Handheld chlorophyll meters as Soil Plant Analysis Development (SPAD) have proven to be useful tools for rapid, no-destructive assessment of chlorophyll and nitrogen status in various crops. This method is used to diagnose the need of nitrogen fertilization to improve the efficiency of the agricultural system and to minimize nitrogen losses and deficiency. The objective of this study is to evaluate the effect of repeated conservative agriculture practices on the SPAD readings, leaves chlorophyll concentration and Nitrogen Nutrition Index (NNI) relationships in durum wheat under Mediterranean conditions. The experimental site is a part of a long-term-experiment established in 1994 and is still on-going where three tillage managements and three nitrogen fertilizer treatments were repeated in the same plots every year. We observed a linear relationship between the SPAD readings performed in the central and distal portion of the leaf (R² = 0.96). In fertilized durum wheat, we found all positive exponential relationships between SPAD readings, chlorophyll leaves concentration (R² = 0.85) and NNI (R² = 0.89). In the unfertilized treatment, the SPAD has a good attitude to estimate leaves chlorophyll concentration (R² = 0.74) and NNI (R² = 0.77) only in crop grow a soil with relative high content of soil organic matter and nitrogen availability, as observed in the no tilled plots. The results show that the SPAD can be used for a correct assessment of chlorophyll and nitrogen status in durum wheat but also to evaluate indirectly the content of soil organic matter and nitrogen availability during different growth stages of the crop cycle.

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.

Combining remote and proximal sensing in agriculture is essential to monitor crop spatial-temporal variability and to provide high-quality prescription maps for the precision agriculture applications. The study showed how different combinations of soil management (no tillage—NT vs. conventional tillage—CT) and nitrogen (N) fertilization levels (0.90 and 180 kg N ha⁻¹) can affect the durum wheat nutritional status and development through vegetation indices computation and proximal sensing tool application. Chlorophyll and N crop content were measured, in addition a proximal sensing tool and multispectral imagery equipped on unmanned aerial vehicle were used. The N input is the key driver for durum wheat development (4.5 ± 0.92 t ha⁻¹ on average), but when it was not provided the NT performed better than CT (2.51 ± 0.22 vs. 1.46 ± 0.28 t ha⁻¹ respectively) in terms of grain yield. This is due to the greater content of organic matter and N availability which characterizes the NT system. The near infrared (NIR) band-based vegetation indices can well detect the durum wheat nutritional status (R² = 0.70 on average). The showed results can provide an important contribution in the implementation of ago-environmental policies aimed at environmental impact of cereal-based-cropping systems reduction.

The forecasting of crop yield is one of the most critical research areas in crop science, which allows for the development of decision support systems, optimization of nitrogen fertilization, and food safety. Many tested modeling approaches can be differentiated according to the models and data used. The models used are traditional crop models that require data that are often difficult to measure. New modeling approaches based on artificial intelligence algorithms have proven to be of high performance, flexible, and can be tested based on available data. In this study, four independent field experiments conducted on Triticum turgidum subsp. durum Desf. in central–southern Italy were used to train a set of machine learning (ML) algorithms to predict the yield using 16 variables: fertilization, nitrogen management, pedoclimatic, and remote sensing data. Four ML algorithms were calibrated and validated over two independent sites, and a linear regression model was used as a control. The calibrated models can predict the grain yield in the two regions by using ancillary data, topsoil physical and chemical properties, multispectral drone imagery, climatic data, and nitrogen fertilizer applied at the site. Among the four ML algorithms, stochastic gradient boosting (root‐mean‐square error = 0.58 t ha⁻¹) outperformed others during calibration and transferability. Nitrogen application rate, seasonal precipitation, and temperature are the most important features for predicting wheat yield.

Mostly, precision agriculture applications include the acquisition and elaboration of images, and it is fundamental to understand how farmers’ practices, such as soil management, affect those images and relate to the vegetation index. We investigated how long-term conservation agriculture practices, in comparison with conventional practices, can affect the yield components and the accuracy of five vegetation indexes. The experimental site is a part of a long-term experiment established in 1994 and is still ongoing that consists of a rainfed 2-year rotation with durum wheat and maize, where two unfertilized soil managements were repeated in the same plots every year. This study shows the superiority of no tillage over conventional tillage for both nutritional and productive aspects on durum wheat. The soil management affects the vegetation indexes’ accuracy, which is related to the nitrogen nutrition status. No-tillage management, which is characterized by a higher content of soil organic matter and nitrogen availability into the soil, allows obtaining a higher accuracy than the conventional tillage. So, the users of multispectral cameras for precision agriculture applications must take into account the soil management, organic matter, and nitrogen content.

The acceleration of Digital Agriculture is evident through the increased adoption of digital technologies on farms including smart machines, sensors and cloud computing. In this paper we present the preliminary results of the research project funded by Università Politecnica delle Marche in 2018 “PFRLab: Setting of a precision farming robotic laboratory for cropping system sustainability and food safety and security”, which is still underway. In this context, as first result, an interdepartmental Research and Services Center called “Smart Farming” has been set up with the aim to strengthen multidisciplinary collaborations in the fields of Agriculture and Forestry, Geomatics, ICT and Robotics. Regarding field activities the SPAD 502 as well as Normalized Difference Vegetation Index (NDVI) provide a good estimate of the Chlorophylla+b content in durum wheat leaves so can be used to predict in a quickly and non-destructively way, the crop greenness status and to identify any nutritional deficiencies in real time. Future research activities are certainly needed to fully explore the potentialities of conservation agriculture and precision farming, and to drive the transition process from conventional agriculture to modern conservation agriculture and precision farming techniques. In-depth studies are planned on the combined effect of nitrogen fertilization and soil management on the main production variables of durum wheat in order to evaluate whether specific tools for precision agriculture applications can find significant diffusion even in Mediterranean cereal based cropping systems.