Vascular wilt (Fusarium oxysporum f. sp. niveum [Fon]) is the main fungal disease of watermelon crops worldwide and can be caused by any of the four known races of this fungus (race 0, 1, 2 and 3). In Mexico, there are no reports of the presence of any of these races. The presence of plants with wilt and vascular necrosis has been observed in commercial watermelon plantations in the municipality of Hermosillo, Sonora, Mexico, mainly during the spring-summer growing seasons. The aim of this study was to identify the Fon race causing vascular wilt in watermelon plants in Hermosillo, Sonora. Three fungal isolates were collected from symptomatic plants. Based on their morphological characteristics, DNA amplification of the isolates (with specific primers Fon-1/Fon-2, FONSIX6F/FONSIX6R and FNR3-F/FNR3-R, which allow differentiating Fon race 1, 2 and 3) and inoculation into three differential watermelon cultivars, the three isolates (UESFON01, UESFON02 and UESFON03) were identified as F. o. f. sp. niveum race 1. To our knowledge, this is the first report where F. o. f. sp. niveum race 1 is identified as the causal agent of wilt, vascular necrosis and plant death in watermelon crops in Mexico.

The transport of sugars, a product of photosynthesis, involves the distribution of carbohydrates from synthesis organs, such as leaves, to the tissues of demand organs such as roots, stems, flowers, fruits and seeds. Their mobilization occurs through the plasmodesmata or apoplast by means of membrane transporters, which are proteins encoded by the so-called sugar transporter genes. Their expression in plants is modified by the presence of microorganisms that colonize them. Studies on their expression in plantnematode interactions are relatively recent, and in plant-beneficial endophyte fungus interactions they have been carried out mainly with arbuscular mycorrhizal fungi. In this review, information on the current state of knowledge of sugar transporters in plantendophyte fungus and plant-nematode interactions is summarized.

Environmental pollution, food sufficiency, and health are among the main challenges for sustainable crop production in Mexico. The main tool for plant protection is chemical pesticides, but environmental safety and risks to animal and human health have prompted the community and scientists to propose alternatives for their integrated management. This has encouraged the inclusion of biological products, such as species of the antagonistic fungus Trichoderma spp., which act against pests and pathogens in different crops, and are environmentally benign, effective and economically viable. The aim of this paper was to compile information on Trichoderma spp. regarding their taxonomy, ecology, use as biological control, isolation, culture, morphological and molecular identification, mode of action, distribution of native species, and availability of international commercial products and those produced in Mexico. For this purpose, information was selected from indexed journals and suppliers of Trichoderma spp. on open data digital platforms. The exposed information could be useful for Mexican farmers, to know, accept and use biological control with Trichodermaspp. in their crops, for the benefit of quality agriculture and pesticide-free crops. In addition, research could be generated to develop new autochthonous products based on Trichodermaspp. that are more effective against pests and diseases native to Mexico.

Timely identification of phytosanitary problems in agricultural crops is essential to reduce production losses. Artificial intelligence algorithms facilitate their rapid and reliable identification. In this research, three learning classifiers, namely random forest (RF), support vector machine (SVM) and multilayer perceptron (MLP), were evaluated to identify three target classes (healthy fruit, anthracnose [Colletotrichum spp.] and scab [Sphaceloma perseae]) from digital fruit images. Two color descriptor extraction techniques (region selection and image subsampling) were compared with the RF classifier, and an overall classification accuracy (ACC) of 98±0.03 % with region selection and 84±0.08 % with subsampling was obtained. Subsequently, the classifiers were evaluated with color descriptors extracted with region selection. RF and MLP were superior to SVM, with an ACC of 98±0.03 %. Scab and anthracnose were identified with an F1 score of 98 %. %. The high performance of the classifiers shows the potential for applying artificial intelligence paradigms to identify phytosanitary problems in agricultural crops.

Solanum demissum Lindl. is one of the most valued and used species for potato (Solanum tuberosum L.) breeding due to its richness in genes for resistance to various biotic and abiotic factors. Over the years, it has been a plant of interest, mainly for its resistance to potato late blight (caused by Phytophthora infestans [Mont.] de Bary.), which has led to crosses between S. demissum and S. tuberosum to obtain resistant cultivars. It has also been used for identifying physiological races of P. infestans and, recently, it has been associated with glycoalkaloids and antioxidant compounds, whose potential uses are of interest to various industries. Since there is great potential in the species S. demissum, not only as a source of resistance genes but also as a source of important bioactive compounds, this review summarizes the research conducted on the use of Solanum demissum in the breeding of cultivated potato, as well as its potential uses in other areas. In addition, areas of opportunity and limitations for breeding for resistance to late blight are highlighted to serve as a starting point for the development of new research or lines.