No abstract

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.

The objective of the present study was to compare two chemometric approaches for characterizing the rheological properties of fruits from puncture test force/displacement curves. The first approach (parameter approach) computed six texture parameters from the curves, which were supposed to be representative of skin hardness, fruit deformation before skin rupture, flesh firmness and mechanical work needed to penetrate the fruit. The second approach (whole curve approach) used the whole digitized curve (300 data points) in further data processing. Two experimental studies were compared: first, the variability of the rheological parameters of five apple cultivars; second, the rheological variability that was characterized as a function of storage conditions. For both approaches, factorial discriminant analysis was applied to discriminate the fruits based on the measured rheological properties. The qualitative groups in factorial discriminant analysis were either the apple cultivar or the storage conditions (days and temperatures of storage). The tests were carried out using cross-validation procedures, making it possible to compute the number of fruits correctly identified. Thus the percentage of correct identification was 92% and 87% for using the parameter and the whole curve approaches, respectively. The discrimination of storage duration was less accurate for both approaches giving about 50% correct identifications. Comparison of the percentage of correct classifications based on the whole curve 3 Corresponding author.Journal of Texture Studies 36 (2005) 387-401. All Rights Reserved. © Copyright 2005, Blackwell Publishing 387 and the parameter approaches showed that the six computed parameters gave a good summary of the information present in the curve. The whole curve approach showed that some additional information, not present in the six parameters, may be appropriate for a complete description of the fruit rheology. KEYWORDSApple, digitized curve, factorial discriminant analysis, penetrometry, puncture test, storage, texture 388 C. CAMPS ET AL. COMPARISON OF TWO CHEMOMETRIC APPROACHES

Herbivorous arthropods, such as spider mites, are one of the major causes of annual crop losses. They are usually hard to spot before a severe infestation takes place. When feeding, these insects cause external perturbation that triggers changes in the underlying physiological process of a plant, which are expressed by a generation of distinct variations of electrical potential. Therefore, plant electrophysiology data portray information of the plant state. Analyses involving machine learning techniques applied to plant electrical response triggered by spider mite infestation have not been previously reported. This study investigates plant electrophysiological signals recorded from 12 commercial tomatoes plants contaminated with spider mites and proposes a workflow based on Gradient Boosted Tree algorithm for an automated differentiation of the plant’s normal state from the stressed state caused by infestation. The classification model built using the signal samples recorded during daylight and employing a reduced feature subset performs with an accuracy of 80% in identifying the plant’s stressed state. Furthermore, the Hjorth complexity encloses the most relevant information for discrimination of the plant status. The obtained findings open novel access towards automated detection of insect infestation in greenhouse crops and, consequently, more optimal prevention and treatment approaches.

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.