A vision of precision agriculture: Balance between agricultural sustainability and environmental stewardship

Nath, Soumitra

doi:10.1002/agj2.21405

Cited by 13 publications

(6 citation statements)

References 110 publications

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“…Many studies considered the use of sensors (IoT) to reduce the costs of agricultural production [ 14 , 15 , 97 , 99 , 128 , 135 , 144 , 146 , 149 , 150 , 155 , 162 , 164 ].…”

Section: Discussionmentioning

confidence: 99%

“…Using cutting-edge technology like IoT, GPS, GIS, sensors, drones, and machine learning has made it possible to complement this. This technology allows farmers to cultivate their land more precisely and efficiently [ 99 ].…”

Section: Discussionmentioning

confidence: 99%

“…PA boosts olive sustainability and potato profitability and sustainability. Nath [ 99 ] envisioned sustainable intensification and examined PA’s role. PA practices, such as precision irrigation, fertilizers, pest and disease control, and animal farming, are highlighted in this review.…”

Section: Discussionmentioning

confidence: 99%

“…Before this review paper discusses the above topic of concern in Section 5 , the basic understanding of the agroecosystem [ 165 , 166 , 167 , 168 ], I4 for PA [ 90 , 91 , 92 , 93 , 94 , 95 , 96 , 97 , 98 , 99 , 100 , 101 , 102 , 103 , 104 , 105 , 106 , 107 , 108 , 109 , 110 , 111 , 112 , 113 , 114 , 115 , 116 , 117 , 118 , 119 , 120 , 121 , 122 , 123 , 124 , 125 , 126 , 127 , 128 , 129 , 130 , 131 , 132 , 133 , 134 , 135 , 136 , 137 , 138 , 139 , 140 , 141 ...…”

Section: Introductionmentioning

confidence: 99%

“…However, not all publications mention the use of I4 directly. However, the elements of I4 are proposed indirectly and are already implemented in PA’s sustainability [ 90 , 91 , 92 , 93 , 94 , 95 , 96 , 97 , 98 , 99 , 100 , 101 , 102 , 103 , 104 , 105 , 106 , 107 , 108 , 109 , 110 , 111 , 112 , 113 , 114 , 115 , 116 , 117 , 118 , 119 , 120 , 121 , 122 , 123 , 124 , 125 , 126 , 127 , 128 , 129 , 130 , 131 , 132 , 133 , 134 , 135 , 136 , 137 , 138 , 139 , 140 , 141 , 142 , 143 , ...…”

Section: Introductionmentioning

confidence: 99%

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A Conceptual Model Relationship between Industry 4.0—Food-Agriculture Nexus and Agroecosystem: A Literature Review and Knowledge Gaps

Yap,

Al-Mutairi

2024

Foods

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With the expected colonization of human daily life by artificial intelligence, including in industry productivity, the deployment of Industry 4.0 (I4) in the food agriculture industry (FAI) is expected to revolutionize and galvanize food production to increase the efficiency of the industry’s production and to match, in tandem, a country’s gross domestic productivity. Based on a literature review, there have been almost no direct relationships between the I4—Food-Agriculture (I4FA) Nexus and the agroecosystem. This study aimed to evaluate the state-of-the-art relationships between the I4FA Nexus and the agroecosystem and to discuss the challenges in the sustainable FAI that can be assisted by the I4 technologies. This objective was fulfilled by (a) reviewing all the relevant publications and (b) drawing a conceptual relationship between the I4FA Nexus and the agroecosystem, in which the I4FA Nexus is categorized into socio-economic and environmental (SEE) perspectives. Four points are highlighted in the present review. First, I4 technology is projected to grow in the agricultural and food sectors today and in the future. Second, food agriculture output may benefit from I4 by considering the SEE benefits. Third, implementing I4 is a challenging journey for the sustainable FAI, especially for the small to medium enterprises (SMEs). Fourth, environmental, social, and governance (ESG) principles can help to manage I4’s implementation in agriculture and food. The advantages of I4 deployment include (a) social benefits like increased occupational safety, workers’ health, and food quality, security, and safety; (b) economic benefits, like using sensors to reduce agricultural food production costs, and the food supply chain; and (c) environmental benefits like reducing chemical leaching and fertilizer use. However, more studies are needed to address social adaptability, trust, privacy, and economic income uncertainty, especially in SMEs or in businesses or nations with lower resources; this will require time for adaptation to make the transition away from human ecology. For agriculture to be ESG-sustainable, the deployment of I4FA could be an answer with the support of an open-minded dialogue platform with ESG-minded leaders to complement sustainable agroecosystems on a global scale.

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“…Many studies considered the use of sensors (IoT) to reduce the costs of agricultural production [ 14 , 15 , 97 , 99 , 128 , 135 , 144 , 146 , 149 , 150 , 155 , 162 , 164 ].…”

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

A Conceptual Model Relationship between Industry 4.0—Food-Agriculture Nexus and Agroecosystem: A Literature Review and Knowledge Gaps

Yap,

Al-Mutairi

2024

Foods

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Will artificial intelligence and machine learning change agriculture: A special issue

Clay,

Brugler,

Joshi

2024

Agronomy Journal

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In agriculture, important unanswered questions about machine learning and artificial intelligence (ML/AI) include will ML/AI change how food is produced and will ML algorithms replace or partially replace farmers in the decision process. As ML/AI technologies become more accurate, they have the potential to improve profitability while reducing the impact of agriculture on the environment. However, despite these benefits, there are many adoption barriers including cost, and that farmers may be reluctant to adopt a decision tool they do not understand. The goal of this special issue is to discuss cutting‐edge research on the use of ML/AI technologies in agriculture, barriers to the adoption of these technologies, and how technologies can affect our current workforce. The papers are separated into three sections: Machine Learning within Crops, Pasture, and Irrigation; Machine Learning in Predicting Crop Disease; and Society and Policy of Machine Learning.

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Predicting herbage biomass on small‐scale farms by combining sward height with different aggregations of weather data

Scheurer,

Leukel,

Zimpel

et al. 2024

Agronomy Journal

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Accurate predictions of herbage biomass are important for efficient grazing management. Small‐scale farms face challenges using remote sensing technologies due to insufficient resources. This limitation hinders their ability to develop machine learning‐based prediction models. An alternative is to adopt less expensive measurement methods and readily available data such as weather data. This study aimed to examine how different temporal aggregations of weather data combined with compressed sward height (CSH) affect the prediction performance. We considered weather features based on different numbers of weather variables, statistical functions, weather events, and periods. Between 2019 and 2021, data were collected from 11 organic dairy farms in Germany. Herbage biomass exhibited high variability (coefficient of variation [CV] = 0.65). Weather data were obtained from on‐farm and nearby public stations. Prediction models were learned on a training set (n = 291) and evaluated on a test set (n = 125). Random forest models performed better than models based on artificial neural networks and support vector regression. Representing weather data by a single feature for leaf wetness reduced the root mean square error (RMSE) by 12.1% (from 536 to 471 kg DM ha−1, where DM is dry matter) and increased the R2 by 0.109 (from 0.518 to 0.627). Adding features based on multiple variables, functions, events, and periods resulted in a further reduction in RMSE by 15.9% (R2 = 0.737). Overall, different aggregations of weather data enhanced the accuracy of CSH‐based models. These aggregations do not cause additional effort for data collection and, therefore, should be integrated into CSH‐based models for small‐scale farms.

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A vision of precision agriculture: Balance between agricultural sustainability and environmental stewardship

Cited by 13 publications

References 110 publications

A Conceptual Model Relationship between Industry 4.0—Food-Agriculture Nexus and Agroecosystem: A Literature Review and Knowledge Gaps

A Conceptual Model Relationship between Industry 4.0—Food-Agriculture Nexus and Agroecosystem: A Literature Review and Knowledge Gaps

Will artificial intelligence and machine learning change agriculture: A special issue

Predicting herbage biomass on small‐scale farms by combining sward height with different aggregations of weather data

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