Building Digital Crop Signatures using AI, ML, and Remote Sensing

With climate change affecting agriculture more and more every year, it’s getting tougher to predict several crop factors such as yield volume, quality, harvest time, and more that significantly impact crop outcomes. With fraction of resources, as compared to traditional farm assessment methods, this is where digital agriculture comes in handy.

With modern technologies such as remote sensing and deep learning (ML, AI, and more,) modern agriculture has become easier to predict. In addition, digital agriculture technologies such as geospatial monitoring, weather forecasting, precision application of pesticides and other chemicals have made agriculture easy, predictable, and efficient.

Crop production forecasting, Identification of planting and harvesting dates

Building Digital Crop Signatures using AI, ML, and Remote Sensing

Farming is one of the most challenging professions in the world. Crops need meticulous care delivered promptly, lest they risk putrefaction resulting in heavy financial losses. Thus, farmers and agriculturists have been looking for ways to gain future insights into weather and other crop affecting factors to precisely plan their actions.

To forecast crop production and other insights, it is imperative to use the latest technologies such as remote sensing, which help farmers with allied information and current crop situation in one click. Whereas traditional crop assessment methods take months, remote sensing has revolutionized crop assessment by consuming a fraction of the time and resources and rendering highly accurate results.

Additional to crop forecasting, knowing precisely when to plant and harvest is also a key to successful farming. With technologies such as remote sensing, ML, and AI, farmers can easily estimate the planting and harvesting date by estimating the crop expanse, weather patterns, soil type, crop quality, and more.

Assessment of crop damage and crop progress, Crop condition assessment, and stress detection

Building Digital Crop Signatures using AI, ML, and Remote Sensing

Crop assessment is a rather cumbersome task if carried out manually. It can take up to several months to reach a dubious conclusion when it comes to accuracy, especially after a natural calamity or untimely climate change. However, with the help of remote sensing, ML, and AI, the data collected is highly beneficial when combined with other available information and can give good results.

Remote sensing can quickly identify if the crop is too dry or wet, affected by pests or any other infestations such as weed or fungus, and much more. In addition, the technology helps by taking pictures of a crop via satellite, which helps monitor and assess a crop.

Remote sensing is also instrumental when it comes to detecting damage and monitoring crop health. One of the most beneficial and essential factors of remote sensing for farmers is its quick turnaround time; it enables farmers to acquire data quickly and distribute it to crop experts for taking swift action in no time. The ability to capture images frequently, at the needed time, makes remote sensing technology no less than a boon.

Horticulture, Cropping Systems Analysis, Crop yield modelling, and estimation

Building Digital Crop Signatures using AI, ML, and Remote Sensing

Horticulture crops are instrumental in enhancing the quality and productivity of the land. To improve horticulture crop production, updated and accurate forecasting is needed for systematic decision making.

Farmers need to know which horticulture crops to plant and at precisely what time to achieve desired results. Thus, remote sensing can collect accurate data after analysing the flower growth patterns and deriving predictions.

Moreover, with the help of remote sensing, farmers can also estimate the yield volume of the crop by taking into account the extent of farmland and crop quality.

Identification of pests and disease infestation and more.

Using remote sensing technology to detect pest infestations through satellite imagery has been around since the 1920s; albeit the principle remains the same, the technology has come a long way. It is difficult for farmers to evaluate crops manually and check each scion for pest infestation or any other deficiencies.

Remote sensing enables farmers and experts to envisage pest infestations and take necessary actions beforehand. Furthermore, it also significantly helps in identifying the infestation type and gives the right solution to the farmers to get rid of the pests and diseases.

Building Digital Crop Signatures using AI, ML, and Remote Sensing

It is one thing to forecast crop conditions and outcomes, but it is another to assess current crop conditions and stress detection in the crops. With the help of remote sensing, farmers can get accurate real-time crop insights and suggestions, and solutions to improve crop condition, yield volume, quality and other crop factors. Similarly, remote sensing technology can also be used to identify crops that endure yield-impacting stress. This stress detection characteristic of remote sensing technology enables farmers to provide adequate nutrition and clean enough space to ensure crop grows stress-free.

SourceTrace's software solutions have been deployed across 37 countries and 4 continents already. We are on a mission to make agriculture and food systems more sustainable. Get in touch and we will extend our expertise and commitment to you.


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About SourceTrace

SourceTrace is a SaaS (Software As A Service) company that focuses on sustainable agriculture and empowerment of farmers.

SourceTrace's advanced technology platform DATAGREEN provides comprehensive solutions to manage all aspects of the agricultural value chain.

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