Sustainability in our food and fiber agriculture systems is inherently knowledge intensive. It is more likely to be achieved by using all the knowledge, technology, and resources available, including data-driven agricultural technology and precision agriculture methods, than by relying entirely on human powers of observation, analysis, and memory following practical experience. Data collected by sensors and digested by artificial intelligence (AI) can help farmers learn about synergies between the domains of natural systems that are key to simultaneously achieve sustainability and food security. In the quest for agricultural sustainability, some high-payoff research areas are suggested to resolve critical legal and technical barriers as well as economic and social constraints. These include: the development of holistic decision-making systems, automated animal intake measurement, low-cost environmental sensors, robot obstacle avoidance, integrating remote sensing with crop and pasture models, extension methods for data-driven agriculture, methods for exploiting naturally occurring Genotype x Environment x Management experiments, innovation in business models for data sharing and data regulation reinforcing trust. Public funding for research is needed in several critical areas identified in this paper to enable sustainable agriculture and innovation.

Weed control is commonly performed by applying selective herbicides homogeneously over entire agricultural fields. However, applying herbicide only where needed could have economical and environmental benefits. The objective of this study was to apply remote sensing to the detection of grasses and broadleaf weeds among cereal and broadleaf crops. Spectral relative reflectance values at both leaf and canopy scales were obtained by field spectroscopy for four plant categories: wheat, chickpea, grass weeds, and broadleaf weeds. Total reflectance spectra of leaf tissues for botanical genera were successfully classified by general discriminant analysis (GDA). The total canopy spectral classification by GDA for specific narrow bands was 95 ± 4.19% for wheat and 94 ± 5.13% for chickpea. The total canopy spectral classification by GDA for future Vegetation and Environmental Monitoring on a New Micro-Satellite (VENμS) bands was 77 ± 8.09% for wheat and 88 ± 6.94% for chickpea, and for the operative satellite Advanced Land Imager (ALI) bands was 78 ± 7.97% for wheat and 82 ± 8.22% for chickpea. Within the critical period for weed control, an overall classification accuracy of 87 ± 5.57% was achieved for >5% vegetation coverage in a wheat field, thereby providing potential for implementation of herbicide applications. Qualitative models based on wheat, chickpea, grass weed, and broadleaf weed spectral properties have high-quality classification and prediction potential that can be used for site-specific weed management.

Spatial information embedded in a crop model can improve yield prediction. Leaf area index (LAI) is a well-known crop variable often estimated from remote-sensing data and used as an input into crop models. In this study, we evaluated the assimilation of LAI derived from high-resolution (both spatial and temporal) satellite imagery into a mechanistic crop model, a simple algorithm for yield estimate (SAFY), to assess the within-field crop yield. We tested this approach on spring wheat grown in Israel. Empirical LAI models were derived from the biophysical processor for Sentinel-2 LAI and spectral vegetation indices from Sentinel-2 and PlanetScope images. The predicted grain yield obtained from the SAFY model was compared against the harvester’s yield map. LAI derived from PlanetScope and Sentinel-2 fused images achieved higher yield prediction (RMSE = 69 g/m²) accuracy than that of Sentinel-2 LAI (RMSE = 88 g/m²). Even though the spatial yield estimation was only moderately correlated to the ground truth (R² = 0.45), this is consistent with current studies in this field, and the potential to capture within-field yield variations using high-resolution imagery has been demonstrated. Accordingly, this is the first application of PlanetScope and Sentinel-2 images conjointly used to obtain a high-density time series of LAI information to model within-field yield variability.

The Near East climate ranges from arid to a Mediterranean, under which local wheat landraces have been grown for over millennia, assumingly accumulating a unique repertoire of genetic adaptations. In the current study, we subjected a subset of the Israeli Palestinian Landraces (IPLR) collection (n = 19: durum and bread wheat landraces, modern wheat cultivars, and landraces mixtures) to full-field evaluation. The multifield experiment included a semiarid site (2018–2019, 2019–2020) under low (L) and high (H) supplementary irrigation, and a Mediterranean site (2019–2020). Water availability had a major impact on crop performance. This was reflected in a strong discrimination between environments for biomass productivity and yield components. Compared to landraces, modern cultivars exhibited significantly higher grain yield (GY) across environments (+102%) reflecting the effect of the Green Revolution. However, under the Gilat19 (L) environment, this productivity gap was significantly reduced (only +39%). Five excelling landraces and the durum mix exhibited good agronomic potential across all trails. This was expressed in relatively high GY (2.3–2.85 t ha⁻¹), early phenology (86–96 days to heading) and lodging resistance. Given the growing interest of stakeholders and consumers, these might be considered future candidates for the local artisanal wheat grain market. Yet, this step should be taken only after establishing an adjustable field management protocol.