Optimization of Chemical Fungicide Combinations Targeting the Maize Fungal Pathogen, <italic>Bipolaris maydis</italic>: A Systematic Quantitative Approach

Abstract

To control the southern corn leaf blight, a severe disease of maize around the world, a combination of fungicides is often more potent than using individual fungicides. However, the number of possible combinations increases exponentially with the increase of the number of fungicides combined and their concentrations. It is thus extremely challenging to identify effective fungicide combinations by trial and error from all possible combinations. In this paper, a systematic approach based on a support vector machine, a machine learning algorithm, is proposed to searching for the optimal combinations using only a limited number of measurements. The constructed model also incorporates information related to the inhibition rate (IR) and the cost of each composing fungicide into the optimization process. With this method, we show that only around 130 measurements on a coarse grid of concentrations out of thousands of possible experiments are sufficient to reconstruct the response model and to obtain the optimal fungicide combinations. Experimental results demonstrate that the optimized combinations can achieve an IR greater than 90%, while the required concentrations and the cost of individual fungicides are dramatically reduced. We anticipate that this method should be equally effective in the search for optimal combinations of multiple compounds in other diseases.

Proteomic Analysis of the Relationship between Metabolism and Nonhost Resistance in Soybean Exposed to Bipolaris maydis

“…maydis isolate was grown for 8 days at 27°C in a culture medium containing potato dextrose agar (PDA) and in the dark. The PDA medium was prepared as described elsewhere [ 19 ]. Conidia were collected with sterilized water (with 0.1% Tween-20) and the concentration was adjusted to 1 × 10 5 conidia per mL.…”

Study on Machine Learning for Identification of Farmer’s Query in Kannada Language

“…These approaches involves taking the images of seeds of plant leaf as the input and perform segmentation on those images and with the help of SVM, different kind of diseases are detected and distinguished. Wang, Xiang, et al [4] have presented a study on how SVM technique is used in controlling diseases in maize crop. Southern corn beam is the disease that is usually found in maize crops and to solve this problem several combinations of fungicides are used.…”

Unveiling the potential of rice rhizospheric microbes and indigenous organic sources for the management of rice blast

“…Meanwhile, the fungicide resistance limits the fungicidal usage against plant pathogens, and it is evident with the emergence of Quinone outside Inhibitor (QoI) in the Italian population of Pyricularia oryzae has alarmed the farming community to revise and rethink about its practical credibility in the field (Magar et al, 2015; Tenni et al, 2021). Inappropriate and non‐judicious use of synthetic pesticides results in harmful effects to environment, which is of severe public concern today (Wang, Ma, et al, 2015; Wang, Mavrodi, et al, 2015). Conversely, it is well established fact that, the microbial biological approach to disease management seems to be a promising approach for balancing nature's diversity, and it could be utilized independently as well as in combination with other biological entities such as actinomycetes and endophytes (Dubey et al, 2015; Saikia & Bora, 2021).…”