Farming & Agriculture

Crop Monitoring

The yield and quality of important crops such as rice and wheat determine the stability of food security. Traditionally, crop growth monitoring mainly relies on subjective human judgment and is not timely or accurate. Computer Vision applications allow to continuously and non-destructively monitor plant growth and the response to nutrient requirements. Compared with manual operations, the real-time monitoring of crop growth by applying computer vision technology can detect the subtle changes in crops due to malnutrition much earlier and can provide a reliable and accurate basis for timely regulation. In addition, computer vision applications can be used to measure plant growth indicators or determine the growth stage.

Flowering Detection

The heading date of wheat is one of the most important parameters for wheat crops. An automatic computer vision observation system can be used to determine the wheat heading period.

Computer vision technology has the advantages of low cost, a small error, high efficiency, and good robustness and can be dynamically and continuously analyzed.

Plantation monitoring

In intelligent agriculture, image processing with drone images can be used to monitor palm oil plantations remotely. With geospatial orthophotos, it is possible to identify which part of the plantation land is fertile for planted crops. It is also possible to identify areas less fertile in terms of growth and parts of plantation fields that were not growing at all.

Insect Detection

Rapid and accurate recognition and counting of flying insects are of great importance, especially for pest control. However, traditional manual identification and counting of flying insects are inefficient and labor-intensive. Vision-based systems allow the counting and recognizing of flying insects (based on You Only Look Once (YOLO) object detection and classification).

Plant Disease Detection

Automatic and accurate estimation of disease severity is essential for food security, disease management, and yield loss prediction. The deep learning method avoids labor-intensive feature engineering and threshold-based image segmentation.

Automatic image-based plant disease severity estimation using Deep convolutional neural network (CNN) applications were developed, for example, to identify apple black rot.

Automatic Weeding

Weeds are considered to be harmful plants in agronomy because they compete with crops to obtain the water, minerals, and other nutrients in the soil. Spraying pesticides only in the exact locations of weeds greatly reduces the risk of contaminating crops, humans, animals, and water resources.

The intelligent detection and removal of weeds are critical to the development of agriculture. A neural network-based computer vision system can be used to identify potato plants and three different weeds for on-site specific spraying.

Automatic Harvesting

In traditional agriculture, there is a reliance on mechanical operations, with manual harvesting as the mainstay, which results in high costs and low efficiency. However, in recent years, with the continuous application of computer vision technology, high-end intelligent agricultural harvesting machines, such as harvesting machinery and picking robots based on computer vision technology, have emerged in agricultural production, which has been a new step in the automatic harvesting of crops.

The main focus of harvesting operations is to ensure product quality during harvesting to maximize the market value. Computer Vision powered applications include picking cucumbers automatically in a greenhouse environment or the automatic identification of cherries in a natural environment.

Agricultural Product Quality Testing

The quality of agricultural products is one of the important factors affecting market prices and customer satisfaction. Compared to manual inspections, Computer Vision provides a way to perform external quality checks.

AI vision systems are able to achieve high degrees of flexibility and repeatability at a relatively low cost and with high precision. For example, systems based on machine vision and computer vision are used for rapid testing of sweet lemon damage or non-destructive quality evaluation of potatoes.

Irrigation Management

Soil management based on using technology to enhance soil productivity through cultivation, fertilization, or irrigation has a notable impact on modern agricultural production. By obtaining useful information about the growth of horticultural crops through images, the soil water balance can be accurately estimated to achieve accurate irrigation planning.

Computer vision applications provide valuable information about the irrigation management water balance. A vision-based system can process multi-spectral images taken by unmanned aerial vehicles (UAVs) and obtain the vegetation index (VI) to provide decision support for irrigation management.

UAV Farmland Monitoring

Real-time farmland information and an accurate understanding of that information play a basic role in precision agriculture. Over recent years, UAV, as a rapidly advancing technology, has allowed the acquisition of agricultural information that has a high resolution, low cost, and fast solutions.

In addition, UAV platforms equipped with image sensors provide detailed information on agricultural economics and crop conditions (for example, continuous crop monitoring). As a result, UAV remote sensing has contributed to an increase in agricultural production with a decrease in agricultural costs.

Yield Assessment

Through the application of computer vision technology, the functions of soil management, maturity detection, and yield estimation for farms have been realized. Moreover, the existing technology can be well applied to methods such as spectral analysis and deep learning.

Most of these methods have the advantages of high precision, low cost, good portability, good integration, and scalability and can provide reliable support for management decision making. An example is the estimation of citrus crop yield via fruit detection and counting using computer vision.

Also, the yield from sugarcane fields can be predicted by processing images obtained using UAVs.

Animal Monitoring

Animals can be monitored using novel techniques that have been trained to detect the type of animal and its actions. There is much use for animal monitoring in farming, where livestock can be monitored remotely for disease detection, changes in behavior, or giving birth.

Additionally, agriculture and wildlife scientists can view wild animals safely at a distance.

Farm Automation

Technologies such as harvest, seeding, and weeding robots, autonomous tractors, and drones to monitor farm conditions and apply fertilizers can maximize productivity with labor shortages. Agriculture can also be more profitable when the ecological footprint of farming is minimized.


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