Water stress is one of the most important yield constraints on crop productivity for many crops, and especially for maize, worldwide. In addition, climate change creates new challenges for crop adaptation as water stress appears even in areas where, until recently, there was an adequate water supply. The objective of the present study was to determine the effect of water availability on the morphological and physiological characteristics of maize, and also on the environmental cost under field conditions. The lowest water treatment (ET₅₀) reduced leaf area index, plant height, chlorophyll content, assimilation rate and gas exchange parameters, photosynthetic efficiency, and silage yield. Furthermore, mild water stress (ΕΤ₇₀) affected the characteristics that were studied but maintained a high crop yield. Moreover, the outputs/inputs ratio and energy efficiency showed similar trends, with the highest values under ΕΤ₁₀₀ treatment and the lowest under ΕΤ₅₀ treatment in two consecutive years. Therefore, the results of this study can be used by farmers in the Mediterranean area, who can maintain or improve their crop yield using a lower amount of water when the water supply is limited, thereby contributing to reducing the impact of global climate change and maintaining crop productivity.

The verticillium fungus has become a widespread threat to olive fields around the world in recent years. The accurate and early detection of the disease at scale could support solving the problem. In this paper, we use the YOLO version 5 model to detect verticillium fungus in olive trees using aerial RGB imagery captured by unmanned aerial vehicles. The aim of our paper is to compare different architectures of the model and evaluate their performance on this task. The architectures are evaluated at two different input sizes each through the most widely used metrics for object detection and classification tasks (precision, recall, mAP@0.5 and mAP@0.5:0.95). Our results show that the YOLOv5 algorithm is able to deliver good results in detecting olive trees and predicting their status, with the different architectures having different strengths and weaknesses.

Maize (Zea mays L.) is one of the most important crops in the world and fertilization is the most important management practice which contributes to high yield. The objective of this study was to determine the effect of different fertilizers on maize crop and their contribution to the carbon footprint. The experiments were conducted in a commercial field in the area of Thessaloniki during the growing seasons of 2019 and 2020. During the experiment a number of physiological and morphological characteristics, and the energy output/input ratio, energy efficiency, and carbon footprint were determined. The results of the experiment showed that the inorganic fertilizers and manure improved the morphological and physiological characteristics that were studied compared to the green manure treatment and the control. In addition, it appeared most of the energy input of maize cultivation is from fertilizers (52%), followed by diesel (25%) and the use of machinery (14%). The treatments with the slow release fertilizers and the manure gave satisfactory results with an average of 42.1 Mg ha⁻¹ in 2019 and 43.6 Mg ha⁻¹ in 2020 for both fertilization treatments. Therefore, it is necessary to use the appropriate fertilizers in order to maintain the productivity of the crop and reduce the environmental costs.

Non-destructive tools for the in situ evaluation of vine fruit quality and vineyard management can improve the market value of table grape. We proposed a new approach based on a portable fluorescence sensor to map the ripening level of Crimson Seedless table grape in five different plots in the East, Central-North and South of the Macedonia Region of Greece. The sensor provided indices of ripening and color such as SFR_R and ANTH_RG correlated to the chlorophyll and anthocyanin berry contents, respectively. The mean ANTH_RG index was significantly different among all the plots examined due to the occurrence of different environmental conditions and/or asynchronous ripening processes. The indices presented moderate, poor in some cases, spatial variability, probably due to a significant vine-to-vine, intra-vine and intra-bunch variability. The cluster analysis was applied to the plot with the most evident spatial structure (at Kilkis). Krigged maps of the SFR_R, ANTH_RG and yield were classified by k-means clustering in two-zones that differed significantly in their mean values. ANTH_RG and SFR_R were inversely correlated over 64% of the plot. SFR_R appeared to be a potential useful proxy of yield since it was directly correlated to yield over 66% of the plot. The grape color (ANTH_RG) was slightly higher over the low-yield zones with respect to the high-yield zones. Our study showed that the combination of anthocyanins and chlorophyll indices detected in the field on Crimson Seedless table grape by a portable fluorescence sensor can help in defining the best harvest time and the best areas for harvesting.