Apple scab, caused by the ascomycete fungus Venturia inaequalis (Cooke) G. Winter (1875), is one of the most devastating diseases of apple (Malus × domestica) worldwide, including in Morocco, where apple cultivation plays an important role in the arboriculture sector. This disease can spread widely under favorable environmental conditions and in the absence of effective preventive and curative measures, leading to severe economic losses due to fruit damage, reduced yields, and increased costs associated with fungicide applications. Moreover, the intensive and repeated use of chemical fungicides has contributed to the emergence of V. inaequalis strains that are resistant to several active substances. In Morocco, thirteen active fungicidal compounds are currently used to control this disease. This review presents all the active ingredients authorized for use in Morocco against apple scab and evaluates their efficacy. It also discusses the development of resistance to systemic fungicides by Venturia inaequalis at both the national and global levels and assesses their future prospects. Finally, this article highlights alternative approaches to chemical control, including biological and genetic interventions in the management of apple scab, with an emphasis on the potential of these strategies to guide and support future research efforts.
Tomato (Lycopersicon esculentum Mill) is a frequently grown vegetable in various regions of the world. Tomatoes are one of the most demanded vegetables and a source of revenue for third-world countries. Tomato plant diseases can significantly reduce tomato production and impact a nation’s economic development. Timely and reliable detection of tomato leaf diseases can increase tomato production, ensure food security, and sustain agricultural activities. A Convolutional Neural Network (CNN) model was employed in this study to detect and classify Fungal, Bacterial, and Viral diseases in nine types of tomato leaves. High-resolution images of plant leaves damaged by pathogens were used for training and testing the CNN model. The dataset used in Experiment 1 consisted of nine different tomato leaf classes, including one healthy leaf class and eight pathogen-affected classes. The dataset used for Experiment 2 was categorized into three classes based on the pathogens: bacteria, fungi, and viruses. Experiment 1 achieved an accuracy of 93%, and Experiment 2 achieved 96% accuracy in detecting tomato leaf diseases. The results demonstrate that the deep learning-based Convolutional Neural Network (CNN) model is an advanced and effective solution for the identification of plant leaf diseases. This research will support better crop planning and help increase overall tomato production.
Charcoal rot, caused by Macrophomina phaseolina, is one of the main difficult-to-control diseases that affect cowpea. Biocontrol has been gaining prominence for being an economically viable alternative with low risk to the environment. This study aimed to identify and evaluate bacterial isolates from the Caatinga biome for managing charcoal rot in cowpea. For this purpose, in vitro tests were performed, where the bacterial inhibition capacity under the growth of M. phaseolina strains was evaluated, and the production of non-volatile metabolites with an inhibitory effect on the mycelial growth of the fungus. In vivo tests, cowpea seeds were bacterized and sown in sterilized and natural soil with inoculum of M. phaseolina strains Fe 01 and Fe 02, for evaluation of Disease Severity Index (ISD%) and other growth variables and seedling development. Of the six isolates, four reduced more than 60% of fungal growth as well as their non-volatile metabolites. In vivo, the bacterium B. licheniformis FO5.7 applied to the seeds reduced the ISD% caused by M. phaseolina Fe 01 by more than 60% both in sterilized soil and in natural soil, and provided an increase in shoot and root mass, as well as the length of both. For M. phaseolina Fe 02, Bacillus sp. T 6.2 and M 7.1 reduced the severity in sterilized soil and positively influenced the growth and development of seedlings, however, they were not efficient in controlling the disease in natural soil.
Bacterial panicle blight (BPB), caused by Burkholderia glumae, is a devastating disease of rice, with its severity exacerbated by warm and humid conditions, posing a significant threat to global food security. This study aimed to quantify the relationships between environmental factors, disease development, and yield loss, and to define specific thresholds for predictive disease management. A controlled glasshouse experiment was conducted using a susceptible rice variety, with disease progression, panicle sterility, and microclimate data monitored following inoculation. Results demonstrated a significantly higher area under the disease progress curve (AUDPC) in inoculated plants (p < 0.001). A strong positive correlation was identified between final disease severity and panicle sterility (y = 0.58x + 16, R & sup2; = 0.77), confirming the direct impact of BPB on yield. Furthermore, receiver operating characteristic (ROC) analysis established actionable forecasting thresholds, identifying maximum relative humidity >83.7% (AUC = 0.773) and temperature >29.7 degrees C as robust predictors of economic damage. This study provides a quantitative framework that bridges the gap between disease epidemiology and practical management. The defined thresholds enable the development of a precision forecasting system, empowering farmers to implement timely interventions and advancing the goal of sustainable, climate-resilient rice production.
Root-knot nematodes (RKN), particularly Meloidogyne spp., are major soil-borne pests that cause substantial damage to grapevines worldwide. A survey was conducted to determine the incidence, prevalence, species composition, and severity of RKN infestation in grape orchards across the Pothohar region of Pakistan. The study revealed an overall prevalence of 60.35% and an incidence of 19.9% in the surveyed orchards. Among the four districts, Rawalpindi exhibited the highest prevalence (74.28%) and incidence (21.90%), whereas Jhelum showed the lowest prevalence (31.42%) and incidence (9.52%). Seven grapevine varieties were recorded during the survey. Among these, NARC Black showed the highest prevalence (87.5%) and incidence (30.83%) of RKN infestation, whereas Sughra One and Flame Seedless exhibited the lowest prevalence (35%) and incidence (<= 10%), respectively. Two species, Meloidogyne incognita and M. javanica, were identified, with M. javanica being the dominant species in most districts, except in Jhelum where M. incognita predominated. Sole infestations of M. incognita and M. javanica were recorded in 36.5% and 43.25% of orchards, respectively, while mixed populations occurred in 20.25% of infested orchards. The highest galling index among varieties (2.5) was recorded for NARC Black in Rawalpindi, whereas the lowest (0.5) occurred in Flame Seedless in Jhelum. District-wise, Attock exhibited the highest mean galling index (2.0), while Jhelum had the lowest (0.7). These findings demonstrate the widespread distribution and variability of RKN infestations in grape orchards of the Pothohar region and emphasize the need for integrated nematode management strategies tailored to regional conditions and varietal susceptibility. The differences in prevalence and incidence among districts were statistically significant (p <= 0.05).
