Genotype-by-Environment-by-Management (G×E×M) interactions in millet cultivation significantly impact yield, stability, and trait expression due to the complex interplay of genetic makeup, environmental conditions (e.g., soil, climate), and management practices (e.g., irrigation, fertilization). These interactions lead to variable millet performance across regions, as seen in pearl millet trials where yield rankings vary by soil type and preceding crops, complicating cultivar selection. For instance, environmental factors like drought or temperature, combined with management choices, can alter phenotypic outcomes, with studies showing environment explains ~80% of yield variation. Machine learning (ML) addresses these complexities by integrating high-dimensional genotypic, phenotypic, and environmental data to predict outcomes like yield or disease resistance. Models such as Random Forests or LSTM with attention can capture non-linear G×E×M relationships, achieving up to 73% explained variance in maize yield compared to 16% for process-based models. ML also enables genomic selection, reducing phenotyping needs by predicting traits from genetic markers, and incorporates real-time web inputs (e.g., weather APIs) or computer vision for phenotyping, enhancing precision. Challenges include data quality, model interpretability, and computational demands, but ML’s ability to handle large datasets and model interactions offers a path to optimize millet breeding and management for sustainable(https://www.mdpi.com/2073-4395/13/11/2727)[](https://www.mdpi.com/2073-4395/13/8/1970)(https://journals.ametsoc.org/view/journals/aies/1/4/AIES-D-22-0002.1.xml)

Modeling G×E×M (Genotype × Environment × Management) interactions in millet cultivation is inherently complex due to the non-linear, multi-dimensional nature of crop responses. Machine learning (ML) offers a scalable path to capture these interactions and optimize varietal selection under variable field conditions.

In my research titled "Advanced Crop Recommendation System: Leveraging Deep Learning and Fuzzy Logic for Precision Farming," we integrated environmental, soil, and cultivation parameters into a decision-support system using fuzzy logic and deep learning. While the study was crop-agnostic, the underlying architecture is well-suited to accommodate G×E×M modeling in millet. For instance, by training models on geospatial weather, soil attributes, and genotype-specific responses, we can simulate performance under unseen scenarios.

Such approaches enable:

Regional varietal targeting (spatial deployment)

Management-specific yield predictions (e.g., low-input vs high-input systems)

Data-driven genotype ranking under stress scenarios like drought or low soil fertility

Combining G×E×M-aware datasets with attention-based deep learning or hybrid fuzzy-ML pipelines improves generalization and adaptability—critical for underutilized crops like millet in climate-vulnerable zones.

Please read more on my research here :-

Article ADVANCED CROP RECOMMENDATION SYSTEM: LEVERAGING DEEP LEARNIN...