The purpose of this paper is to identify the main factors affecting the consumption of avocado fruit among Italian consumers. In order to respond to the aim of the study, an empirical survey was conducted through the submission of an online questionnaire to 327 consumers. An ordered logit econometric model was adopted to examine the relationship among some explanatory variables and the frequency of consumption of avocado fruit. The findings of this study show that the consumption of avocado fruit is affected by different factors, including fruit consumption habit, neophilia attitudes, and various intrinsic and extrinsic quality attributes (credence attributes in particular). The study deals with a little explored topic; therefore, the findings contribute to fill the gap in the existent literature, inasmuch as they enrich the discussion on consumers' preference for avocado fruit. However, further comparative research is clearly needed, as well as a larger research sample, to overcome limits to the external validity of the results and to investigate the analytical effort proposed in this article.
In recent years, agricultural robotics has received great attention in research studies, being considered a way to address some important issues of the agricultural sector, such as precision agriculture, resources saving, improvement of safety conditions, and shortage of human labor. These issues are particularly relevant in greenhouse production systems, where many highly repetitive and sometimes dangerous operations are still required to be performed by humans. The purpose of the present review is providing an overview of the research conducted in recent years related to robotic automation for greenhouse applications. The currently available literature about robots and automated solutions for greenhouse applications has been reviewed through the consultation of international databases of journals. A total of 38 publications were included after screening and the information related to each retrieved automated solution was classified. The research highlighted great variability among studies, which often describe automation solutions designed for specific crops and define the specific “supporting tasks” necessary for the completion of a “main task”. Specifically, the technologies used for guidance and navigation systems, crop detection and fruit grasping system, spraying system, and other minor supporting tasks have been described. Furthermore, a critical appraisal of the main challenges of the sector and future research directions are provided.
This paper investigates the potential of a consumer-grade infrared stereo camera, i.e. the Intel RealSense D435, to automatically extract crop status information, such as Normalized Difference Vegetation Index (NDVI), in arable and permanent crops. The sensing device includes two infrared (IR) sensors for depth calculation and one colour sensor, which provide, for each point of the scene, both IR and visible light information thus making it possible pixel per pixel NDVI estimations. Measurements were performed on various arable crops including corn (Zea mays) and barley (Ordeum vulgare) and on two vine varieties, Freisa and Malvasia, and were compared to measurements taken by a Trimble GreenSeeker handheld crop sensor. Results show that the RealSense camera tends to underestimate NDVI values compared to the GreenSeeker, with squared correlation coefficient r2 = 0.68. The fitted regression equation is successively applied to correct new camera observations, resulting in good agreement with the GreenSeeker output. The use of the RGB-D camera to simultaneously provide canopy height measurements by a farmer robot is also demonstrated in a Malvasia field, showing that the proposed system can be effectively adopted for fully automated plant-scale monitoring of vineyards.
Agricultural soils provide a variety of ecological services, including nutrient cycling, water purification and storage, carbon sequestration, and flood protection. Soil Surface Roughness (SSR) represents a key parameter for evaluating the terrain quality structure, especially in the layers beneath the usual primary tillage depth, and therefore its estimation has been widely investigated over the years in many fields of science and engineering. This paper proposes the adoption of an innovative sensing approach that relies on contactless measurements provided by a depth camera in contrast to traditional contact measurements such as pin meter and roller chain. In addition, novel features are investigated to achieve a complete statistical description of the SSR with the least number of parameters. The proposed methods are validated in an experimental campaign performed on a vineyard plot. This research could be useful for many applications, including soil erosion prediction models, autonomous vehicle navigation in rural and agricultural settings, and controlled traffic farming.
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