深度学习在农业中的应用：一项调查

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"这篇文档是'Computers and Electronics in Agriculture'期刊中的一篇综述文章，作者是Andreas Kamilaris和Francesc X. Prenafeta-Boldú，主要探讨了深度学习在农业领域的应用及其潜力。" 深度学习在农业中的应用正逐渐成为农业科技的一个重要方向。该综述文章深入探讨了这一现代技术如何改变传统农业实践，特别是在图像处理和数据分析方面的作用。随着深度学习在多个领域取得显著成果，它也逐渐渗透到农业这个看似传统但亟需技术创新的领域。文章详尽分析了40项研究案例，这些案例均采用了深度学习技术来解决各种农业和食品生产中的问题。作者们对所研究的具体农业问题、使用的特定模型和框架、数据来源、性质以及预处理方法进行了深入探讨，并基于每项工作所采用的评价指标评估了它们的整体性能。文章关注的深度学习模型主要包括卷积神经网络（CNNs）和循环神经网络（RNNs），这两种模型在处理图像识别、时间序列分析等农业相关的复杂任务时表现出色。例如，CNNs常用于作物病害检测、生长监测，而RNNs则适用于长期预测，如作物生长周期的预测。智能农业是深度学习技术的一大应用领域。通过收集和分析大量农田数据，包括土壤条件、气候参数、作物生长状态等，深度学习模型可以提供精准的决策支持，帮助农民优化种植策略，提高产量，减少资源浪费，并有助于实现可持续的粮食系统。此外，文章还讨论了深度学习在食品安全领域的应用，例如，通过检测和识别食品中的污染物或异常，确保食品安全。这些技术的进步对于保障全球食品供应链的稳定性和安全性具有重要意义。这篇综述为读者提供了深度学习在农业领域广泛应用的全面概述，揭示了其在提升农业生产效率、优化资源利用、保障食品安全等方面的重要作用。同时，也指出了未来可能的研究方向，包括数据获取的挑战、模型的可解释性以及如何将这些技术实际部署到农田中。

transposing (Sørensen et al., 2017), mirroring (Dyrmann et al., 2016a),

translations and perspective transform (Sladojevic et al., 2016), adap-

tations of objects’ intensity in an object detection problem (Steen et al.,

2016) and a PCA augmentation technique (Bargoti and Underwood,

2016).

Papers involving simulated data performed additional augmenta-

tion techniques such as varying the HSV channels and adding random

shadows (Dyrmann et al., 2016b) or adding simulated roots to soil

images (Douarre et al., 2016).

4.6. Technical details

From a technical side, almost half of the research works (17 papers,

42%) employed popular CNN architectures such as AlexNet, VGG16

and Inception-ResNet. From the rest, 14 papers developed their own

CNN models, 2 papers adopted a ﬁrst-order Diﬀerential Recurrent

Neural Networks (DRNN) model, 5 papers preferred to use a Long

Short-Term Memory (LSTM) model (Gers et al., 2000), one paper used

deep belief networks (DBN) and one paper employed a hybrid of PCA

with auto-encoders (AE). Some of the CNN approaches combined their

model with a classiﬁer at the output layer, such as logistic regression

(Chen et al., 2014), Scalable Vector Machines (SVM) (Douarre et al.,

2016), linear regression (Chen et al., 2017), Large Margin Classiﬁers

(LCM) (Xinshao and Cheng, 2015) and macroscopic cellular automata

(Song et al., 2016).

Regarding the frameworks used, all the works that employed some

well-known CNN architecture had also used a DL framework, with Caﬀe

being the most popular (13 papers, 32%), followed by Tensor Flow (2

papers) and deeplearning4j (1 paper). Ten research works developed

their own software, while some authors decided to build their own

models on top of Caﬀe (5 papers), Keras/Theano (5 papers), Keras/

TensorFlow (4 papers), Pylearn2 (1 paper), MatConvNet (1 paper) and

Deep Learning Matlab Toolbox (1 paper). A possible reason for the wide

use of Caﬀe is that it incorporates various CNN frameworks and data-

sets, which can be used then easily and automatically by its users.

Most of the studies divided their dataset between training and

testing/veriﬁcation data using a ratio of 80–20 or 90–10 respectively.

In addition, various learning rates have been recorded, from 0.001

(Amara et al., 2017) and 0.005 (Mohanty et al., 2016) up to 0.01

(Grinblat et al., 2016). Learning rate is about how quickly a network

learns. Higher values help avoid the solver being stuck in local minima,

which can reduce performance signiﬁcantly. A general approach used

by many of the evaluated papers is to start out with a high learning rate

and lower it as the training goes on. We note that learning rate is very

dependent on the network architecture.

Moreover, most of the research works that incorporated popular DL

architectures took advantage of transfer learning (

Pan and Yang, 2010),

which

concerns leveraging the already existing knowledge of some re-

lated task or domain in order to increase the learning eﬃciency of the

problem under study by ﬁne-tuning pre-trained models. As sometimes it

is not possible to train a network from scratch due to having a small

training data set or having a complex multi-task network, it is required

that the network be at least partially initialized with weights from

another pre-trained model. A common transfer learning technique is the

use of pre-trained CNN, which are CNN models that have been already

trained on some relevant dataset with possibly diﬀerent number of

classes. These models are then adapted to the particular challenge and

dataset. This method was followed (among others) in Lu et al. (2017),

Douarre et al. (2016), Reyes et al. (2015), Bargoti and Underwood

(2016), Steen et al. (2016), Lee et al. (2015), Sa et al. (2016), Mohanty

et al. (2016), Christiansen et al. (2016) and Sørensen et al. (2017), for

the VGG16, DenseNet, AlexNet and GoogleNet architectures.

4.7. Outputs

Finally, concerning the 31 papers that involved classiﬁcation, the

classes as used by the authors ranged from 2 (Lu et al., 2017; Pound

et al., 2016; Douarre et al., 2016; Milioto et al., 2017) up to 1000

(Reyes et al., 2015). A large number of classes was observed in Luus

et al. (2015) (21 land-use classes) (Rebetez et al., 2016) (22 diﬀerent

crops plus soil) (Lee et al., 2015) (44 plant species) and (Xinshao and

Cheng, 2015) (91 classes of common weeds found in agricultural

ﬁelds). In these papers, the number of outputs of the model was equal to

the number of classes respectively. Each output was a diﬀerent prob-

ability for the input image, segment, blob or pixel to belong to each

class, and then the model picked the highest probability as its predicted

class.

From the rest 9 papers, 2 performed predictions of fruits counted

(scalar value as output) (Rahnemoonfar and Sheppard, 2017; Chen

et al., 2017), 2 identiﬁed regions of fruits in the image (multiple

bounding boxes) (Bargoti and Underwood, 2016; Sa et al., 2016), 2

predicted animal growth (scalar value) (Demmers et al., 2010, 2012),

one predicted weather conditions (scalar value) (Sehgal et al., 2017),

one crop yield index (scalar value) (Kuwata and Shibasaki, 2015) and

one paper predicted percentage of soil moisture content (scalar value)

(Song et al., 2016).

4.8. Performance metrics

Regarding methods used to evaluate performance, various metrics

have been employed by the authors, each being speciﬁc to the model

used at each study. Table 1 lists these metrics, together with their de-

ﬁnition/description, and the symbol we use to refer to them in this

survey. In some papers where the authors referred to accuracy without

specifying its deﬁnition, we assumed they referred to classiﬁcation

accuracy (CA, ﬁrst metric listed in Table 1). From this point onwards,

we refer to “DL

performance” as its score in some performance metric

from the ones listed in Table 1.

CA was the most popular metric used (24 papers, 60%), followed by

F1 (10 papers, 25%). Some papers included RMSE (4 papers), IoU (3

papers), RFC (Chen et al., 2017; Rahnemoonfar and Sheppard, 2017)or

others. Some works used a combination of metrics to evaluate their

eﬀorts. We note that some papers employing CA, F1, P or R, used IoU in

order to consider a model’s prediction (Bargoti and Underwood, 2016;

Sa et al., 2016; Steen et al., 2016; Christiansen et al., 2016; Dyrmann

et al., 2017). In these cases, a minimum threshold was put on IoU, and

any value above this threshold would be considered as positive by the

model.

We note that in some cases, a trade-oﬀ can exist between metrics.

For example, in a weed detection problem (Milioto et al., 2017), it

might be desirable to have a high R to eliminate most weeds, but not

eliminating crops is of a critical importance, hence a lower P might be

acceptable.

4.9. Overall performance

We note that it is diﬃcult if not impossible to compare between

papers, as diﬀerent metrics are employed for diﬀerent tasks, con-

sidering diﬀerent models, datasets and parameters. Hence, the reader

should consider our comments in this section with some caution.

In 19 out of the 24 papers that involved CA as a metric, accuracy

was high (i.e. above 90%), indicating good performance. The highest

CA has been observed in the works of Hall et al. (2015), Pound et al.

(2016), Chen et al. (2014), Lee et al. (2015), Minh et al. (2017), Potena

et al. (2016) and Steen et al., 2016, with values of 98% or more, con-

stituting remarkable results. From the 10 papers using F1 as metric, 5

had values higher than 0.90 with the highest F1 observed in Mohanty

et al. (2016) and Minh et al. (2017) with values higher than 0.99.

The works of Dyrmann et al. (2016a), Rußwurm and Körner (2017),

Ienco et al. (2017), Mortensen et al. (2016), Rebetez et al. (2016),

Christiansen et al. (2016) and Yalcin (2017) were among the ones with

the lowest CA (i.e. 73–79%) and/or F1 scores (i.e. 0.558–0.746),

A. Kamilaris, F.X. Prenafeta-Boldú

Computers and Electronics in Agriculture 147 (2018) 70–90

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