Fusarium head blight (FHB), caused mainly by Fusarium graminearum, results in devastating economic losses in small grain cereal crops. In regions where FHB occurs frequently, breeding for resistance to the disease is a priority in breeding programs. Screening lines or cultivars for resistance to FHB under controlled conditions is necessary due to the sporadic nature of the disease and lack of control of error under field research conditions. However, screening for resistance is challenging due to the quantitative nature of resistance to FHB and the variability in aggressiveness of pathogen isolates. High or low inoculum concentrations or too much humidity can lead to inaccurate results. Greenhouse experiments were conducted to determine the optimum inoculum concentration and spike bagging period following inoculation for discriminating between a susceptible (cultivar Samson) and a moderately resistant (Glenn) spring wheat (Triticum aestivum L.) cultivar. In one experiment, spikes were inoculated at anthesis with the standard F. graminearum conidia concentration of 1 × 105 spores/mL and 1/2, 1/4, 1/8, and 1/16 of the standard concentration. In a second experiment, spikes were inoculated at anthesis with the standard spore concentration of 1 × 105 spores/mL and covered with plastic reclosable bags for 12, 24, 36, 48, or 72 h. In both experiments, FHB severity was visually assessed seven times at 3-day intervals following inoculation. The percentage of Fusarium-damaged kernels (FDK) and deoxynivalenol (DON) concentration were determined after harvest. FHB severity was the best variable for discriminating between the two cultivars. FHB severity results showed that ½ of the standard spore concentration best discriminated between the two cultivars from the 9th to the 15th day post inoculation whereas bagging spikes for 72 h following inoculation at the standard conidial concentration best discriminated between the two cultivars from the 9th to the 18th day post inoculation. The results from this study indicated that for screening wheat cultivars for resistance to FHB under greenhouse conditions, 5 × 104 spores/mL (1/2 of the standard F. graminearum spore concentration) was more accurate, and the optimum spike bagging period following inoculation was 72 h.
Canker disease is an important disease affecting Pinus merkusii plantations in Indonesia. However, the factors associated with its occurrence and severity under operational plantation conditions remain poorly understood. This study quantified canker disease incidence and severity across 39 plots in Central Java and evaluated their associations with stand structure, elevation, and resin-tapping status. Five fungal isolates obtained from symptomatic tissues were characterized morphologically and molecularly, and pathogenicity was evaluated using P. merkusii seedlings. Lasiodiplodia theobromae as identified based on ITS and EF1-α sequences reproduced canker and dieback symptoms in inoculated seedlings, supporting its role as the primary pathogen associated with the disease. Disease incidence averaged 76.9
Banana (Musa spp.) is a major fruit crop, with India being the largest global producer. However, viral diseases increasingly threaten its productivity, among which cucumber mosaic virus (CMV) has emerged as a significant constraint. This study assessed the incidence, distribution, and genetic diversity of CMV infecting banana across major growing regions of India during 2024–2025. Field surveys conducted in six states revealed widespread occurrence of CMV, with disease incidence ranging from 10 to 70
Quantifying foliar disease severity accurately and reproducibly remains a fundamental challenge in plant pathology, as traditional visual assessment is inherently subjective and prone to rater variability. Although computer vision has enabled automated disease detection, validated end-to-end frameworks that integrate reference generation, model training, and field-deployable percent severity estimation are lacking. Here, we developed and validated a computer vision pipeline for quantifying powdery mildew (Oidium spp.) severity in pea (Pisum sativum) using smartphone-acquired images under field conditions, with tomato (Solanum lycopersicum) powdery mildew serving as a cross-species validation system. A semi-automated multichannel segmentation approach generated continuous percent severity estimates as reproducible training references. Among evaluated deep learning architectures, a YOLO-based model was selected for its superior balance of predictive accuracy, computational efficiency, and deployment flexibility and was integrated into PowderyComputerVision, a purpose-built web application enabling real-time inference. Under field conditions, the automated system showed substantially higher agreement with reference severity values than trained human raters (concordance correlation coefficient (CCC), 0.952 vs. 0.513). A linear mixed-effects model revealed that human evaluators produced absolute estimation errors 280.96 times greater than the automated system (p < 0.0001; 95
