How machine learning can be used in agriculture

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Machine learning in agriculture allows for a more accurate disease diagnosis while preserving energy and preventing false data. Farmers can upload field images taken by satellites, UAVs

Unmanned aerial vehicle

An unmanned aerial vehicle (UAV), commonly known as a drone, as an unmanned aircraft system (UAS), and also referred by several other names, is an aircraft without a human pilot aboard.

, land based rovers, smartphones, and tools like the Climate FieldView™ platform, which can identify potential issues on the farm and recommend a management plan.

In pre-harvesting machine learning is used to capture the parameters of soil, seeds quality, fertilizer application, pruning, genetic and environmental conditions and irrigation. Focusing on each component it is important to minimize the overall losses in production.

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Answer

What machine learning techniques are used in agriculture?

Mostly, machine learning techniques are used in crop management processes, following with farming conditions management and livestock management. The literature review shows that the most popular models in agriculture are Artificial and Deep Neural Networks (ANNs and DL) and Support Vector Machines (SVMs).

Is machine learning the future of knowledge-based farming?

A subset of artificial intelligence, namely machine learning, has a considerable potential to handle numerous challenges in the establishment of knowledge-based farming systems.

How can machine learning be used to classify plants?

While the traditional human approach for plant classification would be to compare color and shape of leaves, machine learning can provide more accurate and faster results analyzing the leaf vein morphology which carries more information about the leaf properties.

What is machine learning and how does it work?

Machine learning can be defined as the scientific method that will allow machines the ability to learn without programming the devices. Machine learning is used in various scientific areas such as Bioinformatics, Biochemistry, Medicines, Meteorology, Economic Sciences, Robotics, Food Security and Climatology.

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How can AI be used in agriculture?

AI systems are helping to improve the overall harvest quality and accuracy – known as precision agriculture. AI technology helps in detecting disease in plants, pests and poor nutrition of farms. AI sensors can detect and target weeds and then decide which herbicide to apply within the region.


Which algorithm is used in agriculture?

Overall, the LDA algorithm was the best tool, and SVM was the worst algorithm in maize yield prediction. Evaluating the performance of different ML algorithms using different criteria is critical in order to get a more robust assessment of the tools before their application in the agriculture sector.


How is Deep learning used in agriculture?

In smart agriculture, deep learning algorithms are used to monitor the temperature and water level of the crops. In addition to farmers can observe their fields from anywhere in the world. This AI based smart agriculture is really efficient [4]. Water is requiring for proper growth of crop.


What are the advantages of AI in agriculture?

AI can provide farmers with real-time insights from their fields, allowing them to identify areas that need irrigation, fertilization, or pesticide treatment. Also, innovative farming practices like vertical agriculture may help increase food production while minimizing the use of resources.


How do robots help agriculture?

The main area of application of robots in agriculture today is at the harvesting stage. Emerging applications of robots or drones in agriculture include weed control, cloud seeding, planting seeds, harvesting, environmental monitoring and soil analysis.


How Python is useful in agriculture?

Python is also being used for developing the IoT devices. AI is assisting IoT in enabling real-time data analytics to help make informed decisions to farmers. Precision agriculture or smart Agriculture relies on emerging technologies such as AI, ML and data analytics to revolutionize farming practices.


How AI can help Indian farmers?

Artificial Intelligence (AI) is being used by the agriculture industry to help produce healthier crops, control pests, monitor soil and growing conditions, organise data for farmers, reduce effort, and improve a wide range of agriculture-related operations along the food supply chain.


How can IOT help in agriculture?

On farms, IOT allows devices across a farm to measure all kinds of data remotely and provide this information to the farmer in real time. IOT devices can gather information like soil moisture, chemical application, dam levels and livestock health – as well as monitor fences vehicles and weather.


What is precision agriculture technology?

Precision agriculture is a farming management concept based on observing, measuring and responding to variability in crops. These variabilities contain many components that can be difficult to compute and as a result technology has advanced to off-set these difficulties.


What are the challenges of AI in agriculture?

“The major challenge in the broad adoption of AI in agriculture is the lack of simple solutions that seamlessly incorporate and embed AI in agriculture. The majority of farmers don’t have the time or digital skills experience to explore the AI solutions space by themselves.


Which of the following is an example of using AI to increase agriculture productivity?

Harvest CROO Robotics – Crop Harvesting Harvest CROO Robotics has developed a robot to help strawberry farmers pick and pack their crops.


What are the disadvantages of AI in agriculture?

DisadvantagesIt costs a lot of money to make or buy robots.They need maintenance to keep them running.The farmers can lose their jobs.The robots can change the culture / the emotional appeal of agriculture.Energy cost and maintenance.The high cost of research and development.Lack of access to poor farmers.


Why is machine learning important in agriculture?

Machine learning in agriculture allows for much higher precision, enabling farmers to treat plants and animals almost individually.


How does machine learning help farmers?

Machine learning in agriculture allows for much higher precision, enabling farmers to treat plants and animals almost individually, which in turn significantly increases the effectiveness of farmers’ decisions. Machine learning in agriculture allows for much higher precision, enabling farmers to treat plants and animals almost individually.


What is ML in livestock?

ML-driven innovation in livestock control allows farmers to increase the outputs while requiring fewer resources. For example, New Zealand-based company Vence utilizes ML in combination with connected sensors to provide farmers with virtual fencing. This method implies improved control of rotational and strip grazing to substantially increase the yield.


What is agrochemical production?

Agrochemical Production. Decades ago, agrochemical products entered the mass market and revolutionized agriculture. Various chemical products including pesticides, antibiotics, and insecticides had finally made it possible for farmers to combat the havoc wreaked by unwanted insects and bacteria.


Why is image analysis important for farmers?

Image analysis significantly raises the accuracy and speed of species identification, which also saves farmers’ time and resources.


Why are weeds a problem in agriculture?

Weeds are the next most serious threat to crop farming since they grow very quickly, compete with the crop, cause various plant diseases, and ultimately lower yield and farmers’ profits. Currently, herbicides are the most popular solution, but at the same time they raise many environmental and economic concerns. Moreover, as time goes by, weeds are learning to adapt to chemicals and resist them, which makes widespread usage of herbicides even more questionable.


Is agriculture a data centric industry?

Despite agriculture being a very data-centric industry, there are significant challenges on the way to the global adoption of machine learning. The first layer of complexity lies in the variance of conditions based on location. For example, a fertilizer program applied in Australia would most likely be irrelevant in the United States due to significant differences in humidity, soil types, daylight hours, temperature, and many other factors. Currently, many promising startups’ solutions can be effective only for specific crops in specific regions.


How does machine learning work in agriculture?

It begins with a seed being planted in the soil — from the soil preparation, seeds breeding and water feed measurement — and it ends when robots pick up the harvest determining the ripeness with the help of computer vision. Let’s discover how agriculture can benefit …


How does machine learning help farmers?

Similar to crop management, machine learning provides accurate prediction and estimation of farming parameters to optimize the economic efficiency of livestock production systems, such as cattle and eggs production. For example, weight predicting systems can estimate the future weights 150 days prior to the slaughter day, allowing farmers to modify diets and conditions respectively.


How does water management affect agriculture?

Water management in agriculture impacts hydrological, climatological, and agronomical balance. So far, the most developed ML-based applications are connected with estimation of daily, weekly, or monthly evapotranspiration allowing for a more effective use of irrigation systems and prediction of daily dew point temperature, which helps identify expected weather phenomena and estimate evapotranspiration and evaporation.


What are chatbots for farmers?

This is an application that can be called a bonus: imagine a farmer sitting late at night and trying to figure out the next steps in management of his crops. Whether he could sell more now to a local producer or head to a regional fair? He needs someone to talk through the various options to take a final decision. To help him, companies are now working on development specialized chatbots that would be able to converse with farmers and provide them with valuable facts and analytics. Farmers’ chatbots are expected to be even smarter than consumer-oriented Alexa and similar helpers, since they would be able not only to give figures, but analyze them and consult farmers on tough matters.


How do behavior classifiers help animals?

Animals behavior classifiers can connect their chewing signals to the need in diet changes and by their movement patterns, including standing, moving, feeding, and drinking, they can tell the amount of stress the animal is exposed to and predict its susceptibility to diseases, weight gain and production.


What is the most widely used practice in pest and disease control?

Both in open-air and greenhouse conditions, the most widely used practice in pest and disease control is to uniformly spray pesticides over the cropping area . To be effective, this approach requires significant amounts of pesticides which results in a high financial and significant environmental cost.


How can machine learning help plant classification?

While the traditional human approach for plant classification would be to compare color and shape of leaves, machine learning can provide more accurate and faster results analyzing the leaf vein morphology which carries more information about the leaf properties.


What are the applications of ML in agriculture?

The majority of these review studies have been dedicated to crop disease detection [13,14,15,16], weed detection [17,18], yield prediction [19,20], crop recognition [21,22], water management [23,24], animal welfare [25,26], and livestock production [27,28]. Furthermore, other studies were concerned with the implementation of ML methods regarding the main grain crops by investigating different aspects including quality and disease detection [29]. Finally, focus has been paid on big data analysis using ML, aiming at finding out real-life problems that originated from smart farming [30], or dealing with methods to analyze hyperspectral and multispectral data [31].


What are the problems with ML in agriculture?

According to [23,24,28,32], the main problems are associated with the implementation of sensors on farms for numerous reasons, including high costs of ICT, traditional practices, and lack of information. In addition, the majority of the available datasets do not reflect realistic cases, since they are normally generated by a few people getting images or specimens in a short time period and from a limited area [15,21,22,23]. Consequently, more practical datasets coming from fields are required [18,20]. Moreover, the need for more efficient ML algorithms and scalable computational architectures has been pointed out, which can lead to rapid information processing [18,22,23,31]. The challenging background, when it comes to obtaining images, video, or audio recordings, has also been mentioned owing to changes in lighting [16,29], blind spots of cameras, environmental noise, and simultaneous vocalizations [25]. Another important open problem is that the vast majority of farmers are non-experts in ML and, thus, they cannot fully comprehend the underlying patterns obtained by ML algorithms. For this reason, more user-friendly systems should be developed. In particular, simple systems, being easy to understand and operate, would be valuable, as for example a visualization tool with a user-friendly interface for the correct presentation and manipulation of data [25,30,31]. Taking into account that farmers are getting more and more familiar with smartphones, specific smartphone applications have been proposed as a possible solution to address the above challenge [15,16,21]. Last but not least, the development of efficient ML techniques by incorporating expert knowledge from different stakeholders should be fostered, particularly regarding computing science, agriculture, and the private sector, as a means of designing realistic solutions [19,22,24,33]. As stated in [12], currently, all of the efforts pertain to individual solutions, which are not always connected with the process of decision-making, as seen for example in other domains.


What is crop management?

The crop management category involves versatile aspects that originated from the combination of farming techniques in the direction of managing the biological, chemical and physical crop environment with the aim of reaching both quantitative and qualitative targets [52]. Using advanced approaches to manage crops, such as yield prediction, disease detection, weed detection, crop recognition, and crop quality, contributes to the increase of productivity and, consequently, the financial income. The above aspects constitute key goals of precision agriculture.


What is reinforcement learning?

Reinforcement learning: Decisions are made towards finding out actions that can lead to the more positive outcome, while it is solely determined by trial and error method and delayed outcome.


What is supervised learning?

Supervised learning: The input and output are known and the machine tries to find the optimal way to reach an output given an input;


How does crop quality affect the market?

Crop quality is very consequential for the market and, in general, is related to soil and climate conditions, cultivation practices and crop characteristics, to name a few. High quality agricultural products are typically sold at better prices, hence, offering larger earnings to farmers. For instance, as regards fruit quality, flesh firmness, soluble solids content, and skin color are among the most ordinary maturity indices utilized for harvesting [64]. The timing of harvesting greatly affects the quality characteristics of the harvested products in both high value crops (tree crops, grapes, vegetables, herbs, etc.) and arable crops. Therefore, developing decision support systems can aid farmers in taking appropriate management decisions for increased quality of production. For example, selective harvesting is a management practice that may considerably increase quality. Furthermore, crop quality is closely linked with food waste, an additional challenge that modern agriculture has to cope with, since if the crop deviates from the desired shape, color, or size, it may be thrown away. Similarly to the above sub-section, ML algorithms combined with imaging technologies can provide encouraging results.


How are crops recognized?

Plant species can be recognized and classified via analysis of various organs, including leaves, stems, fruits, flowers, roots, and seeds [61,62]. Using leaf-based plant recognition seems to be the most common approach by examining specific leaf’s characteristics like color, shape, and texture [63]. With the broader use of satellites and aerial vehicles as means of sensing crop properties, crop classification through remote sensing has become particularly popular. As in the above sub-categories, the advancement on computer software and image processing devices combined with ML has led to the automatic recognition and classification of crops.


Abstract

Through Big Data and High-Performance Computing, Machine Learning has evolved, creating new possibilities for data-intensive research in the area of agriculture. For precision analysis, numerous computing methods, such as and neural networks, k-means etc. have been used in the past.


References (22)

ResearchGate has not been able to resolve any citations for this publication.


What are the benefits of AI and machine learning?

Thanks to artificial intelligence (AI) and machine learning (ML), farmers can now access advanced data and analytics tools that will foster better farming, improve efficiencies, reduce waste in biofuel and food production while at the same time minimizing the negative impact on the environment.


What is ML in agriculture?

ML is a secure way of maximizing agricultural productivity at the same time minimizing the impact on the environment. Through data collected from crops, farmers are able to understand crops, their genes, and potential diseases better. This data will help farmers make quick, informed, and results-driven decisions. Lastly, as the world population grows, ML is the solution to addressing the issue of food security and scarcity to meet the rising demand in the global food system.


Why do farmers need to classify crop quality features?

For product prices to go up and reduce waste, farmers need to detect and classify crop quality features accurately. Machines can use data to detect and reveal new characteristics that contribute significantly to the overall crop quality.


What is ML in farming?

Just like crop management, ML gives accurate prediction and estimation of farming parameters useful in optimizing the production of milk, meat, eggs, and other dairy products. For instance, a weight predicting system can estimate the future weight of a bull 5 months prior to the slaughter day, giving the farmer the opportunity to adjust the bull’s diet and living conditions as needed.


How does water management affect agriculture?

Water management in agriculture has a significant impact on the agronomical, climatological, and hydrological balance. ML-based applications are capable of estimating daily, weekly, or monthly evapotranspiration, eventually leading to the effective utilization of irrigation systems. Moreover, the accurate prediction of daily dew point temperature assists in the identification of expected weather phenomena, and also the estimation of evapotranspiration and evaporation.


What is the process of selecting a species?

Species selection is a long process that involves looking for specific genes that determine how effective water and nutrients usage is, how adaptable to climate change they are, and how well they can resist diseases.


How to classify plants using ML?

The traditional method for classifying plants is comparing leaf color and shape. However, ML introduces a more accurate and quicker way of classification by analyzing leaf vein morphology that has more information about the leaf properties, sometimes even using aerial imagery.

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Use Precision Ag Insights to Keep You Up to Date

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