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.
DOI: 10.15302/J-FASE-2024564