These are recent papers about precision agriculture with machine learning (ML). Together, they show that responsible use of AI and ML can benefit the environment, help mitigate the effects of climate change, and promote sustainable farming, all while increasing efficiency and output. Though AI and ML are not panaceas, if we avoid using technology that promotes wanton use of pesticides and water waste, environmental goals and production goals can be mutually beneficial:

• Implementation of artificial intelligence in agriculture for optimisation of irrigation and application of pesticides and herbicides by T. Talaviya et. al - Employs artificially intelligent robots to apply chemicals more accurately
• Weed and crop discrimination using image analysis and artificial intelligence methods by MJ Aitkenhead et. al - Points to the need to reduce agricultural dependence on herbicides, which could lead to cost savings in the hundreds of millions of dollars and significantly reduce chemical contamination into the water and food supplies plus the environment in general
• Strategies for discovering resistance-breaking, safe and sustainable commercial herbicides with novel modes of action and chemotypes by S. Hachisu - Acknowledges that herbicides create problems of their own, such as facilitating the growth of herbicide-resistant strains of weeds that render the herbicides ineffective
• Glufosinate-ammonium: a review of the current state of knowledge by HK Takano et. al - Describes how controversy has surrounded the herbicide glusofinate (Monsanto's Roundup), for decades, yet it remains a ubiquitously popular product
• Agricultural intensification and damages to human health in relation to agrochemicals: Application of artificial intelligence by E. Elahi et. al - Proposes an ANN-based method to determine the best ways to distribute resources, like water and fertilizer, and to calculate the volume of resources that could be saved in order to promote sustainable development
• A new suggestion for an irrigation schedule with an artificial neural network by N. Karasekreter et. al - Applies ANN models to the problem of efficient nighttime irrigation, where they save resources and prevent evaporation by irrigating after sunset, demonstrating a savings in water and electricity
• A decision support system for managing irrigation in agriculture by N. Navarro-Hellin et. al - Proposes a statistical and ML-based Smart Irrigation Decision Support System (SIDSS) to predict necessary levels of irrigation for various crops, demonstrating a savings in artificial irrigation
• Autonomous crop irrigation system using artificial intelligence by S. Choudhary et. al - Proposes a fully automated and autonomous ML-based irrigation for weather prediction and predicting amounts of water required, reporting better than 80% accuracy
• Prediction of applied irrigation depths at farm level using artificial intelligence techniques by RG Parea et. al - Provides a strong case that AI can help balance the tradeoff between productivity and more efficient water use, applies genetic algorithms