Plant Phenomics最新文献

The cultivation of seed mixtures for local pastures is a traditional mixed cropping technique of cereals and legumes for producing, at a low production cost, a balanced animal feed in energy and protein in livestock systems. By considerably improving the autonomy and safety of agricultural systems, as well as reducing their impact on the environment, it is a type of crop that responds favorably to both the evolution of the European regulations on the use of phytosanitary products and the expectations of consumers who wish to increase their consumption of organic products. However, farmers find it difficult to adopt it because cereals and legumes do not ripen synchronously and the harvested seeds are heterogeneous, making it more difficult to assess their nutritional value. Many efforts therefore remain to be made to acquire and aggregate technical and economical references to evaluate to what extent the cultivation of seed mixtures could positively contribute to securing and reducing the costs of herd feeding. The work presented in this paper proposes new Artificial Intelligence techniques that could be transferred to an online or smartphone application to automatically estimate the nutritional value of harvested seed mixes to help farmers better manage the yield and thus engage them to promote and contribute to a better knowledge of this type of cultivation. For this purpose, an original open image dataset has been built containing 4,749 images of seed mixes, covering 11 seed varieties, with which 2 types of recent deep learning models have been trained. The results highlight the potential of this method and show that the best-performing model is a recent state-of-the-art vision transformer pre-trained with self-supervision (Bidirectional Encoder representation from Image Transformer). It allows an estimation of the nutritional value of seed mixtures with a coefficient of determination R² score of 0.91, which demonstrates the interest of this type of approach, for its possible use on a large scale.

In intercropping systems, higher crops block direct radiation, resulting in inevitable shading on the lower crops. Cumulative shading capacity (CSC), defined as the amount of direct radiation shaded by higher crops during a growth period, affects the light interception and radiation use efficiency of crops. Previous studies investigated the light interception and distribution of intercropping. However, how to directly quantify the CSC and its inter-row heterogeneity is still unclear. Considering the canopy height differences (H_ms, obtained using an unmanned aerial vehicle) and solar position, we developed a shading capacity model (SCM) to quantify the shading on soybean in maize-soybean intercropping systems. Our results indicated that the southernmost row of soybean had the highest shading proportion, with variations observed among treatments composed of strip configurations and plant densities (ranging from 52.44% to 57.44%). The maximum overall CSC in our treatments reached 123.77 MJ m^-2. There was a quantitative relationship between CSC and the soybean canopy height increment (y = 3.61 × 10^-2×ln(x)+6.80 × 10^-1, P < 0.001). Assuming that the growth status of maize and soybean was consistent under different planting directions and latitudes, we evaluated the effects of factors (i.e., canopy height difference, latitude, and planting direction) on shading to provide insights for optimizing intercropping planting patterns. The simulation showed that increasing canopy height differences and latitude led to increased shading, and the planting direction with the least shading was about 90° to 120° at the experimental site. The newly proposed SCM offers a quantitative approach for better understanding shading in intercropping systems.