Computer vision is a powerful technology that has enabled solutions in various fields by analyzing visual attributes in images. One field that has taken advantage of computer vision is agricultural automation, which promotes high-quality crop production. The nutritional status of a crop is a crucial factor in determining its productivity. This status is mediated by approximately 14 chemical elements acquired by the plant, and their determination plays a pivotal role in farm management. To address the timely identification of nutritional disorders, this study focuses on the classification of three levels of phosphorus deficiencies through individual leaf analysis. The methodological steps include: (1) generating a database with laboratory-grown maize plants that were induced to total phosphorus deficiency, medium deficiency, and total nutrition, using different capture devices; (2) processing the images with state-of-the-art transfer learning architectures (i.e. VGG16, ResNet50, GoogLeNet, DenseNet201, and MobileNetV2); and (3) evaluating the classification performance of the models using the created database. The results show that the VGG16 model achieves superior performance, with 98% classification accuracy. However, the other studied architectures also demonstrate competitive performance and are considered state-of-the-art automatic leaf deficiency detection tools. The proposed method can be a starting point to fine-tune machine vision-based solutions tailored for real-time monitoring of crop nutritional status.

Frosty pod rot, caused by Moniliophthora roreri, is the most damaging disease of cacao in Latin America. However, to better comprehend its epidemiology, we must understand its dissemination and proliferation. Still, we ignore how loads of M. roreri spores fluctuate across growing seasons since we lack a reliable technique to quantify M. roreri spores in the fields. Therefore, we developed a method that uses a spore trap to capture M. roreri spores and qPCR to quantify them. This study demonstrated that this technique could quantify 3.9 x104 M. roreri spores with a 95 % confidence level. However, it could not differentiate between M. roreri and its close relative, M. perniciosa. Despite this limitation, we could detect and quantify Moniliophthora spores from environmental samples taken from a cacao field. This technique can help the phytopathologist address studies more accurately in disseminating cacao pathogens.