Validation of Rapid and Low-Cost Approach for the Delineation of Zone Management Based on Machine Learning Algorithms

Abstract

Proximal soil sensors are receiving strong attention from several disciplinary fields, and this has led to a rise in their availability in the market in the last two decades. The aim of this work was to validate agronomically a zone management delineation procedure from electromagnetic induction (EMI) maps applied to two different rainfed durum wheat fields. The k-means algorithm was applied based on the gap statistic index for the identification of the optimal number of management zones and their positions. Traditional statistical analysis was performed to detect significant differences in soil characteristics and crop response of each management zones. The procedure showed the presence of two management zones at both two sites under analysis, and it was agronomically validated by the significant difference in soil texture (+24.17%), bulk density (+6.46%), organic matter (+39.29%), organic carbon (+39.4%), total carbonates (+25.34%), total nitrogen (+30.14%), protein (+1.50%) and yield data (+1.07 t ha−1). Moreover, six unmanned aerial vehicle (UAV) flight missions were performed to investigate the relationship between five vegetation indexes and the EMI maps. The results suggest performing the multispectral images acquisition during the flowering phenological stages to attribute the crop spatial variability to different soil proprieties.

Modelling the Impact of Water Stress during Post-Veraison on Berry Quality of Table Grapes

“…Machine learning is used to teach machines how to handle the data more efficiently when we cannot interpret the information extracted from them [72]. In supervised machine learning algorithms, a set of input variables (for instance, water stress integral and fruit load) are used to predict a response variable (for instance, berry color or firmness at harvest).…”

Spatially Correlated Nuclear Magnetic Resonance Profiles as a Tool for Precision Agriculture

“…Both fields were characterized and conducted by the University of Basilicata, in the framework of the same research project, according to a precision farming protocol. Cultivation details and characterization of both fields are described in detail by Denora et al 13 Sample Preparation. Lyophilized green material (shoots and flowering ears) were ground in an IKA ULTRA-TURRAX homogenizer with 20 steal balls at 4000 rpm for 2 min.…”

“…In this work, we analized by NMR profiling the metabolic expression of durum wheat at three different vegetation stages and in two different fields of the Basilicata region in the south of Italy conducted by precision farming techniques. 13 The use of georeferenced samples and geostatistics allowed for the evaluation of the spatial variability of the metabolome at each development stage.…”

Coverage Path Planning Optimization of Heterogeneous UAVs Group for Precision Agriculture

“…According to Eagle Brother, the agricultural UAV market is expected to grow to $5.19 billion by 2025 [10] primarily because UAVs have a number of advantages over satellites and manned aircraft [11]. The intensive use of UAVs is conditioned by such factors as relatively low cost [12], availability in the market [13], ease of use and control [14], use in operation in hard-to-reach places [15], and the possibility of changing the functionality depending on the task, which should be solved [16].…”