Impact of Artificial Intelligence on the Agriculture and Seed Industry: In recent times AI (Artificial Intelligence) has been used in the agriculture and seed industry sector aiming to enhance productivity, efficiency, and sustainability. Let’s discuss some conditions in which AI is used in the agricultural sector.
Impact of Artificial Intelligence on the Agriculture and Seed Industry
A. Precision Agriculture:
Precision maintenance of crop conditions, optimizing seed planting, irrigation, and fertigation for crop-improved yields.
- Smart Seed Planting: AI analyzes the data about land and gives a blueprint for each crop about sowing pattern, seed rate, and others.
- Intelligent Irrigation: AI studies soil moisture content and weather forecasts, determining the ideal watering condition of soil.
- Fertigation: Al analyzes the accurate needs of each crop and provides customized fertilization plans.
B. Crop Monitoring and Management
- Smart Surveillance for Crops: AI smart surveillance tools, like drones and sensors, keep a close eye on fields. It helps in the early detection of pests, disease, and nutrient deficiencies at an early stage of the crop.
- Optimal Resource Utilization: AI optimizes the use of resources like irrigation, fertilizer, and growth regulators.
C. Predictive Analytics
- Weather: AI analyzes weather data, current field conditions, and global climate patterns and farmers can make plans for their activities, from farm planting to harvesting,
- Crop Disease: By analyzing abiotic factors like temperature, humidity, and soil conditions, AI predicts the disease. Early identification of disease helps farmers prevent the spread of diseases.
- Yield: By collecting information such as soil health, weather prediction, and crop history. AI provides an estimate of the potential yield.
- Financial guide: AI can predict market trends and help farmers make decisions about when to sell the harvested products.
D. AI in seed quality testing
Steps to set up a germination analysis
- Step I: Training (image acquisition of training samples)
- Step II: Labeling (Normal, abnormal, root and shoot length)
- Step III: Development of a machine learning model
- Step IV: Applying the fully trained model to analyze the real samples
- In the initial ad, the data is collected from visual inspection of seed quality parameters like seedling assessment (including normal and abnormal seedlings), 1st and final count, and other information into the computer. Additionally, upload the normal, abnormal, and disease-infected images to the computer.
- Check the germination rate by evaluating seedlings on germination paper. With the use of AI software to inspect the seedlings and assess the germination rate.
- After image processing, they can recognize normal and abnormal seedlings, shoot length, root length, fungal-infected seedlings, etc.
- In the image all shoots are marked in purple, roots are marked in brown, root hair is marked in yellow, and the background is marked in blue. Later we can get into numbers as we see in the chart.
Image source: LemnaTec
- Other information also gets like root length, shoot length, seed count, damages or infection seedlings.
- Seed count: AI algorithm that counts how many seeds are present and they also classify seeds or are there other crop seeds. And counting has to be done instantly. Measure all visible objects like the length, width, and color of the seed.
- Seed purity: Also classified the seeds of different crops, seeds of different varieties, weed seeds, and harvested residuals.
- Seed processing and grading: AI helps in the accurate sorting and grading of seeds based on size, shape, and quality. It helps in uniformity in plant population.
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The Influence of Seed Quality on Farmers
- Good germination rate and vigor: Farmer fields should filled up with very uniform germinated seeds as it helps to get a good yield. If the germination rate is too low there are gaps so there will be less yield and there is also space for unwanted plants.
- Good seed purity: The physical purity of the seed should be pure it should be free from other crop seeds, and weed seeds.
- Seed vigor: the vigor of the seed is not good, and it impacts the uniformity of the seeds as the results differ in the size of the plants.
E. Farm Robotics
Al monitoring weather conditions, crop health, and crop conditions, and these advanced robotic systems use AI to identify ripe fruits, and unripe fruits and harvest them without causing damage. These robots have cameras, sensors, and machine-learning algorithms that help in the identification of ripe and unripe fruits.
Image source: orchardtech
F. Supply Chain Optimization
AI is designing the supply chain, used advanced strategies that enhance efficiency and reduce waste. AI is making a revolution in the production, and distribution, of agricultural products.
G. Crop Breeding and Genetics
AI studies the genetic analysis of genomic data with machine learning algorithms, it can identify patterns and correlations, resulting in rapid trait predictions. This accelerates the breeding process.
H. Remote sensing
Through satellite imagery analysis, AI monitors crop conditions, and crop health and manages pests and diseases, irrigation management, predict yields, and detects weeds for precision management.
Advantages
- Fast processing: The Al algorithm system does seed counting and seedling evaluation is done in seconds, but in the traditional method it takes several minutes.
- Consistent Performance: Such an imaging system and the computer do not get tired.
- Elimination of Bias: Al-based software is not dependent on human impressions so it is not an inter-operator or bias that comes when two persons are doing the same job.
- Real-Time Monitoring: AI monitoring in crop conditions, that not only watches the field but also provides valuable information and makes farming more efficient, and sustainable.
