Electrophysiological assessment of plant status outside a Faraday cage using supervised machine learning.

Living organisms have evolved complex signaling networks to drive appropriate physiological processes in response to changing environmental conditions. Amongst them, electric signals are a universal method to rapidly transmit information. In animals, bioelectrical activity measurements in the heart or the brain provide information about health status. In plants, practical measurements of bioelectrical activity are in their infancy and transposition of technology used in human medicine could therefore, by analogy provide insight about the physiological status of plants. This paper reports on the development and testing of an innovative electrophysiological sensor that can be used in greenhouse production conditions, without a Faraday cage, enabling real-time electric signal measurements. The bioelectrical activity is modified in response to water stress conditions or to nycthemeral rhythm. Furthermore, the automatic classification of plant status using supervised machine learning allows detection of these physiological modifications. This sensor represents an efficient alternative agronomic tool at the service of producers for decision support or for taking preventive measures before initial visual symptoms of plant stress appear.

Optimized Prediction of Reducing Sugars and Dry Matter of Potato Frying by FT-NIR Spectroscopy on Peeled Tubers.

Dry matter content (DMC) and reducing sugars (glucose, fructose) contents of three potato varieties for frying (Innovator, Lady Claire, and Markies) were determined by applying Fourier-transform near-infrared spectrometry (FT-NIR), with paying particular attention to tubers preparation (unpeeled, peeled, and transversally cut tubers) before spectral acquisitions. Potatoes were subjected to normal storage temperature as it is processed in the industry (8 °C) and lower temperature inducing sugar accumulations (5 °C) for 195 and 48 days, respectively. Prediction of DMC has been successfully modeled for all varieties. A common model to the three varieties reached R², root mean square error (RMSEP), and ratio performance to deviation (RPD) values of 0.84, 1.2, and 2.49. Prediction accuracy of reducing sugars was variety dependent. Reducing sugars were accurately predicted for Innovator (R² = 0.84, RMSEP = 0.097, and RPD = 2.86) and Markies (R² = 0.78, RMSEP = 0.033, and RPD = 2.15) and slightly less accurate for Lady Claire (R² = 0.63, RMSEP = 0.036, and RPD = 1.64). The lack of accuracy obtained with the Lady Claire variety is mainly due to the tight variability in sugar content measured over the storage. Finally, the best preparation of the tuber from the point of view of the accuracy of the prediction models was to use the whole peeled potato. Such preparation allowed for the improvement in RPD values by 15% to 38% the RPD values depending on reducing sugars and 35% for DMC.

Singular approach to penetrometry by preprocessing of digitized force-displacement curves and chemometry: A case study of 12 tomato varieties.

This study evaluated a singular approach to the analysis of digitized force-displacement curves from penetrometry performed on tomatoes. Penetrometry is commonly used to evaluate the texture properties of fresh fruits and vegetables. Currently, the parameters are computed from force-displacement curves. The parameters are statistically analyzed to highlight differences, for instance, between fruit varieties or differentially stored fruits. In this study, digitized force-displacement curves were considered "mechanical imprints" (MIs). Twelve varieties of tomato were analyzed, and the assumption that a given variety is characterized by a singular MI was tested. The digitized curves were preprocessed through standardization and smoothing. This preprocessing enabled the classification of more than 94% of fruit according to variety compared with 45% of fruit without preprocessing. To compare this approach with a classical approach of texture analysis, 13 parameters were computed from the force-displacement curves and analyzed. This parameters-based approach enabled the classification of approximately 46% of fruit according to variety. The digitized curve enabled the correct classification of 88% more fruit than the level achieved by the parameters-based approach. Thus, penetrometry analysis presents new opportunities, particularly for breeding programs aimed at improving the texture characteristics of fruits and vegetables. Additionally, this approach could be adapted to other mechanical tests for the characterization of the textural properties of food products. PRACTICAL APPLICATIONS: The force-displacement curve of penetrometry can be considered a "mechanical imprint" to improve fruit classification according to preharvest and postharvest characteristics (i.e., maturity, variety, etc.). This mechanical imprint represents a new and discriminant phenotyping criterion for tomato breeding to improve texture. The use of the digitized curve rather than computed parameters may be more easily implemented in industry.

Biological control of Tetranychus urticae by Phytoseiulus macropilis and Macrolophus pygmaeus in tomato greenhouses.

Biological control against phytophagous arthropods has been widely used under greenhouse conditions. Its success is dependent on a number of factors related to the abiotic conditions and to the interactions between pests and biological control agents. In particular, when multiple predator species are introduced to suppress one pest, competitive interactions might occur, including intraguild predation (IGP). In tomato crops, the spider mite Tetranychus urticae Koch is a very problematic phytophagous mite and its control is not yet satisfactory. In 2012 and 2013, the ability of a potential new predatory mite Phytoseiulus macropilis (Banks) was assessed, alone and in the presence of Macrolophus pygmaeus Rambur. Macrolophus pygmaeus is a polyphagous mirid supposed to predate on P. macropilis. Both years, under greenhouse conditions, the effectiveness of the two predators was compared between the following treatments: T. urticae, T. urticae + P. macropilis, T. urticae + M. pygmaeus, and T. urticae + P. macropilis + M. pygmaeus. The number of arthropods per tomato plant over time indicated that P. macropilis well-controlled the population of T. urticae, whereas M. pygmaeus had a very limited impact. Furthermore, there was no evidence of IGP between the two predators but in the presence of M. pygmaeus, P. macropilis tended to have a more clumped spatial distribution. Further studies should clarify the number and location of inoculation points to optimize the control of T. urticae by P. macropilis.

Prediction of essential oil content of oregano by hand-held and Fourier transform NIR spectroscopy.

BACKGROUND: In the framework of a breeding programme, the analysis of hundreds of oregano samples to determine their essential oil content (EOC) is time-consuming and expensive in terms of labour. Therefore developing a new method that is rapid, accurate and less expensive to use would be an asset to breeders. The aim of the present study was to develop a method based on near-inrared (NIR) spectroscopy to determine the EOC of oregano dried powder. Two spectroscopic approaches were compared, the first using a hand-held NIR device and the second a Fourier transform (FT) NIR spectrometer. RESULTS: Hand-held NIR (1000-1800 nm) measurements and partial least squares regression allowed the determination of EOC with R² and SEP values of 0.58 and 0.81 mL per 100 g dry matter (DM) respectively. Measurements with FT-NIR (1000-2500 nm) allowed the determination of EOC with R² and SEP values of 0.91 and 0.68 mL per 100 g DM respectively. RPD, RER and RPIQ values for the model implemented with FT-NIR data were satisfactory for screening application, while those obtained with hand-held NIR data were below the level required to consider the model as enough accurate for screening application. CONCLUSION: The FT-NIR approach allowed the development of an accurate model for EOC prediction. Although the hand-held NIR approach is promising, it needs additional development before it can be used in practice.