How AI is Revolutionizing Agriculture: A Deep Dive into Advanced Models – By Gaurab Dhakal || Krishi Vines

Introduction

Agriculture has been the backbone of human civilization for thousands of years, developing along with technological development. Nowadays, AI leads in agriculture’s revolution, enabling farmers to increase outputs, optimize resources, and even make exact predictions. From crop monitoring, disease detection, climate resilience, and irrigation optimization, AI models are changing modern farming making a big impact on production with different models excelling in various tasks.

Crop Monitoring and Disease Detection

One of the most critical challenges in agriculture is early plant stress and crop disease identification. For the early detection of such disorders, AI models, especially those based on deep learning. DeepLabV3+ semantic segmentation models are usually trained to classify satellite or drone images in each pixel in a picture into healthy and bad agricultural patches (Luo et al., 2023b). Another powerful model is ResNet in the field of image classification. This would be ideal for plant disease identification since it retains the features in detail using deep convolutional layers. Meanwhile, YOLO, a real-time object identification model, can capture field photos to identify crop diseases, weeds, and pests (Sonawane & Patil, 2024b).

Soil Health

Machine learning models such as XGBoost, Random forest, and decision trees are effective in analyzing soil quality based on factors like pH level, nutrient content, and moisture level. These model’s ensemble learning from multiple decision trees on the classification of soil properties, leading to more accurate prediction.

Yield prediction 

Predicting crop yield is essential for food security. AI models like CNN LSTM (Convolution Neural Networks Long Short-Term Memory) extract features from images and process the time series of weather data available from sources like sentinel 2 satellite image, NASA Power project, and MODIS weather data for precise predictions (Sun et al., 2019b). Google Earth Engine integrated with geospatial machine learning is another cloud-based tool that processes large-scale agriculture landscapes with higher accuracy.

Weed and pest control

 R-CCN (Region-based convolutional neural network) (W. C. Tin et al., 2024), and SVM (Support vector machine Classification model) available in PyTorch have been developed to detect and classify weed and pest species by extracting features like color, texture, and shape.

Irrigation management

In crop production, water shortage is becoming a bigger issue, thus effective irrigation management is crucial. Reinforcement learning (RL) models like DQNs develop irrigation systems, by receiving feedback from soil moisture sensors and optimizing irrigation schedules through trial and error (Chen et al., 2021b).

Conclusion 

AI in agriculture is going to be a game changer in the area of food production, natural resources management, and the struggle against climate change. While AI models continue to advance, we might expect even finer levels of farming detail to reduce environmental impact further and increase food security for the growing global population. In the future, AI technologies will refine precision farming with real-time decision-making through advanced analytics, AI-driven farms, autonomous machinery, robotics, IoT sensors, and blockchain integration with AI to enhance supply chain transparency from farm to table.

References

Chen, M., Cui, Y., Wang, X., Xie, H., Liu, F., Luo, T., Zheng, S., & Luo, Y. (2021). A reinforcement learning approach to irrigation decision-making for rice using weather forecasts. Agricultural Water Management, 250, 106838. https://doi.org/10.1016/j.agwat.2021.106838

Luo, Z., Yang, W., Yuan, Y., Gou, R., & Li, X. (2023). Semantic segmentation of agricultural images: A survey. Information Processing in Agriculture, 11(2), 172–186. https://doi.org/10.1016/j.inpa.2023.02.001

Sonawane, S., & Patil, N. N. (2024). Comparative performance analysis of YOLO object detection algorithms for weed detection in agriculture. Intelligent Decision Technologies, 1–13. https://doi.org/10.3233/idt-240978

Sun, J., Di, L., Sun, Z., Shen, Y., & Lai, Z. (2019). County-level soybean yield prediction using deep CNN-LSTM model. Sensors, 19(20), 4363. https://doi.org/10.3390/s19204363

W. C. Tin et al., “Exploring an Alternative Approach: RCNN -SVM for Weed Identification in Aerial Images Captured by UAS,” 2024 International Conference on Emerging Trends in Networks and Computer Communications (ETNCC), Windhoek, Namibia, 2024, pp. 362-369, https://doi.org/10.1109/ETNCC63262.2024.10767507