How can efficient and eco-friendly weed control in farmland be achieved?
Higher Education Press
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view moreCredit: Mohammad MEHDIZADEH1,2 , Duraid K. A. AL-TAEY3 , Anahita OMIDI4 , Aljanabi Hadi Yasir ABBOOD5 , Shavan ASKAR6 , Soxibjon TOPILDIYEV7 , Harikumar PALLATHADKA8 , Renas Rajab ASAAD9
In farmland, the “resource competition” between weeds and crops is unceasing. These seemingly ordinary plants not only compete with crops for water, light, and nutrients but can also carry pests and diseases, and even inhibit crop growth by releasing allelopathic substances. For a long time, manual weeding and the application of chemical herbicides have been the primary means for farmers to combat weeds——manual weeding is time-consuming and labor-intensive, often requiring several hours of work per acre, while chemical herbicides, though efficient, lead to soil pollution, increased weed resistance, and even threaten ecological safety due to overuse. How can we ensure effective weed control while reducing environmental burdens? This longstanding agricultural challenge is being quietly rewritten by a new technology: machine learning.
An international team from countries including Iran, Iraq, Uzbekistan, and India has co-authored a review paper published in the journal Frontiers of Agricultural Science and Engineering (DOI: 10.15302/J-FASE-2024564). The corresponding author is Dr. Mohammad MEHDIZADEH from University of Mohaghegh Ardabili. The article outlines the potential applications of machine learning technology in weed management and provides insights for addressing the aforementioned issues. In simple terms, machine learning acts like an “intelligent brain” for farmland——it can analyze vast amounts of agricultural data, automatically identify patterns, and make precise decisions, shifting weed control from a “broad net” approach to “precision strikes”.
One major pain point of traditional weeding is the inability to distinguish between crops and weeds: when spraying herbicides, both crops and weeds are often covered, wasting chemicals and potentially harming crops. Machine learning’s “keen eye” can solve this problem. The article points out that by training computers to recognize the image features of weeds (such as leaf shape, color, and texture), algorithms can quickly differentiate between various weed species and even accurately locate weeds within dense crop growth.
More critically, machine learning can “calculate” the optimal weeding strategy. In the past, farmers often relied on experience when applying herbicides, resulting in either excessive use leading to waste or insufficient application resulting in incomplete weed control. Now, algorithms can comprehensively analyze historical application data, weed growth patterns, soil moisture, temperature, and other factors to predict weed growth trends in different areas and dynamically adjust the quantity and timing of herbicide application. This “on-demand application” model not only reduces farmers’ cultivation costs but also significantly alleviates the chemical burden on soil and water sources.
Additionally, machine learning can enable “real-time monitoring” of weeds. By continuously collecting farmland data through drones, sensors, and other devices, algorithms can track the spread of weeds in real-time. If an area shows a sudden increase in weed density, farmers will receive immediate warnings to prevent weeds from “growing rampantly”. This dynamic monitoring capability transforms weed control from “passive response” to “active defense”, particularly effective against invasive and rapidly spreading harmful weeds.
However, this technology is still in the research and validation stage. To be practically applied, challenges such as comprehensive data collection and the adaptability of algorithms in complex agricultural environments need to be addressed.
Journal
Frontiers of Agricultural Science and Engineering
Method of Research
Experimental study
Subject of Research
Not applicable
Article Title
Advancing agriculture with machine learning: a new frontier in weed management
How to achieve intelligent and precise pesticide application in sustainable agriculture?
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view moreCredit: Rohit ANAND1 , Roaf Ahmad PARRAY1 , Indra MANI2 , Tapan Kumar KHURA1 , Harilal KUSHWAHA3 , Brij Bihari SHARMA4 , Susheel SARKAR5 , Samarth GODARA6 , Shideh MOJERLOU7 , Hasan MIRZAKHANINAFCHI8
In traditional agriculture, crop disease control often relies on the extensive spraying of chemical pesticides. However, this “one-size-fits-all” approach not only wastes resources but also risks environmental pollution and threats to human health. With global climate change and increasing pathogen resistance, reducing pesticide dependence through smarter, more precise methods while ensuring crop yields has become a major challenge for sustainable agriculture. Is there a technology that can rapidly identify diseases and apply pesticides precisely, achieving “targeted treatment”?
Dr. Roaf Ahmad Parray from ICAR-indian agricultural research institute (ICAR-IARI) and his colleagues provide an answer in a study published in Frontiers of Agricultural Science and Engineering (DOI: 10.15302/J-FASE-2024572). In this research, an international team of scientists from India, Denmark, and the United States developed an innovative technology integrating spectral sensors, machine learning models, and an intelligent spraying system, successfully applying it to control black rot disease in cauliflower. This technology, comprising three core components—non-destructive detection, intelligent decision-making, and targeted pesticide application—significantly reduces pesticide use and offers new insights for green agriculture.
Traditional disease detection relies on manual observation, which is time-consuming, labor-intensive, and error-prone. The researchers adopted a novel approach, using spectral sensors to capture reflected light signals from plant leaves. Healthy leaves and those infected with black rot exhibit distinct differences in light reflection, particularly in visible and near-infrared bands. By analyzing these “spectral fingerprints”, the sensors rapidly assess plant health. However, how can disease-infected areas be accurately distinguished from healthy ones within vast spectral data? To address this, the researchers introduced machine learning algorithms, comparing decision trees and support vector machines (SVM). Results showed that the SVM model outperformed the decision tree, achieving a test accuracy of 96.7% versus 89.9%. This high-precision model was embedded into the intelligent spraying system’s control unit, serving as the “brain” to determine pesticide application.
Traditional spraying equipment often covers the entire farmland, while the intelligent spraying system in this study is as precise as a “surgeon”. When the sensor detects a diseased area, the system sprays pesticides only on the diseased part through a micro pump and a special nozzle; if a plant is identified as healthy, the spraying function is automatically turned off. Field trials have shown that this system successfully identified and treated 75% of the diseased plants in a 100-square-meter cauliflower test field, and avoided mis-spraying 87.5% of the healthy plants. Compared with traditional backpack sprayers, the intelligent system reduced the amount of pesticides used by 72.5% and saved 21% of the spraying time.
At the same time, the equipment uses low-cost materials and open-source hardware to ensure that small-scale farmers can afford it. The distance between the sensor and the spraying unit of the equipment has been optimized and can work stably within the range of 25–45 centimeters, adapting to farmlands with different planting densities. In addition, the system only needs to regularly calibrate the whiteboard reference value, and it is simple to operate, making it suitable for farmers lacking professional skills.
Currently, the researchers have completed the preliminary verification in the experimental field of the Indian Agricultural Research Institute. Future research will explore the applicability of this technology to other crops (such as tomatoes, potatoes) and diseases (such as downy mildew), and expand its application by combining with drone technology.
Journal
Frontiers of Agricultural Science and Engineering
Method of Research
Experimental study
Subject of Research
Not applicable
Article Title
A multimodal approach for enhanced disease management in cauliflower crops: integration of spectral sensors, machine learning models and targeted spraying technology
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