena’ sweet cherry orchard trained to a Y-trellised system to 1.25 kg/person/min. In comparison, harvest rate of ‘Fuji’ apple trees trained to a moderate density central leader architecture and ‘Braeburn’/’M9’ apple trees trained to a high density tall spindle system was 3.58 kg/person/min and 5.61 kg/person/min, respectively.

Specific Fluorescence in Situ Hybridization (FISH) Test to Highlight Colonization of Xylem Vessels by Xylella fastidiosa in Naturally Infected Olive Trees (Olea europaea L.)

The colonization behavior of the Xylella fastidiosa strain CoDiRO, the causal agent of olive quick decline syndrome (OQDS), within the xylem of Olea europaea L. is still quite controversial. As previous literature suggests, even if xylem vessel occlusions in naturally infected olive plants were observed, cell aggregation in the formation of occlusions had a minimal role. This observation left some open questions about the whole behavior of the CoDiRO strain and its actual role in OQDS pathogenesis. In order to evaluate the extent of bacterial infection in olive trees and the role of bacterial aggregates in vessel occlusions, we tested a specific fluorescence in situ hybridization (FISH) probe (KO 210) for X. fastidiosa and quantified the level of infection and vessel occlusion in both petioles and branches of naturally infected and non-infected olive trees. All symptomatic petioles showed colonization by X. fastidiosa, especially in the larger innermost vessels. In several cases, the vessels appeared completely occluded by a biofilm containing bacterial cells and extracellular matrix and the frequent colonization of adjacent vessels suggested a horizontal movement of the bacteria. Infected symptomatic trees had 21.6 ± 10.7% of petiole vessels colonized by the pathogen, indicating an irregular distribution in olive tree xylem. Thus, our observations point out the primary role of the pathogen in olive vessel occlusions. Furthermore, our findings indicate that the KO 210 FISH probe is suitable for the specific detection of X. fastidiosa.

Evaluating the performance of spectral features and multivariate analysis tools to detect laurel wilt disease and nutritional deficiency in avocado

Abstract Laurel wilt (Lw) disease is an exotic and lethal disease that can kill laurel family trees very fast. It is vectored by the redbay ambrosia beetle that prefers to live and lay eggs inside avocado trees (among other plants). Lw disease continues to expand in Florida posing a major threat to the avocado industry. Early and accurate disease detection is very critical in this case to remove infected trees and distinguish Lw disease from other diseases or disorders with similar symptoms. Herein, we present a nondestructive remote sensing method to detect Lw-infected avocado trees (in early and late stage) and discriminate them from healthy and other factors that cause similar symptoms, such as iron and nitrogen deficiencies, by using a portable spectral data collection system (visible – near infrared; 400–970 nm). Two data sets were collected in 10 nm and 40 nm spectral resolution, and 23 vegetation indices (VIs) were calculated to detect Lw-affected trees by using two classification methods: decision tree (DT) and multilayer perceptron (MLP) neural networks. Additionally, the optimal wavelengths and VIs to discriminate healthy, Lw-infected and avocado trees with iron and nitrogen deficiencies were identified. The results showed that it was possible to detect Lw-infected trees at early stage and distinguish them from other biotic and abiotic factors with high accuracy (around 100%) using the MLP method. Poorer results were achieved with DTs. The optimum 10 nm wide bands and VIs selected for the Lw-detection were found in the red, red-edge and NIR bands.

Friction Loss and Heat Transfer of Fibre and Wood Pulp Suspensions: A Review

Abstract. Manufacturing advancements have led to a focus on the use of natural composites prepared from wood pulp fibres. Wood pulp fibre suspension has shown an interesting which could be used for commercial products because of its many advantages, such as low density and relatively high mechanical properties. However it is different from other solid particles due to its different length, flexibility and concentration properties. The thermos-physical properties of the fiber suspension have various applications in industries such as papermaking, textiles and composite production. This review -summarizes the effects of changing parameters on friction loss and heat transfer in wood pulp fibre suspensions- This information can play a significant role in the future development of papermaking.

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.