-Four potato cultivars (Solanum tuberosum L.) differing in their precocity and contrasted for their drought tolerance were investigated in the field and in the greenhouse (2 cultivars). They were subjected to two water treatments, well-irrigated and droughted. Our objective was to examine which shoot and leaf characters were related to the decrease in tuber yield. Drought reduced tuber yields by 11% in 53%. Drought stress highly reduced the dry mass of leaves in all cases. Tuber number was reduced only in early cultivars whereas in the later cultivars, leaf area index and leaf area duration were more affected than in the early cultivars. The cultivar which maintained its tuber growth rate better under drought during the first three weeks of tuber bulking also maintained its yield better. No clear common reaction of early versus later varieties to drought was found.potato / drought stress / cultivar / agro-physiological parameters Résumé -Effet du stress hydrique et du cultivar sur les paramètres de croissance, le rendement et ses composantes. Quatre variétés de pomme de terre (Solanum tuberosum L.) de précocité variable et réputées différentes au niveau de la tolérance à la sécheresse ont été testées, sous deux régimes hydriques (irrigué et stressé) au champ et sous serre. Notre objectif était d'étudier les relations entre l'évolution de différents paramètres de surface foliaire, et de masse (tiges et des tubercules) au cours du cycle et les diminutions de rendement en tubercules. Le stress hydrique a réduit le poids des tubercules de 11à 53 %. Le stress hydrique a fortement réduit la masse sèche foliaire dans tous les cas. Le nombre de tubercules a été réduit uniquement chez les cultivars précoces, tandis que les cultivars tardifs ont plus été affectés au niveau de l'indice foliaire et de sa durée. Les cultivars qui ont mieux maintenu le taux de croissance de leurs tubercules durant les trois premières semaines de remplissage ont également mieux maintenu leurs rendements. Aucune relation précocité -sensibilité au rendement sous stress n'a été observée. pomme de terre / stress hydrique / cultivar / paramètres agro-physiologiques

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The overall goal of this study is to define an intelligent system for predicting citrus fruit yield before the harvest period. This system uses a machine learning algorithm trained on historical field data combined with spectral information extracted from satellite images. To this end, we used 5 years of historical data for a Moroccan orchard composed of 50 parcels. These data are related to climate, amount of water used for irrigation, fertilization products by dose, phytosanitary treatment dose, parcel size, and root-stock type on each parcel. Additionally, two very popular indices, the normalized difference vegetation index and normalized difference water index were extracted from Sentinel 2 and Landsat satellite images to improve prediction scores. We managed to build a total dataset composed of 250 rows, representing the 50 parcels over a period of 5 years labeled with the yield of each parcel. Several machine learning algorithms were tested with the necessary parameter optimization, while the orthonormal automatic pursuit algorithm gave good prediction scores of 0.2489 (MAE: Mean Absolute Error) and 0.0843 (MSE: Mean Squared Error). Finally, the approach followed in this study shows excellent potential for fruit yield prediction. In fact, the test was performed on a citrus orchard, but the same approach can be used on other tree crops to achieve the same goal.

Remote sensing-based crop mapping has continued to grow in economic importance over the last two decades. Given the ever-increasing rate of population growth and the implications of multiplying global food production, the necessity for timely, accurate, and reliable agricultural data is of the utmost importance. When it comes to ensuring high accuracy in crop maps, spectral similarities between crops represent serious limiting factors. Crops that display similar spectral responses are notorious for being nearly impossible to discriminate using classical multi-spectral imagery analysis. Chief among these crops are soft wheat, durum wheat, oats, and barley. In this paper, we propose a unique multi-input deep learning approach for cereal crop mapping, called “CerealNet”. Two time-series used as input, from the Sentinel-2 bands and NDVI (Normalized Difference Vegetation Index), were fed into separate branches of the LSTM-Conv1D (Long Short-Term Memory Convolutional Neural Networks) model to extract the temporal and spectral features necessary for the pixel-based crop mapping. The approach was evaluated using ground-truth data collected in the Gharb region (northwest of Morocco). We noted a categorical accuracy and an F1-score of 95% and 94%, respectively, with minimal confusion between the four cereal classes. CerealNet proved insensitive to sample size, as the least-represented crop, oats, had the highest F1-score. This model was compared with several state-of-the-art crop mapping classifiers and was found to outperform them. The modularity of CerealNet could possibly allow for injecting additional data such as Synthetic Aperture Radar (SAR) bands, especially when optical imagery is not available.