Relationship between Hexokinase and the Aquaporin PIP1 in the Regulation of Photosynthesis and Plant Growth

Increased expression of the aquaporin NtAQP1, which is known to function as a plasmalemma channel for CO2 and water, increases the rate of both photosynthesis and transpiration. In contrast, increased expression of Arabidopsis hexokinase1 (AtHXK1), a dual-function enzyme that mediates sugar sensing, decreases the expression of photosynthetic genes and the rate of transpiration and inhibits growth. Here, we show that AtHXK1 also decreases root and stem hydraulic conductivity and leaf mesophyll CO2 conductance (gm). Due to their opposite effects on plant development and physiology, we examined the relationship between NtAQP1 and AtHXK1 at the whole-plant level using transgenic tomato plants expressing both genes simultaneously. NtAQP1 significantly improved growth and increased the transpiration rates of AtHXK1-expressing plants. Reciprocal grafting experiments indicated that this complementation occurs when both genes are expressed simultaneously in the shoot. Yet, NtAQP1 had only a marginal effect on the hydraulic conductivity of the double-transgenic plants, suggesting that the complementary effect of NtAQP1 is unrelated to shoot water transport. Rather, NtAQP1 significantly increased leaf mesophyll CO2 conductance and enhanced the rate of photosynthesis, suggesting that NtAQP1 facilitated the growth of the double-transgenic plants by enhancing mesophyll conductance of CO2

Early Detection of Grapevine (Vitis vinifera) Downy Mildew (Peronospora) and Diurnal Variations Using Thermal Imaging

Agricultural industry is facing a serious threat from plant diseases that cause production and economic losses. Early information on disease development can improve disease control using suitable management strategies. This study sought to detect downy mildew (Peronospora) on grapevine (Vitis vinifera) leaves at early stages of development using thermal imaging technology and to determine the best time during the day for image acquisition. In controlled experiments, 1587 thermal images of grapevines grown in a greenhouse were acquired around midday, before inoculation, 1, 2, 4, 5, 6, and 7 days after an inoculation. In addition, images of healthy and infected leaves were acquired at seven different times during the day between 7:00 a.m. and 4:30 p.m. Leaves were segmented using the active contour algorithm. Twelve features were derived from the leaf mask and from meteorological measurements. Stepwise logistic regression revealed five significant features used in five classification models. Performance was evaluated using K-folds cross-validation. The support vector machine model produced the best classification accuracy of 81.6%, F1 score of 77.5% and area under the curve (AUC) of 0.874. Acquiring images in the morning between 10:40 a.m. and 11:30 a.m. resulted in 80.7% accuracy, 80.5% F1 score, and 0.895 AUC

Nirs detection of moldy core in apples

Proceedings of the International Conference “Environmentally friendly and safe technologies for quality of fruit and vegetables”, held in Universidade do Algarve, Faro, Portugal, on January 14-16, 2009. This Conference was a join activity with COST Action 924.Moldy core of apples is undetectable until the fruit is cut or bitten into, it can therefore pose serious problems to both producer and consumer. Removal of diseased fruits prior to storage would be most desirable. The objective of this study was to evaluate the ability of VIS-NIR mini-spectrometers to detect moldy core in apples, on line. An apparatus which is qualified for online NIRS (near infrared spectrometry) measurements was developed based on off-the-shelf mini-spectrometers. ‘Top Red’ apples, were collected from several orchards before and during the commercial harvest, and were stored at 0°C pending the tests. The data were analyzed by chemometric procedures, specifically, by partial least squares regression (PLSR), and were classified by means of canonical discriminant analysis. The canonical variables were represented by the latent variables of the PLS models, which were based on the spectra. The accuracy of the classification results was high when the moldy fraction threshold was set at 5%; in such a case the mold covers only the seed carpals of the fruit, where it might remain without really damaging the fruit. Improvements should aim to reduce errors in classifying low-level damage, and also in misclassifying some healthy fruits. The rate of testing (1 s per fruit) is acceptable for quality control purposes, but should be accelerated for future packing-line implementation

Using Time Series of High-Resolution Planet Satellite Images to Monitor Grapevine Stem Water Potential in Commercial Vineyards

Spectral-based vegetation indices (VI) have been shown to be good proxies of grapevine stem water potential (Ψstem), assisting in irrigation decision-making for commercial vineyards. However, VI-Ψstem correlations are mostly reported at the leaf or canopy scales, using proximal canopy-based sensors or very-high-spatial resolution images derived from sensors mounted on small airplanes or drones. Here, for the first time, we take advantage of high-spatial resolution (3-m) near-daily images acquired from Planet’s nano-satellite constellation to derive VI-Ψstem correlations at the vineyard scale. Weekly Ψstem was measured along the growing season of 2017 in six vines each in 81 commercial vineyards and in 60 pairs of grapevines in a 2.4 ha experimental vineyard in Israel. The Clip application programming interface (API), provided by Planet, and the Google Earth Engine platform were used to derive spatially continuous time series of four VIs—GNDVI, NDVI, EVI and SAVI—in the 82 vineyards. Results show that per-week multivariable linear models using variables extracted from VI time series successfully tracked spatial variations in Ψstem across the experimental vineyard (Pearson’s-r = 0.45–0.84; N = 60). A simple linear regression model enabled monitoring seasonal changes in Ψstem along the growing season in the vineyard (r = 0.80–0.82). Planet VIs and seasonal Ψstem data from the 82 vineyards were used to derive a ‘global’ model for in-season monitoring of Ψstem at the vineyard-level (r = 0.78; RMSE = 18.5%; N = 970). The ‘global’ model, which requires only a few VI variables extracted from Planet images, may be used for real-time weekly assessment of Ψstem in Mediterranean vineyards, substantially improving the efficiency of conventional in-field monitoring efforts

An Integrated Decision Support System for Environmentally-Friendly Management of the Ethiopian Fruit Fly in Greenhouse Crops

The Ethiopian fruit fly (EFF), Dacus ciliatus, is a key, invasive pest of melons in the Middle East. We developed and implemented a novel decision support system (DSS) to manage this pest in a greenhouse environment in Southern Israel. Dacus ciliatus is commonly controlled in Israel with repeated calendar-sprayings (every 15 days) of pyrethroid pesticides. The current study compares the performance of a DSS against calendar-spraying management (CSM). DSS was based on EFF population monitoring and infestation. DSS took into consideration concerns and observations of expert managers and farmers. During 2014, EFF damage was concentrated in the spring melon production season. Fall and winter production did not show important damage. Damage during the spring of 2014 started to increase when average EFF/trap/day reached 0.3. This value was suggested as the threshold to implement pesticide spraying in DSS greenhouses. EFF/trap/day trends were derived from monitoring with conventional traps and a novel electronic remote sensing trap, developed by our group. CSM during the spring of 2015 included 3 EFF control sprays, while DSS-managed greenhouses were only sprayed once. At the end of the spring season, damage was slightly higher in DSS greenhouses (1.5%), but not significantly different to that found in CSM greenhouses (0.5%). Results support continuing DSS research and optimization to reduce/remove pesticide use against EFF in melon greenhouses. Interactions with farmers and managers is suggested as essential to increase adoption of DSS in agriculture