A Review on Biocontrol Agents as Sustainable Approach for Crop Disease Management: Applications, Production, and Future Perspectives
Bibliographic record
Abstract
Horticultural crops are vulnerable to diverse microbial infections, which have a detrimental impact on their growth, fruit quality, and productivity. Currently, chemical pesticides are widely employed to manage diseases in horticultural crops, but they have negative effects on the environment, human health, soil physiochemical properties, and biodiversity. Additionally, the use of pesticides has facilitated the development and spread of resistant pathovars, which have emerged as a serious concern in contemporary agriculture. Nonetheless, the adverse consequences of chemical pesticides on the environment and public health have worried scientists greatly in recent years, which has led to a switch to the use of biocontrol agents such as bacteria, fungi, and insects to control plant pathogens. Biocontrol agents (BCAs) form an integral part of organic farming, which is regarded as the future of sustainable agriculture. Hence, harnessing the potential of BCAs is an important viable strategy to control microbial disease in horticultural crops in a way that is also ecofriendly and can improve the soil health. Here, we discuss the role of the biological control of microbial diseases in crops. We also discuss different microbial-based BCAs such as fungal, bacterial, and viral and their role in disease management. Next, we discuss the factors that affect the performance of the BCAs under field conditions. This review also highlights the genetic engineering of BCAs to enhance their biocontrol efficiency and other growth traits. Finally, we highlight the challenges and opportunities of biocontrol-based disease management in horticulture crops and future research directions to boost their efficacy and applications.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".