Search

The use of peat in traditional cultivation systems and in commercial nurseries is an environmental problem. In this work, we evaluated the partial replacement of peat with different amounts of biochar sourced from vineyard pruning as plant growing substrates. We studied its effect on the growth of lettuce plants under greenhouse and semi-hydroponic conditions. Substrate mixtures contained 30% (v/v) of vermiculite and 70% (v/v) of different biochar:peat treatments as follows: 0:70 (B0), 15:55 (B15), 30:40 (B30), 50:20 (B50), and 70:0 (B70). Higher biochar treatments increased the pH and electrical conductivity of the substrate, negatively affecting plant growth and germination (especially in B70). The partial substitution of peat by 30% biochar (B30) delayed seed germination but improved plant growth and nitrogen use efficiency (NUE), with shoots containing higher levels of organic nitrogen and nitrate. Moreover, it increased the water holding capacity (WHC) and led to an efficient use of nutrients. Our study demonstrates that biochar can successfully replace and reduce peat and N fertilizer consumption. This has the potential to promote more sustainable farming with positive impacts on both plant growth and the environment.

The increasing public concern about food security and the stricter rules applied worldwide concerning herbicide use in the agri-food chain, reduce consumer acceptance of chemical plant protection. Site-Specific Weed Management can be achieved by applying a treatment only on the weed patches. Crop plants and weeds identification is a necessary component for various aspects of precision farming in order to perform on the spot herbicide spraying or robotic weeding and precision mechanical weed control. During the last years, a lot of different methods have been proposed, yet more improvements need to be made on this problem, concerning speed, robustness, and accuracy of the algorithms and the recognition systems. Digital cameras and Artificial Neural Networks (ANNs) have been rapidly developed in the past few years, providing new methods and tools also in agriculture and weed management. In the current work, images gathered by an RGB camera of Zea mays, Helianthus annuus, Solanum tuberosum, Alopecurus myosuroides, Amaranthus retroflexus, Avena fatua, Chenopodium album, Lamium purpureum, Matricaria chamomila, Setaria spp., Solanum nigrum and Stellaria media were provided to train Convolutional Neural Networks (CNNs). Three different CNNs, namely VGG16, ResNet–50, and Xception, were adapted and trained on a pool of 93,000 images. The training images consisted of images with plant material with only one species per image. A Top-1 accuracy between 77% and 98% was obtained in plant detection and weed species discrimination, on the testing of the images.