Detection and Characterization of Stressed Sweet Cherry Tissues Using Machine Learning

Recent technological developments in the primary sector and machine learning algorithms allow the combined application of many promising solutions in precision agriculture. For example, the YOLOv5 (You Only Look Once) and ResNet Deep Learning architecture provide high-precision real-time identifications of objects. The advent of datasets from different perspectives provides multiple benefits, such as spheric view of objects, increased information, and inference results from multiple objects detection per image. However, it also raises crucial obstacles such as total identifications (ground truths) and processing concerns that can lead to devastating consequences, including false-positive detections with other erroneous conclusions or even the inability to extract results. This paper introduces experimental results from the machine learning algorithm (Yolov5) on a novel dataset based on perennial fruit crops, such as sweet cherries, aiming to enhance precision agriculture resiliency. Detection is oriented on two points of interest: (a) Infected leaves and (b) Infected branches. It is noteworthy that infected leaves or branches indicate stress, which may be due to either a stress/disease (e.g., Armillaria for sweet cherries trees, etc.) or other factors (e.g., water shortage, etc). Correspondingly, the foliage of a tree shows symptoms, while this indicates the stages of the disease

Detection and Characterization of Stressed Sweet Cherry Tissues Using Machine Learning

Recent technological developments in the primary sector and machine learning algorithms allow the combined application of many promising solutions in precision agriculture. For example, the YOLOv5 (You Only Look Once) and ResNet Deep Learning architecture provide high-precision real-time identifications of objects. The advent of datasets from different perspectives provides multiple benefits, such as spheric view of objects, increased information, and inference results from multiple objects detection per image. However, it also raises crucial obstacles such as total identifications (ground truths) and processing concerns that can lead to devastating consequences, including false-positive detections with other erroneous conclusions or even the inability to extract results. This paper introduces experimental results from the machine learning algorithm (Yolov5) on a novel dataset based on perennial fruit crops, such as sweet cherries, aiming to enhance precision agriculture resiliency. Detection is oriented on two points of interest: (a) Infected leaves and (b) Infected branches. It is noteworthy that infected leaves or branches indicate stress, which may be due to either a stress/disease (e.g., Armillaria for sweet cherries trees, etc.) or other factors (e.g., water shortage, etc). Correspondingly, the foliage of a tree shows symptoms, while this indicates the stages of the disease

Towards Climate Smart Farming—A Reference Architecture for Integrated Farming Systems

Climate change is emerging as a major threat to farming, food security and the livelihoods of millions of people across the world. Agriculture is strongly affected by climate change due to increasing temperatures, water shortage, heavy rainfall and variations in the frequency and intensity of excessive climatic events such as floods and droughts. Farmers need to adapt to climate change by developing advanced and sophisticated farming systems instead of simply farming at lower intensity and occupying more land. Integrated agricultural systems constitute a promising solution, as they can lower reliance on external inputs, enhance nutrient cycling and increase natural resource use efficiency. In this context, the concept of Climate-Smart Agriculture (CSA) emerged as a promising solution to secure the resources for the growing world population under climate change conditions. This work proposes a CSA architecture for fostering and supporting integrated agricultural systems, such as Mixed Farming Systems (MFS), by facilitating the design, the deployment and the management of crop–livestock-=forestry combinations towards sustainable, efficient and climate resilient agricultural systems. Propelled by cutting-edge technology solutions in data collection and processing, along with fully autonomous monitoring systems, e.g., smart sensors and unmanned aerial vehicles (UAVs), the proposed architecture called MiFarm-CSA, aims to foster core interactions among animals, forests and crops, while mitigating the high complexity of these interactions, through a novel conceptual framework

Sesame Meal, Vitamin E and Selenium Influence Goats' Antioxidant Status

This study aimed to determine the impact of sesame meal, selenium (Se), and vitamin E (VitE) on goats' oxidative status. Thirty mid-lactation crossbred goats were divided into five homogeneous groups, and were fed 1 kg of alfalfa hay and 1.2 kg of concentrates daily. The control group (C) received a basal diet. In the concentrates of the treated groups, 10% of the soybean meal was replaced by sesame meal and no extra VitE or Se (SM), or an extra 60 mg of VitE (SME), or 0.1 mg organic Se (SMSe), or their combination (60 mg VitE and 0,1 mg organic Se/kg of concentrate (SMESe). In the plasma of the goats, the dietary treatments did not affect glutathione reductase, glutathione peroxidase, glutathione transferase, catalase, superoxide dismutase activities, malondialdehyde (MDA) content, or the total antioxidant capacity. A reduction and a trend for lower protein carbonyls content was found in goats fed SM (p = 0.03) and SME (p = 0.06) compared to SMESe. In the milk, the lactoperoxidase activity decreased with SMSe and SMESe. A numerical decrease in the total antioxidant capacity and an increase in the MDA content in the milk of the SMESe group compared with the other treated groups was found. In mid-lactation goats, SM improves the oxidative status of both the organism and the milk